From 494ab298065c54a93e35d1381e0ea6f40908f921 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 17:15:51 +0000 Subject: [PATCH 01/23] =?UTF-8?q?probe(weather):=20voxel-chess=20=E2=80=94?= =?UTF-8?q?=20rotation=20physics=20as=20bitboard=20popcounts,=20raw=20vs?= =?UTF-8?q?=20u8=20palette?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit EXPLORATORY (operator frame: "am Ende ist es volumetrisches Voxel-Schach"). Pre-registered E1-E6 in the docstring BEFORE the run; NOT an EV — promotion requires the adversarial audit gate per plan section 8, and this session measured why (0/11). The chess mapping made operational: threshold masks over the voxel grid ARE bitboards (Tiefdruck = p' < -sigma, Zyklonal = zeta > 0), and the rotation verdict is popcount(zeta_pos & lows & hemisphere) — Stockfish's popcount(attacks & targets) with weather masks. Measured (WB2 ERA5 6h, t=91246, 2021-06-15 12Z): - E1/E2 PASS with the built-in inversion control: NH lows ccw-frac 0.636, SH lows 0.402 — the SAME statistic inverts across the equator. Highs mirror, stronger: NH 0.151 / SH 0.719. - E4 PASS, the substrate headline: u8-palette arm reproduces every popcount verdict to max deviation 0.0047. Gradients of bucket-center fields keep the vorticity sign structure — the rotation physics SURVIVES the palette. - E5 PARTIAL FAIL, as pre-registered bars demand honesty: geostrophic corr NH 0.389/0.433 (below the 0.5 bar), SH 0.862/0.736 (pass). The palette reproduces the failure too (0.384/0.422) — the substrate is not the problem; the surface-wind==geostrophic approximation is weakest in the NH sample. Candidate mechanisms (summer gradients, land friction, ageostrophy) are [S] hypotheses, explicitly NOT findings — this session's own lesson: "finding to investigate" is not "finding". - E6 PASS, the Gluecksrad: deepest NH low (55.75N, p'=-2954 Pa), tangential wind +10.2 m/s at 75 km rising to +12.4 at 525 km (torque / solid-body zone), decaying to +1.2 at 1425 km (momentum zone) — the Rankine-vortex shape, measured, cyclonic at every ring. Formulas were NOT invented: geostrophic balance, vorticity, Rankine vortex — existing dynamic meteorology, evaluated on the substrate. cos(lat) zonal spacing per the EV-1 audit lesson; |lat|<15 masked (f->0). Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/voxel_chess_probe.json | 93 ++++++++++++ probes/weather-p1/voxel_chess_probe.py | 184 +++++++++++++++++++++++ 2 files changed, 277 insertions(+) create mode 100644 probes/weather-p1/voxel_chess_probe.json create mode 100644 probes/weather-p1/voxel_chess_probe.py diff --git a/probes/weather-p1/voxel_chess_probe.json b/probes/weather-p1/voxel_chess_probe.json new file mode 100644 index 00000000..fe0f3d13 --- /dev/null +++ b/probes/weather-p1/voxel_chess_probe.json @@ -0,0 +1,93 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "time_index": 91246, + "preregistered": "E1>0.5, E2<0.5 (inversion = control), E3 mirrored, E4 |raw-u8|<=0.05, E5 corr>0.5 all bands, E6 rise-peak-decay", + "raw": { + "E1_nh_lows_ccw": 0.6356173111797284, + "n_nh_lows": 40806, + "E2_sh_lows_ccw": 0.4021482952191861, + "n_sh_lows": 70009, + "E3_nh_highs_ccw": 0.1514411607869552, + "n_nh_highs": 35631, + "E3_sh_highs_ccw": 0.7192818869510401, + "n_sh_highs": 76144, + "nh_corr_u": 0.3886449660159403, + "nh_corr_v": 0.4334605594972074, + "sh_corr_u": 0.8623853874270709, + "sh_corr_v": 0.7355679117824572 + }, + "palette_u8": { + "E1_nh_lows_ccw": 0.6338667820069204, + "n_nh_lows": 41616, + "E2_sh_lows_ccw": 0.39936678308771334, + "n_sh_lows": 70434, + "E3_nh_highs_ccw": 0.15619026814483097, + "n_nh_highs": 36622, + "E3_sh_highs_ccw": 0.7194711726511773, + "n_sh_highs": 76698, + "nh_corr_u": 0.38443625370572837, + "nh_corr_v": 0.42178482993212973, + "sh_corr_u": 0.8466889689137697, + "sh_corr_v": 0.710729073456687 + }, + "E4_max_popfrac_dev": 0.004749107357875787, + "E6_rankine": { + "center_lat": 55.75, + "center_lon": 334.5, + "profile": [ + { + "r_mid_km": 75, + "vt_mean": 10.234071543181926, + "n": 161 + }, + { + "r_mid_km": 225, + "vt_mean": 12.190355700609066, + "n": 482 + }, + { + "r_mid_km": 375, + "vt_mean": 12.163085321612744, + "n": 822 + }, + { + "r_mid_km": 525, + "vt_mean": 12.40477837840327, + "n": 1130 + }, + { + "r_mid_km": 675, + "vt_mean": 10.932072086933266, + "n": 1456 + }, + { + "r_mid_km": 825, + "vt_mean": 10.033053294346114, + "n": 1800 + }, + { + "r_mid_km": 975, + "vt_mean": 9.2468191601141, + "n": 2114 + }, + { + "r_mid_km": 1125, + "vt_mean": 6.655831785528652, + "n": 2430 + }, + { + "r_mid_km": 1275, + "vt_mean": 3.864982062335603, + "n": 2766 + }, + { + "r_mid_km": 1425, + "vt_mean": 1.206079088789252, + "n": 3076 + } + ], + "peak_ring": 3, + "cyclonic_at_peak": true, + "rises_then_decays": true + } +} \ No newline at end of file diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py new file mode 100644 index 00000000..60b4b564 --- /dev/null +++ b/probes/weather-p1/voxel_chess_probe.py @@ -0,0 +1,184 @@ +"""EXPLORATORY — 'volumetrisches Voxel-Schach': rotation physics as BITBOARD ops, +raw vs u8-palette substrate. (Operator frame, 2026-08-11.) + +The claim under test, stated as chess machinery: threshold masks over the voxel +grid are BITBOARDS, and the rotation physics of pressure systems is decidable by +POPCOUNTS over mask intersections — Stockfish's popcount(attacks & targets), +with weather masks. If the u8 palette substrate carries the physics, the same +popcounts on quantized fields give the same verdicts. + +PRE-REGISTERED, before the run (NOT independently audited — measurements, not +an EV; promotion to an EV requires the adversarial audit gate per plan section 8): + E1 NH lows rotate counterclockwise: pop(L & Zpos & NH)/pop(L & NH) > 0.5 + E2 SH lows rotate clockwise: pop(L & Zpos & SH)/pop(L & SH) < 0.5 + -- E1 vs E2 is the built-in two-sided control: the SAME statistic must + INVERT across the equator. A wiring bug that ignores hemisphere + cannot produce the inversion. + E3 Highs mirror lows in both hemispheres (anticyclonic). + E4 The u8-palette arm reproduces every E1-E3 verdict, fractions within + 0.05 of raw. + E5 Geostrophic move-generation: corr(u_g, u) and corr(v_g, v) > 0.5 in + each hemisphere band (|lat| in 20..70), raw AND palette. + E6 Rankine ('Gluecksrad'): around the deepest NH low, azimuthal-mean + tangential wind is positive (cyclonic), rises from the core (torque / + solid-body zone), peaks, then decays outward (momentum zone). + +Data: WeatherBench2 ERA5 6-hourly 0.25 deg, t=91246 (2021-06-15 12Z). +Fields: mean_sea_level_pressure, 10m u, 10m v. cos(lat) zonal spacing per the +EV-1 audit lesson; |lat| < 15 deg masked (f -> 0, geostrophy undefined). +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T_IDX = 91246 +OMEGA, R_EARTH, RHO0 = 7.2921e-5, 6.371e6, 1.225 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=180).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +lat = fetch("latitude", "0").astype(np.float64).ravel() +p = fetch("mean_sea_level_pressure", f"{T_IDX}.0.0")[0].astype(np.float64) +u = fetch("10m_u_component_of_wind", f"{T_IDX}.0.0")[0].astype(np.float64) +v = fetch("10m_v_component_of_wind", f"{T_IDX}.0.0")[0].astype(np.float64) +print(f"fetched: lat[{lat[0]:.1f}..{lat[-1]:.1f}], p/u/v {p.shape}") + +phi = np.deg2rad(lat) +y = R_EARTH * phi # meridional coordinate (m) +dlam = 2 * np.pi / p.shape[1] +dx = R_EARTH * np.cos(phi) * dlam # zonal spacing per row (m) +f_cor = 2 * OMEGA * np.sin(phi) + + +def ddx(a): + """Periodic central zonal derivative with per-row cos(lat) spacing.""" + return (np.roll(a, -1, 1) - np.roll(a, 1, 1)) / (2 * dx[:, None]) + + +def ddy(a): + return np.gradient(a, y, axis=0) + + +def physics(pf, uf, vf): + zeta = ddx(vf) - ddy(uf) + p_anom = pf - pf.mean(axis=1, keepdims=True) + ug = -(1 / (RHO0 * f_cor[:, None])) * ddy(pf) + vg = (1 / (RHO0 * f_cor[:, None])) * ddx(pf) + return zeta, p_anom, ug, vg + + +def quant_u8(a, q=(0.4, 99.6)): + """The shipped palette scheme: linear 256 buckets over the percentile + window (mirrors helix quantize.rs), decoded at bucket centers.""" + lo, hi = np.percentile(a, q) + idx = np.clip(np.floor((a - lo) / (hi - lo) * 256), 0, 255) + return lo + (idx + 0.5) / 256 * (hi - lo) + + +def popfrac(mask_num, mask_den): + d = int(mask_den.sum()) + return (int((mask_num & mask_den).sum()) / d if d else float("nan")), d + + +def board_eval(zeta, p_anom, tag): + """The chess move: popcounts over bitboard intersections.""" + band = np.abs(lat)[:, None] >= 15.0 + nh = (lat[:, None] > 0) & band & np.ones_like(p_anom, bool) + sh = (lat[:, None] < 0) & band & np.ones_like(p_anom, bool) + sig = p_anom[band[:, 0], :].std() + lows, highs, zpos = p_anom < -sig, p_anom > sig, zeta > 0 + r = {} + r["E1_nh_lows_ccw"], r["n_nh_lows"] = popfrac(zpos, lows & nh) + r["E2_sh_lows_ccw"], r["n_sh_lows"] = popfrac(zpos, lows & sh) + r["E3_nh_highs_ccw"], r["n_nh_highs"] = popfrac(zpos, highs & nh) + r["E3_sh_highs_ccw"], r["n_sh_highs"] = popfrac(zpos, highs & sh) + print(f" [{tag}] lows: NH ccw-frac={r['E1_nh_lows_ccw']:.3f} " + f"(n={r['n_nh_lows']}) SH ccw-frac={r['E2_sh_lows_ccw']:.3f} " + f"(n={r['n_sh_lows']})") + print(f" [{tag}] highs: NH ccw-frac={r['E3_nh_highs_ccw']:.3f} " + f"SH ccw-frac={r['E3_sh_highs_ccw']:.3f}") + return r + + +def geo_corr(ug, vg, tag): + r = {} + for name, lo_b, hi_b in [("nh", 20, 70), ("sh", -70, -20)]: + m = (lat[:, None] >= lo_b) & (lat[:, None] <= hi_b) \ + & np.ones_like(u, bool) + cu = np.corrcoef(ug[m], u[m])[0, 1] + cv = np.corrcoef(vg[m], v[m])[0, 1] + r[f"{name}_corr_u"], r[f"{name}_corr_v"] = float(cu), float(cv) + print(f" [{tag}] geostrophic {name.upper()}: corr(u_g,u)={cu:.3f} " + f"corr(v_g,v)={cv:.3f}") + return r + + +out = {"store": B, "time_index": T_IDX, "preregistered": + "E1>0.5, E2<0.5 (inversion = control), E3 mirrored, E4 |raw-u8|<=0.05, " + "E5 corr>0.5 all bands, E6 rise-peak-decay"} + +print("\n== RAW arm ==") +zeta, p_anom, ug, vg = physics(p, u, v) +out["raw"] = {**board_eval(zeta, p_anom, "raw"), **geo_corr(ug, vg, "raw")} + +print("\n== u8-PALETTE arm (p, u, v each quantized to 256 buckets) ==") +p8, u8, v8 = quant_u8(p), quant_u8(u), quant_u8(v) +zeta8, p_anom8, ug8, vg8 = physics(p8, u8, v8) +out["palette_u8"] = {**board_eval(zeta8, p_anom8, "u8"), + **geo_corr(ug8, vg8, "u8")} + +# E4: substrate fidelity of the popcount verdicts +keys = ["E1_nh_lows_ccw", "E2_sh_lows_ccw", "E3_nh_highs_ccw", + "E3_sh_highs_ccw"] +devs = {k: abs(out["raw"][k] - out["palette_u8"][k]) for k in keys} +out["E4_max_popfrac_dev"] = max(devs.values()) +print(f"\n E4 max |raw - u8| popcount-fraction deviation: " + f"{out['E4_max_popfrac_dev']:.4f}") + +# E6: Rankine profile around the deepest NH low +print("\n== E6 Rankine ('Gluecksrad') around the deepest NH low ==") +nh_rows = lat > 15 +pa_nh = np.where(nh_rows[:, None], p_anom, np.inf) +ci, cj = np.unravel_index(np.argmin(pa_nh), pa_nh.shape) +print(f" center: lat={lat[ci]:.2f} lon={cj * 0.25:.2f} " + f"p'={p_anom[ci, cj]:.0f} Pa") +lon = np.arange(p.shape[1]) * 0.25 +dlon = np.deg2rad((lon[None, :] - lon[cj] + 180) % 360 - 180) +dphi = phi[:, None] - phi[ci] +dx_m = R_EARTH * np.cos(phi[ci]) * dlon * np.ones_like(p) +dy_m = R_EARTH * dphi * np.ones_like(p) +r_km = np.hypot(dx_m, dy_m) / 1e3 +alpha = np.arctan2(dy_m, dx_m) +v_t = -u * np.sin(alpha) + v * np.cos(alpha) # >0 = counterclockwise +prof = [] +for r0 in range(0, 1500, 150): + ring = (r_km >= r0) & (r_km < r0 + 150) + prof.append({"r_mid_km": r0 + 75, "vt_mean": float(v_t[ring].mean()), + "n": int(ring.sum())}) + print(f" r={r0 + 75:>5} km v_t={prof[-1]['vt_mean']:+7.2f} m/s " + f"(n={prof[-1]['n']})") +vts = [q["vt_mean"] for q in prof] +pk = int(np.argmax(vts)) +out["E6_rankine"] = {"center_lat": float(lat[ci]), "center_lon": float(lon[cj]), + "profile": prof, "peak_ring": pk, + "cyclonic_at_peak": vts[pk] > 0, + "rises_then_decays": 0 < pk < len(vts) - 1 + and vts[-1] < vts[pk]} +print(f" peak at ring {pk} ({prof[pk]['r_mid_km']} km): torque zone inside, " + f"momentum zone outside -> rises_then_decays=" + f"{out['E6_rankine']['rises_then_decays']}") + +json.dump(out, open("voxel_chess_probe.json", "w"), indent=2) +print("\nwrote voxel_chess_probe.json") From 05a72501693b32d9d432ee289809ceb88d37ea6f Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 19:53:22 +0000 Subject: [PATCH 02/23] =?UTF-8?q?probe(weather):=20sunflower-cyclone=20?= =?UTF-8?q?=E2=80=94=20the=20golden-spiral=20encoding,=20measured:=202=20P?= =?UTF-8?q?ASS,=202=20FAIL,=201=20parity?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Operator frame: "die goldene Spirale encodiert das ganze Tiefdruckgebiet akin to sunflower ripples". EXPLORATORY, pre-registered E1-E5 before the run, bars mine and unaudited — and two of them FAILED, reported as prominently as the passes per this session's discipline. Same storm, same timestep as voxel_chess_probe E6 and ndarray's geostrophic_stencil (WB2 msl, t=91246, center 55.75N/334.5E). E1 FAIL (marginal): ring-mean radial profile explains 0.639 of in-disk pressure variance vs the 0.70 bar. A real midlatitude cyclone carries fronts — it is ~64% axisymmetric, not a hurricane. E4 PASS (the control that makes E1 meaningful): recentering 750 km off-storm collapses the index 0.639 -> 0.005. The metric genuinely measures centeredness — sharp enough that axisymmetry-maximization would work as a storm-center DETECTOR (by-product, [S]). E2 FAIL as written: spiral does NOT beat uniform grid at equal budget (N~256: 119.1 vs 111.6 Pa; N~1024: 58.9 vs 57.8). Apparatus caveat stated, not used as a rescue: the grid arm got 8-25% more points from the clip-to-disk construction; correcting roughly yields parity, still not the pre-registered spiral win. Both beat random decisively (164.2 / 84.2). E3 FAIL, INVERTED — the informative one: delta-entropy along the spiral walk is 7.13 bits/sample vs 5.95 raster. The "ripples" intuition assumes consecutive spiral samples differ only by a tiny radial step; but the golden angle jumps ~137.5 deg in azimuth each step, so at 64% axisymmetry the serial deltas are dominated by the azimuthal residual the spiral ignores. Low-discrepancy = uniform COVERAGE, not serial smoothness. The ripple claim would need the axisymmetric SHARE of the field, not the field. E5 PASS: u8 palette adds 0.1 Pa on top of 119.1 Pa sampling error (bucket 17.2 Pa) — quantization is free here; sampling dominates by 7x. What survives the failures: (a) the radial profile compresses 64% of the storm into 24 ring means; (b) the natural successor encoding is profile + low-order azimuthal harmonics per ring (fronts are wavenumber-1/2), NOT raw spiral samples — that is a next probe, not a rescue of this one; (c) highheelbgz's (start, stride, len) spiral ADDRESS would apply to the profile stage, and only if (b) measures well; (d) tropical cyclones are far more axisymmetric than this midlatitude low — E1 might pass there ([S]). Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .../weather-p1/sunflower_cyclone_probe.json | 65 ++++++ probes/weather-p1/sunflower_cyclone_probe.py | 204 ++++++++++++++++++ 2 files changed, 269 insertions(+) create mode 100644 probes/weather-p1/sunflower_cyclone_probe.json create mode 100644 probes/weather-p1/sunflower_cyclone_probe.py diff --git a/probes/weather-p1/sunflower_cyclone_probe.json b/probes/weather-p1/sunflower_cyclone_probe.json new file mode 100644 index 00000000..48662b3a --- /dev/null +++ b/probes/weather-p1/sunflower_cyclone_probe.json @@ -0,0 +1,65 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "time_index": 91246, + "R_disk_km": 1200.0, + "center": { + "lat": 55.75, + "lon": 334.5, + "p_anom_Pa": -2954.4862955729186 + }, + "E1_axisym_storm": 0.6392718246695573, + "E4_axisym_offset750km": 0.004993305440683216, + "E2_budgets": { + "64": { + "spiral": { + "n": 64, + "rmse_Pa": 234.48255636818175 + }, + "grid": { + "n": 80, + "rmse_Pa": 215.37203997558382 + }, + "random": { + "n": 64, + "rmse_Pa": 319.37183755793615 + } + }, + "256": { + "spiral": { + "n": 256, + "rmse_Pa": 119.10369656260444 + }, + "grid": { + "n": 293, + "rmse_Pa": 111.55372624817461 + }, + "random": { + "n": 256, + "rmse_Pa": 164.15349088979696 + } + }, + "1024": { + "spiral": { + "n": 1024, + "rmse_Pa": 58.854942160393385 + }, + "grid": { + "n": 1085, + "rmse_Pa": 57.7735059109564 + }, + "random": { + "n": 1024, + "rmse_Pa": 84.22335048725353 + } + } + }, + "E3_delta_entropy_bits": { + "spiral_order": 7.127796366175735, + "raster_order": 5.946501707930685 + }, + "E5_u8": { + "rmse_Pa": 119.175017454153, + "bucket_Pa": 17.188925402801544, + "added_vs_f64_Pa": 0.07132089154855237 + } +} \ No newline at end of file diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py new file mode 100644 index 00000000..bb5a62a7 --- /dev/null +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -0,0 +1,204 @@ +"""EXPLORATORY — 'die goldene Spirale encodiert das ganze Tiefdruckgebiet' +(operator, 2026-08-11, sunflower-ripples frame). NOT an EV; bars are mine, +unaudited — measurements, not findings, per this session's 0/11 lesson. + +The claim, made falsifiable: a cyclone is near-axisymmetric about its center, +so sampling it along the EQUAL-AREA golden spiral (r = sqrt(u)*R, theta = +n*golden_angle — literally helix::HemispherePoint::lift's lattice) turns the +2-D pressure structure into a low-entropy 1-D 'ripple' signal: the radial +profile carries the mass, consecutive spiral samples are near in radius, so +deltas are small. If true, bgz17-palette + highheelbgz spiral-ADDRESSING +(start, stride, len — values recomputed on demand) has something real to +encode. If false, the spiral is decoration. + +PRE-REGISTERED: + E1 Axisymmetry: the ring-mean radial profile explains >= 70% of pressure + variance in the storm disk (R=1200 km). + E2 Equal budget N=256: golden-spiral sampling reconstructs the disk with + RMSE <= uniform-grid sampling AND <= random sampling (nearest-neighbor + reconstruction, same N, same reconstruction rule for every arm). + E3 Ripples: Shannon entropy of u8 first-differences ALONG THE SPIRAL is + lower than along a raster scan of the SAME samples — the spiral + ordering is what makes the signal low-entropy, not the quantization. + E4 CONTROL (can-the-metric-fail): recentering the disk 750 km off-storm + drops the axisymmetry index by >= 0.15 absolute. If it does not, the + index does not measure centeredness and E1 is decoration. + E5 u8 palette quantization of the spiral samples adds reconstruction + error of at most one bucket width on top of E2's sampling error. + +Data: WB2 ERA5 6h msl, t=91246 (2021-06-15 12Z) — the SAME timestep and the +SAME storm (55.75N, 334.5E) as voxel_chess_probe.py E6 and ndarray's +examples/geostrophic_stencil.rs. cos(lat) spacing throughout. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T_IDX = 91246 +R_EARTH_KM = 6371.0 +R_DISK_KM = 1200.0 +GOLDEN_ANGLE = np.pi * (3.0 - np.sqrt(5.0)) + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=180).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +lat = fetch("latitude", "0").astype(np.float64).ravel() +p = fetch("mean_sea_level_pressure", f"{T_IDX}.0.0")[0].astype(np.float64) +phi = np.deg2rad(lat) + +# Storm center: deepest NH low in zonal-anomaly space (same rule as E6). +p_anom = p - p.mean(axis=1, keepdims=True) +nh = lat > 15 +ci, cj = np.unravel_index( + np.argmin(np.where(nh[:, None], p_anom, np.inf)), p.shape) +print(f"storm center: lat={lat[ci]:.2f} lon={cj * 0.25:.2f} " + f"p'={p_anom[ci, cj]:.0f} Pa") + + +def km_grid(ci_, cj_): + """(dx_km, dy_km, r_km) of every gridpoint relative to center (ci_, cj_).""" + lon = np.arange(p.shape[1]) * 0.25 + dlon = np.deg2rad((lon[None, :] - lon[cj_] + 180) % 360 - 180) + dphi = phi[:, None] - phi[ci_] + dx = R_EARTH_KM * np.cos(phi[ci_]) * dlon * np.ones_like(p) + dy = R_EARTH_KM * dphi * np.ones_like(p) + return dx, dy, np.hypot(dx, dy) + + +def bilinear(field, ci_, cj_, dx_km, dy_km): + """Sample field at km-offsets from center (ci_, cj_), bilinear, lon-wrapped.""" + dlat = np.rad2deg(dy_km / R_EARTH_KM) + dlon = np.rad2deg(dx_km / (R_EARTH_KM * np.cos(phi[ci_]))) + ri = ci_ - dlat / 0.25 # lat rows DESCEND 90..-90 + rj = (cj_ + dlon / 0.25) % p.shape[1] + i0 = np.clip(np.floor(ri).astype(int), 0, p.shape[0] - 2) + j0 = np.floor(rj).astype(int) % p.shape[1] + fi, fj = ri - i0, rj - j0 + j1 = (j0 + 1) % p.shape[1] + return (field[i0, j0] * (1 - fi) * (1 - fj) + + field[i0 + 1, j0] * fi * (1 - fj) + + field[i0, j1] * (1 - fi) * fj + + field[i0 + 1, j1] * fi * fj) + + +def axisym_index(ci_, cj_): + """1 - var(residual after removing the ring-mean profile)/var, in-disk.""" + _, _, r = km_grid(ci_, cj_) + disk = r <= R_DISK_KM + vals, rr = p[disk], r[disk] + rings = np.clip((rr / 50.0).astype(int), 0, 23) + prof = np.array([vals[rings == b].mean() if (rings == b).any() else np.nan + for b in range(24)]) + resid = vals - prof[rings] + return 1.0 - resid.var() / vals.var(), disk + + +def spiral_pts(n): + k = np.arange(n) + r = np.sqrt((k + 0.5) / n) * R_DISK_KM + th = k * GOLDEN_ANGLE + return r * np.cos(th), r * np.sin(th) + + +def grid_pts(n): + """~n points on a uniform grid clipped to the disk.""" + side = int(np.ceil(np.sqrt(n * 4 / np.pi))) + g = (np.arange(side) + 0.5) / side * 2 * R_DISK_KM - R_DISK_KM + gx, gy = np.meshgrid(g, g) + m = np.hypot(gx, gy) <= R_DISK_KM + return gx[m], gy[m] + + +def rand_pts(n, seed=7): + rng = np.random.default_rng(seed) + r = np.sqrt(rng.uniform(0, 1, n)) * R_DISK_KM + th = rng.uniform(0, 2 * np.pi, n) + return r * np.cos(th), r * np.sin(th) + + +def recon_rmse(sx, sy, svals, dxg, dyg, disk): + """Nearest-sample reconstruction of the in-disk field (one rule, all arms).""" + tx, ty = dxg[disk], dyg[disk] + truth = p[disk] + # brute-force nearest over <=1024 samples x ~7e3 targets — fine + d2 = (tx[:, None] - sx[None, :]) ** 2 + (ty[:, None] - sy[None, :]) ** 2 + rec = svals[np.argmin(d2, axis=1)] + return float(np.sqrt(((rec - truth) ** 2).mean())) + + +def delta_entropy(q_u8): + d = np.diff(q_u8.astype(np.int16)) + _, counts = np.unique(d, return_counts=True) + pr = counts / counts.sum() + return float(-(pr * np.log2(pr)).sum()) + + +out = {"store": B, "time_index": T_IDX, "R_disk_km": R_DISK_KM, + "center": {"lat": float(lat[ci]), "lon": float(cj * 0.25), + "p_anom_Pa": float(p_anom[ci, cj])}} + +# E1 + E4 -------------------------------------------------------------- +ax_storm, disk = axisym_index(ci, cj) +off_rows = int(round((750.0 / R_EARTH_KM) * 180 / np.pi / 0.25)) +ax_off, _ = axisym_index(ci + off_rows, cj) # 750 km south of the storm +out["E1_axisym_storm"] = ax_storm +out["E4_axisym_offset750km"] = ax_off +print(f"\nE1 axisymmetry (storm-centered): {ax_storm:.3f} (bar >= 0.70)") +print(f"E4 axisymmetry (750 km off): {ax_off:.3f} " + f"(bar: drop >= 0.15 -> {'FIRES' if ax_storm - ax_off >= 0.15 else 'fails'})") + +# E2 ------------------------------------------------------------------- +dxg, dyg, _ = km_grid(ci, cj) +out["E2_budgets"] = {} +for n in (64, 256, 1024): + row = {} + for name, (sx, sy) in [("spiral", spiral_pts(n)), ("grid", grid_pts(n)), + ("random", rand_pts(n))]: + vals = bilinear(p, ci, cj, sx, sy) + row[name] = {"n": len(sx), + "rmse_Pa": recon_rmse(sx, sy, vals, dxg, dyg, disk)} + out["E2_budgets"][str(n)] = row + print(f"E2 N~{n:4}: spiral={row['spiral']['rmse_Pa']:7.1f} Pa " + f"(n={row['spiral']['n']}) grid={row['grid']['rmse_Pa']:7.1f} " + f"(n={row['grid']['n']}) random={row['random']['rmse_Pa']:7.1f}") + +# E3 + E5 -------------------------------------------------------------- +n = 256 +sx, sy = spiral_pts(n) +vals = bilinear(p, ci, cj, sx, sy) +lo, hi = vals.min(), vals.max() +q = np.clip(np.floor((vals - lo) / (hi - lo) * 256), 0, 255).astype(np.uint8) +h_spiral = delta_entropy(q) +# raster order of the SAME samples: sort by (y-band, x) +order = np.lexsort((sx, np.floor(sy / 150.0))) +h_raster = delta_entropy(q[order]) +h_raw = delta_entropy(np.concatenate([[q[0]], q])) # abs values, reference +out["E3_delta_entropy_bits"] = {"spiral_order": h_spiral, + "raster_order": h_raster} +print(f"\nE3 delta-entropy (u8, bits/sample): spiral-order={h_spiral:.2f} " + f"raster-order={h_raster:.2f} (bar: spiral < raster)") + +deq = lo + (q.astype(np.float64) + 0.5) / 256 * (hi - lo) +rmse_q = recon_rmse(sx, sy, deq, dxg, dyg, disk) +bucket = (hi - lo) / 256 +out["E5_u8"] = {"rmse_Pa": rmse_q, "bucket_Pa": float(bucket), + "added_vs_f64_Pa": rmse_q - out["E2_budgets"]["256"]["spiral"]["rmse_Pa"]} +print(f"E5 u8-palette spiral recon: {rmse_q:.1f} Pa " + f"(f64 sampling: {out['E2_budgets']['256']['spiral']['rmse_Pa']:.1f}; " + f"bucket={bucket:.1f} Pa; added={out['E5_u8']['added_vs_f64_Pa']:.1f}, " + f"bar <= {bucket:.1f})") + +json.dump(out, open("sunflower_cyclone_probe.json", "w"), indent=2) +print("\nwrote sunflower_cyclone_probe.json") From 2449822c2364897b6947757ba1b1ae372374faec Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 19:59:37 +0000 Subject: [PATCH 03/23] =?UTF-8?q?probe(weather):=20go-territory=20?= =?UTF-8?q?=E2=80=94=20measured=20and=20FAILED=20on=203=20of=204=20bars,?= =?UTF-8?q?=20committed=20as-is?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Operator frame: overlapping golden spirals, Flaechen-vs-Nahkampf like Go. Pre-registered, run to completion before the operator's interrupt; committed with the failures front and center rather than left uncommitted (the K-12/13 orphan lesson). A-E1 FAIL (marginal): K=10 greedy multi-center radial atoms explain 0.530 of NH-band p_anom variance vs the 0.55 bar. Close, not met. A-E2 FAIL (the informative one): RANDOM-center atoms explain 0.417 — far above the <=0.265 bar. Ring-mean radial atoms are so expressive that even arbitrary centers soak up variance; the control unmasked that A-E1 mostly measures atom POWER, not center CORRECTNESS. The matched-vs-random GAP (0.53 vs 0.42) is real but modest. A-E3 (curiosity, no bar): marginal-gain ratios mean 1.42, nothing like 1/phi=0.618 — no golden-ratio decay; gains are lumpy, not geometric. B ALL FAIL, INVERTED: contested cells carry LOWER |gradT| than secured (0.80x at thresh 0.8, monotone 0.77/0.80/0.83; secured 3.55 > band 2.83; random-center control 1.24 sits at its bar edge). The meteorology the design assumed is wrong: FRONTS ARE NOT AT TERRITORY BOUNDARIES — warm/ cold fronts spiral INTO the low, so the highest gradients live in the low's OWN near-center territory ("secured" under r^-2 influence). Go's fight-at-the-border does not transfer; the fight is inside the stone's territory. 19 of 20 detected centers were lows at this timestep, which also starved the H-vs-T boundary the design imagined. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/go_territory_probe.json | 104 +++++++++++ probes/weather-p1/go_territory_probe.py | 206 ++++++++++++++++++++++ 2 files changed, 310 insertions(+) create mode 100644 probes/weather-p1/go_territory_probe.json create mode 100644 probes/weather-p1/go_territory_probe.py diff --git a/probes/weather-p1/go_territory_probe.json b/probes/weather-p1/go_territory_probe.json new file mode 100644 index 00000000..0ac58b86 --- /dev/null +++ b/probes/weather-p1/go_territory_probe.json @@ -0,0 +1,104 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "time_index": 91246, + "band_lat": [ + 25, + 75 + ], + "A": { + "explained_by_k": [ + 0.12049389150478484, + 0.21273185676323636, + 0.2676820303186759, + 0.3001417574768168, + 0.32981359089194373, + 0.38994206995613034, + 0.3957312192737913, + 0.4214904781858111, + 0.4586807878253665, + 0.529569066040689 + ], + "centers": [ + { + "lat": 55.75, + "lon": 334.5 + }, + { + "lat": 28.5, + "lon": 67.5 + }, + { + "lat": 67.0, + "lon": 28.0 + }, + { + "lat": 31.75, + "lon": 89.75 + }, + { + "lat": 74.25, + "lon": 318.25 + }, + { + "lat": 43.75, + "lon": 108.75 + }, + { + "lat": 36.0, + "lon": 70.5 + }, + { + "lat": 32.25, + "lon": 47.25 + }, + { + "lat": 54.5, + "lon": 128.75 + }, + { + "lat": 33.0, + "lon": 320.5 + } + ], + "E1": 0.529569066040689, + "E2_random": 0.41734777491677777, + "E3_decay_ratios": [ + 0.765499097975342, + 0.5957435574545545, + 0.5907120043104531, + 0.9141122249909357, + 2.0264497384759745, + 0.09627965662462677, + 4.449575835509386, + 1.4437647358791685, + 1.9060954023336814 + ], + "E3_mean": 1.420914694839347 + }, + "B": { + "n_centers": 20, + "by_thresh": { + "0.7": { + "contested_frac": 0.3439227473742399, + "gradT_contested": 2.7313515511506186, + "gradT_secured": 3.546598376186566, + "ratio": 0.7701327473361868 + }, + "0.8": { + "contested_frac": 0.22770176893311223, + "gradT_contested": 2.8360323583495233, + "gradT_secured": 3.546598376186566, + "ratio": 0.7996485808463406 + }, + "0.9": { + "contested_frac": 0.11122857932559425, + "gradT_contested": 2.938374656664403, + "gradT_secured": 3.546598376186566, + "ratio": 0.8285050476518439 + } + }, + "E2_secured_le_bandmean": false, + "E3_random_ratio": 1.2384930773081844, + "gradT_band_mean_K_per_100km": 2.8310022821582104 + } +} \ No newline at end of file diff --git a/probes/weather-p1/go_territory_probe.py b/probes/weather-p1/go_territory_probe.py new file mode 100644 index 00000000..ef1b0812 --- /dev/null +++ b/probes/weather-p1/go_territory_probe.py @@ -0,0 +1,206 @@ +"""EXPLORATORY — 'overlapping golden spirals': eine Spirale pro Hoch/Tief, +Flaechen-vs-Nahkampf-Logik wie Go. (Operator, 2026-08-11.) NOT an EV; bars +mine, unaudited — measurements, not findings. + +Two falsifiable halves: + +A (FLAECHEN / territory): the pressure field as a SUPERPOSITION of radial + (sunflower-samplable) profiles around MANY centers — matching pursuit with + radial atoms. Directly tests the successor hypothesis from + sunflower_cyclone_probe's E3 fail: the single-storm 'azimuthal residual' + (36%) is largely the overlap of NEIGHBORING systems. +B (NAHKAMPF / contested zones): Go-style influence tessellation + (influence_i = |A_i| / r_i^2 — scale-free, deliberately NO length knob). + Contested cells (second/best influence > threshold) should carry the + FRONTS: elevated |grad T2m|. Secured territory should be calm. + +PRE-REGISTERED: + A-E1 greedy K=10 multi-center radial superposition explains >= 0.55 of + p_anom variance over the NH 25..75N band (fresh domain, fresh bar — + NOT comparable to the single-disk 0.639). + A-E2 control: K=10 RANDOM-center atoms explain <= HALF of A-E1's value. + A-E3 observation only, NO bar: marginal-gain decay ratio reported and + compared to 1/phi = 0.618 as a labelled CURIOSITY. + B-E1 can-fire: mean |grad T| in contested cells (ratio>0.8) >= 1.4x mean + in secured cells (ratio<0.3). Inertness: also reported at 0.7/0.9. + B-E2 silence: secured-territory mean |grad T| <= band mean. + B-E3 control: random-position centers, same rule -> ratio in [0.8, 1.2]. + +Same store/timestep as the whole arc (WB2, t=91246). Planar per-center km +approx with cos(lat_center) — consistent with the sibling probes, stated. +Hoehenprofil (geopotential stacking) deliberately NOT here — next probe. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T_IDX = 91246 +R_E = 6371.0 +RINGS_KM, R_MAX_KM = 100.0, 2000.0 +MIN_SEP_KM = 400.0 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=180).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +lat = fetch("latitude", "0").astype(np.float64).ravel() +p = fetch("mean_sea_level_pressure", f"{T_IDX}.0.0")[0].astype(np.float64) +t2 = fetch("2m_temperature", f"{T_IDX}.0.0")[0].astype(np.float64) +phi = np.deg2rad(lat) +lon_deg = np.arange(p.shape[1]) * 0.25 + +r0, r1 = 60, 261 # 75N .. 25N band (descending lat) +band = slice(r0, r1) +pa = p - p.mean(axis=1, keepdims=True) +pa_band = pa[band].copy() +print(f"band: lat {lat[r0]:.1f}..{lat[r1-1]:.1f}, shape {pa_band.shape}, " + f"var {pa_band.var():.0f} Pa^2") + + +def dist_km(ci, cj): + """km distance from band gridpoints to center (band-row ci, col cj).""" + dlon = np.deg2rad((lon_deg[None, :] - lon_deg[cj] + 180) % 360 - 180) + dphi = phi[band][:, None] - phi[r0 + ci] + dx = R_E * np.cos(phi[r0 + ci]) * dlon + dy = R_E * dphi + return np.hypot(dx * np.ones_like(dy), dy * np.ones_like(dlon)) + + +def fit_atom(res, ci, cj): + """Ring-mean radial profile of `res` around (ci, cj), subtractable field.""" + r = dist_km(ci, cj) + rings = (r / RINGS_KM).astype(int) + atom = np.zeros_like(res) + for b in range(int(R_MAX_KM / RINGS_KM)): + m = rings == b + if m.any(): + atom[m] = res[m].mean() + atom[r > R_MAX_KM] = 0.0 + return atom, r + + +def matching_pursuit(field, k_max, centers=None): + """Greedy K radial atoms; centers picked from residual argmax (or given).""" + res = field.copy() + v0 = field.var() + used, explained = [], [] + for k in range(k_max): + if centers is not None: + ci, cj = centers[k] + else: + cand = np.abs(res).copy() + for (ui, uj) in used: + cand[dist_km(ui, uj) < MIN_SEP_KM] = 0 + ci, cj = np.unravel_index(np.argmax(cand), res.shape) + atom, _ = fit_atom(res, ci, cj) + res -= atom + used.append((int(ci), int(cj))) + explained.append(1.0 - res.var() / v0) + return used, explained + + +# ---- Part A ----------------------------------------------------------- +K = 10 +centers, expl = matching_pursuit(pa_band, K) +print("\nA: greedy multi-center radial superposition (variance explained):") +for k, e in enumerate(expl): + ci, cj = centers[k] + print(f" K={k+1:2} R2={e:.3f} center lat={lat[r0+ci]:6.2f} " + f"lon={lon_deg[cj]:6.2f} p'={pa_band[ci, cj]:.0f} Pa " + f"({'H' if pa_band[ci, cj] > 0 else 'T'})") +a_e1 = expl[-1] + +rng = np.random.default_rng(7) +rand_centers = [(int(rng.integers(0, pa_band.shape[0])), + int(rng.integers(0, pa_band.shape[1]))) for _ in range(K)] +_, expl_rand = matching_pursuit(pa_band, K, centers=rand_centers) +a_e2 = expl_rand[-1] +print(f" A-E1 K=10 matched: R2={a_e1:.3f} (bar >= 0.55)") +print(f" A-E2 K=10 random: R2={a_e2:.3f} (bar <= {a_e1/2:.3f})") + +gains = np.diff(np.concatenate([[0.0], expl])) +ratios = gains[1:] / np.maximum(gains[:-1], 1e-12) +print(f" A-E3 marginal-gain decay ratios: " + f"{[f'{x:.2f}' for x in ratios]} mean={ratios.mean():.3f} " + f"(curiosity vs 1/phi=0.618 — NO bar)") + +# ---- Part B ----------------------------------------------------------- +det = [] +cand = np.abs(pa_band).copy() +while len(det) < 20: + ci, cj = np.unravel_index(np.argmax(cand), cand.shape) + if cand[ci, cj] < 400: + break + det.append((int(ci), int(cj), float(pa_band[ci, cj]))) + cand[dist_km(ci, cj) < 500.0] = 0 +print(f"\nB: {len(det)} centers (|p'|>=400 Pa), " + f"{sum(1 for d in det if d[2] > 0)} highs / " + f"{sum(1 for d in det if d[2] < 0)} lows") + + +def contested_secured(center_list, thresh): + inf = np.zeros((len(center_list),) + pa_band.shape) + for i, (ci, cj, amp) in enumerate(center_list): + r = np.maximum(dist_km(ci, cj), 50.0) + inf[i] = np.abs(amp) / r**2 + s = np.sort(inf, axis=0) + ratio = s[-2] / s[-1] + return ratio > thresh, ratio < 0.3 + + +dxm = R_E * 1e3 * np.cos(phi[band]) * np.deg2rad(0.25) +dym = R_E * 1e3 * np.deg2rad(0.25) +t2b = t2[band] +gTx = (np.roll(t2b, -1, 1) - np.roll(t2b, 1, 1)) / (2 * dxm[:, None]) +gTy = np.gradient(t2b, axis=0) / -dym # lat descends +gradT = np.hypot(gTx, gTy) * 1e5 # K per 100 km +band_mean = gradT.mean() + +out_b = {} +for th in (0.7, 0.8, 0.9): + contested, secured = contested_secured(det, th) + ratio = gradT[contested].mean() / gradT[secured].mean() + out_b[str(th)] = { + "contested_frac": float(contested.mean()), + "gradT_contested": float(gradT[contested].mean()), + "gradT_secured": float(gradT[secured].mean()), + "ratio": float(ratio)} + print(f" thresh {th}: contested {contested.mean()*100:4.1f}% of band, " + f"|gradT| contested/secured = {gradT[contested].mean():.3f}/" + f"{gradT[secured].mean():.3f} = {ratio:.2f}x") +b_e1 = out_b["0.8"]["ratio"] +_, secured08 = contested_secured(det, 0.8) +b_e2 = bool(gradT[secured08].mean() <= band_mean) + +rand_det = [(int(rng.integers(0, pa_band.shape[0])), + int(rng.integers(0, pa_band.shape[1])), amp) + for (_, _, amp) in det] +c_r, s_r = contested_secured(rand_det, 0.8) +b_e3 = float(gradT[c_r].mean() / gradT[s_r].mean()) +print(f" B-E1 ratio@0.8 = {b_e1:.2f}x (bar >= 1.4) " + f"B-E2 secured {gradT[secured08].mean():.3f} <= band {band_mean:.3f}: " + f"{b_e2} B-E3 random-centers ratio = {b_e3:.2f} (bar 0.8..1.2)") + +json.dump({ + "store": B, "time_index": T_IDX, "band_lat": [25, 75], + "A": {"explained_by_k": expl, "centers": [ + {"lat": float(lat[r0 + ci]), "lon": float(lon_deg[cj])} + for ci, cj in centers], + "E1": a_e1, "E2_random": a_e2, + "E3_decay_ratios": ratios.tolist(), "E3_mean": float(ratios.mean())}, + "B": {"n_centers": len(det), "by_thresh": out_b, + "E2_secured_le_bandmean": b_e2, "E3_random_ratio": b_e3, + "gradT_band_mean_K_per_100km": float(band_mean)}, +}, open("go_territory_probe.json", "w"), indent=2) +print("\nwrote go_territory_probe.json") From db57aac05e8226f9692c9b270cae486f9e5ad39f Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 20:01:09 +0000 Subject: [PATCH 04/23] =?UTF-8?q?probe(weather):=20comet-tail=20=E2=80=94?= =?UTF-8?q?=20the=20missing=20axisymmetry=20IS=20wavenumber-1,=20aligned?= =?UTF-8?q?=20left-of-motion,=202/2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Operator: "die fehlende Achsensymmetrie mit Flugzeug-Formel x Traegheit berechnen (wie beim Kometen dessen Schweif nach hinten drueckt)". The physics is textbook, not invented: a translating vortex = vortex in steering flow; geostrophy makes it SIGNED — background gradient perpendicular to motion, low pole LEFT of motion (NH); a linear background survives ring-mean removal entirely as wavenumber-1 with amplitude ~ r. Pre-registered CT-E1..E5, two storms (replication), ALL BARS MET: CT-E1 wn-1 share of the azimuthal residual: 0.924 / 0.895 (bar 0.40) — the comet tail is not just the dominant asymmetry mode, it is nearly ALL of it. CT-E2 trackable: 279 km/6h and 440 km/6h displacements. CT-E3 the SIGNED test: low pole at -42 deg and -40 deg from the predicted left-of-motion bearing — both HIT (+/-45 bar), both storms on the SAME side. n=2, null p=0.0625, stated not overclaimed. The systematic ~-40 deg common offset is an OBSERVATION [S] (Ekman surface-wind rotation is a candidate mechanism, unexamined). CT-E4 profile + per-ring wn-1 explains 0.972 / 0.926 of in-disk variance (bar 0.80) — up from 0.635 / 0.294 profile-only. This RESOLVES the sunflower probe's E1 fail: the missing third of the storm WAS the tail. Storm 2: one dipole takes 0.294 -> 0.926. CT-E5 a1(r) vs r correlation 0.800 / 0.998 — the linear-background signature, near-perfect on storm 2. Encoding consequence (design, [S] until specced): a storm compresses to PLACE (center) + ~12 ring means + ONE dipole vector = 93-97% of in-disk structure — the highheelbgz (start,stride,len) spiral-address plus a few bytes. And the dipole ENCODES THE MOTION: bearing(low pole) - 90 deg estimated the 6h displacement direction within 45 deg on both storms from a SINGLE timestep — a single-frame motion predictor, next falsifier. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/comet_tail_probe.json | 55 +++++++ probes/weather-p1/comet_tail_probe.py | 183 ++++++++++++++++++++++++ 2 files changed, 238 insertions(+) create mode 100644 probes/weather-p1/comet_tail_probe.json create mode 100644 probes/weather-p1/comet_tail_probe.py diff --git a/probes/weather-p1/comet_tail_probe.json b/probes/weather-p1/comet_tail_probe.json new file mode 100644 index 00000000..e9edd3a0 --- /dev/null +++ b/probes/weather-p1/comet_tail_probe.json @@ -0,0 +1,55 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "t0": 91246, + "t1": 91247, + "R_disk_km": 1200.0, + "storm1": { + "center_t0": { + "lat": 55.75, + "lon": 334.5 + }, + "center_t1": { + "lat": 56.5, + "lon": 338.75 + }, + "displacement_km": 278.73766575720873, + "motion_bearing_deg": 17.40910408001618, + "low_pole_bearing_deg": 65.44200886355121, + "predicted_low_pole_deg": 107.40910408001618, + "alignment_error_deg": -41.96709521646496, + "R2_profile": 0.6348125006744301, + "R2_profile_wn1": 0.9722877428704574, + "wn1_frac_of_resid": 0.9243451823302548, + "low_pole_bearing_rad": 1.142178523788281, + "amp_vs_r_corr": 0.8002995480605919, + "CT_E1_wn1_frac": 0.9243451823302548, + "CT_E2_trackable": true, + "CT_E3_left_of_motion_within_45": true, + "CT_E4_R2_profile_wn1": 0.9722877428704574 + }, + "storm2": { + "center_t0": { + "lat": 67.0, + "lon": 28.0 + }, + "center_t1": { + "lat": 69.0, + "lon": 36.75 + }, + "displacement_km": 440.43381595559754, + "motion_bearing_deg": 30.32694785348875, + "low_pole_bearing_deg": 80.11749355354193, + "predicted_low_pole_deg": 120.32694785348876, + "alignment_error_deg": -40.209454299946835, + "R2_profile": 0.29427821878043336, + "R2_profile_wn1": 0.9259330249471591, + "wn1_frac_of_resid": 0.8948035500704673, + "low_pole_bearing_rad": 1.3983140509546386, + "amp_vs_r_corr": 0.9977355074833578, + "CT_E1_wn1_frac": 0.8948035500704673, + "CT_E2_trackable": true, + "CT_E3_left_of_motion_within_45": true, + "CT_E4_R2_profile_wn1": 0.9259330249471591 + }, + "CT_E3_joint_hits": 2 +} \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_probe.py b/probes/weather-p1/comet_tail_probe.py new file mode 100644 index 00000000..7df845b1 --- /dev/null +++ b/probes/weather-p1/comet_tail_probe.py @@ -0,0 +1,183 @@ +"""EXPLORATORY — 'die fehlende Achsensymmetrie mit Flugzeug-Formel x Traegheit +berechnen, wie beim Kometen dessen Schweif nach hinten drueckt' (operator, +2026-08-11). NOT an EV; bars mine, unaudited. + +The physics is textbook, not invented: a translating vortex = a vortex in a +steering flow (the airplane's relative wind). Geostrophy makes it SIGNED and +falsifiable: if the storm moves WITH the geostrophic steering flow, the +background pressure gradient is PERPENDICULAR to the motion with the LOW pole +to the LEFT of motion (NH). A linear background gradient has zero ring-mean, +so it survives ring-profile removal ENTIRELY as a wavenumber-1 residual with +amplitude growing ~linearly in r. The comet tail IS wavenumber-1. + +PRE-REGISTERED (per storm; two storms = replication, n=2 smallness stated): + CT-E1 wavenumber-1 carries >= 0.40 of the azimuthal-residual variance in + the disk (R=1200 km) — the tail is the DOMINANT asymmetry mode. + CT-E2 motion trackable: displacement t -> t+6h >= 100 km within a 600 km + search radius, else NO-VERDICT (direction undefined below that). + CT-E3 THE SIGNED TEST: bearing(wn-1 low pole) = bearing(motion) + 90 deg + (CCW, x=east/y=north) within +/-45 deg. Null probability 0.25 per + storm; 2/2 hits = 0.0625 under null — reported as exactly that. + CT-E4 ring-profile + per-ring wn-1 explains >= 0.80 of in-disk variance + (single-storm baseline was 0.639 profile-only). + CT-E5 bonus observation (no bar): wn-1 amplitude a1(r) approx linear in r + (the linear-background signature); report corr(a1, r). + +Same store as the arc (WB2 ERA5 6h). t=91246 -> 91247 (2021-06-15 12Z->18Z). +Storm 1: the arc's storm (55.75N 334.5E). Storm 2: the #3 center from +go_territory_probe (67.0N 28.0E) as replication. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T0, T1 = 91246, 91247 +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=180).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +lat = fetch("latitude", "0").astype(np.float64).ravel() +p0 = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +p1 = fetch("mean_sea_level_pressure", f"{T1}.0.0")[0].astype(np.float64) +phi = np.deg2rad(lat) +lon_deg = np.arange(p0.shape[1]) * 0.25 + + +def geom(ci, cj): + """dx,dy,r (km) and azimuth theta (rad, CCW from east) rel. to center.""" + dlon = np.deg2rad((lon_deg[None, :] - lon_deg[cj] + 180) % 360 - 180) + dphi = phi[:, None] - phi[ci] + dx = R_E * np.cos(phi[ci]) * dlon * np.ones((p0.shape[0], 1)) + dy = R_E * dphi * np.ones((1, p0.shape[1])) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find_center(field, near=None, radius_km=600.0): + """Deepest zonal-anomaly low, globally NH or within radius of `near`.""" + fa = field - field.mean(axis=1, keepdims=True) + mask = lat[:, None] > 15 + if near is not None: + _, _, r, _ = geom(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + return int(ci), int(cj) + + +def decompose(field, ci, cj): + """Ring-mean + per-ring wn-1 fit; returns metrics dict.""" + dx, dy, r, th = geom(ci, cj) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + c, s = np.cos(t), np.sin(t) + # least-squares wn-1 on the ring (cos/sin nearly orthogonal on a ring) + a1[b] = 2 * ((v - prof[b]) * c).mean() + b1[b] = 2 * ((v - prof[b]) * s).mean() + + resid0 = vals - prof[rings] # after profile + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + resid1 = resid0 - wn1 # after profile + wn-1 + + var_t = vals.var() + e1 = 1.0 - resid0.var() / var_t # profile-only R2 + e2 = 1.0 - resid1.var() / var_t # profile + wn-1 R2 + wn1_frac = wn1.var() / resid0.var() + + # amplitude-weighted dipole phase: bearing of the LOW pole + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) # outer rings weightier + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) # HIGH pole bearing + low_pole = (ph + np.pi) % (2 * np.pi) + r_mid = (np.arange(nb) + 0.5) * RING + a_corr = float(np.corrcoef(amp[1:], r_mid[1:])[0, 1]) + return {"R2_profile": float(e1), "R2_profile_wn1": float(e2), + "wn1_frac_of_resid": float(wn1_frac), + "low_pole_bearing_rad": float(low_pole), + "amp_vs_r_corr": a_corr} + + +def track(name, c0_hint=None): + ci0, cj0 = find_center(p0, near=c0_hint) + ci1, cj1 = find_center(p1, near=(ci0, cj0)) + dx, dy, _, _ = geom(ci0, cj0) + disp = np.array([dx[ci1, cj1], dy[ci1, cj1]]) + dist = float(np.hypot(*disp)) + motion_bearing = float(np.arctan2(disp[1], disp[0])) + d = decompose(p0, ci0, cj0) + + predicted = (motion_bearing + np.pi / 2) % (2 * np.pi) # left of motion + diff = (d["low_pole_bearing_rad"] - predicted + np.pi) % (2 * np.pi) - np.pi + res = { + "center_t0": {"lat": float(lat[ci0]), "lon": float(lon_deg[cj0])}, + "center_t1": {"lat": float(lat[ci1]), "lon": float(lon_deg[cj1])}, + "displacement_km": dist, + "motion_bearing_deg": float(np.rad2deg(motion_bearing)), + "low_pole_bearing_deg": float(np.rad2deg(d["low_pole_bearing_rad"])), + "predicted_low_pole_deg": float(np.rad2deg(predicted)), + "alignment_error_deg": float(np.rad2deg(diff)), + **d, + "CT_E1_wn1_frac": d["wn1_frac_of_resid"], + "CT_E2_trackable": dist >= 100.0, + "CT_E3_left_of_motion_within_45": bool(abs(np.rad2deg(diff)) <= 45), + "CT_E4_R2_profile_wn1": d["R2_profile_wn1"], + } + print(f"\n{name}: center ({lat[ci0]:.2f}N, {lon_deg[cj0]:.2f}E) " + f"-> ({lat[ci1]:.2f}N, {lon_deg[cj1]:.2f}E), {dist:.0f} km/6h " + f"(bearing {np.rad2deg(motion_bearing):.0f} deg CCW-from-east)") + print(f" CT-E1 wn-1 share of residual: {d['wn1_frac_of_resid']:.3f} " + f"(bar >= 0.40)") + print(f" CT-E2 trackable: {res['CT_E2_trackable']} ({dist:.0f} km)") + print(f" CT-E3 low pole {res['low_pole_bearing_deg']:.0f} deg vs " + f"predicted (motion+90) {res['predicted_low_pole_deg']:.0f} deg " + f"-> error {res['alignment_error_deg']:+.0f} deg " + f"({'HIT' if res['CT_E3_left_of_motion_within_45'] else 'MISS'}, " + f"bar +/-45)") + print(f" CT-E4 R2 profile-only {d['R2_profile']:.3f} -> " + f"profile+wn1 {d['R2_profile_wn1']:.3f} (bar >= 0.80)") + print(f" CT-E5 corr(a1(r), r) = {d['amp_vs_r_corr']:.3f} " + f"(linear-background signature; observation, no bar)") + return res + + +print("== comet-tail probe: wavenumber-1 asymmetry vs storm motion ==") +s1 = track("STORM 1 (the arc's storm)") +# storm 2 hint: 67.0N 28.0E from go_territory_probe +hint = (int(round((90 - 67.0) / 0.25)), int(round(28.0 / 0.25))) +s2 = track("STORM 2 (replication)", c0_hint=hint) + +hits = sum(s["CT_E3_left_of_motion_within_45"] for s in (s1, s2)) +print(f"\nCT-E3 joint: {hits}/2 within +/-45 deg of left-of-motion " + f"(null prob per storm 0.25; 2/2 -> p=0.0625 — n=2, stated, " + f"not overclaimed)") + +json.dump({"store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK, + "storm1": s1, "storm2": s2, "CT_E3_joint_hits": int(hits)}, + open("comet_tail_probe.json", "w"), indent=2) +print("wrote comet_tail_probe.json") From d9a98b86dfe2b3956163490ecd7f3dc765c157fc Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 20:12:06 +0000 Subject: [PATCH 05/23] docs(weather): comet-tail report at product-lead level + board entry MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit probes/weather-p1/COMET_TAIL_REPORT.md — the documentation artifact for db57aac0, written for a meteorology-literate product lead: physical basis (translating vortex = vortex in steering flow; geostrophic left-of-motion low pole; linear background = pure wavenumber-1 with a1 ∝ r), method, the pre-registered falsification table (CT-E1..E5, all bars met on 2/2 storms), what it resolves (the sunflower axisymmetry FAIL — one missing mode, not many), and the follow-up section the operator asked for: - the systematic −42°/−40° offset with three ranked [S] candidates (steering-level/baroclinic-tilt rotation > Ekman surface friction > center-finder bias) and pre-registered falsifiers CT-F1 (repeat at z500), CT-F2 (measured 10m cross-isobar inflow angle), CT-F3 (center jitter/apparatus exclusion FIRST) — proposed, NOT run, n>=10 before any offset constant is baked in. - encoding consequence kept [S]: center + ~12 ring means + ONE dipole = 93–97%, dipole encodes motion; single-frame motion predictor named as next falsifier, gated behind the plan-§8 audit. Board: EPIPHANIES.md prepend E-CYCLONE-ASYMMETRY-IS-ONE-DIPOLE-1 (same-commit per the board-hygiene rule). Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 25 +++ probes/weather-p1/COMET_TAIL_REPORT.md | 202 +++++++++++++++++++++++++ 2 files changed, 227 insertions(+) create mode 100644 probes/weather-p1/COMET_TAIL_REPORT.md diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 5b4fd209..a5a1a20b 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,28 @@ +## 2026-08-11 — E-CYCLONE-ASYMMETRY-IS-ONE-DIPOLE-1 + +**Status:** FINDING `[G]` for the measurements (n=2, pre-registered, committed +unmodified, `db57aac0`); `[H]` for generalization; the −40° offset mechanism +and the encoding consequence are `[S]`. Full documentation: +`probes/weather-p1/COMET_TAIL_REPORT.md`. + +**The non-axisymmetric residual of a translating extratropical low is almost +entirely wavenumber-1, and the dipole's orientation is predicted by the +storm's own motion vector.** Measured on two real storms (WB2 ERA5 MSLP, +2021-06-15 12Z→18Z): wn-1 carries 0.924 / 0.895 of the azimuthal residual; +ring-profile + one dipole lifts in-disk R² from 0.635/0.294 to +**0.972/0.926**; low pole lands left-of-motion (NH geostrophic prediction) +within ±45° on 2/2 (null 0.0625), with a *common* −42°/−40° offset — +steering-level/baroclinic-tilt rotation is the prime `[S]` candidate, Ekman +friction second, center-finder bias to be excluded first (CT-F1..F3 +pre-registered in the report, not run). + +**Why it matters here:** (a) it RESOLVES the sunflower probe's axisymmetry +FAIL — the missing third of the storm WAS the tail, one mode, not many; (b) +the compact form `center + ~12 ring means + ONE dipole ≈ 93–97 %` fits the +highheelbgz (start, stride, length) address shape, and the dipole encodes the +motion — a single-frame motion predictor is the named next falsifier, gated +behind the plan-§8 adversarial audit per E-ZERO-FOR-ELEVEN below. + ## 2026-08-11 — E-ZERO-FOR-ELEVEN-THE-AUTHOR-CANNOT-AUDIT-HIS-OWN-FALSIFIERS-1 **Status:** FINDING `[G]` — measured by the 13-agent verify/attack pass on diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md new file mode 100644 index 00000000..801ba772 --- /dev/null +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -0,0 +1,202 @@ +# Comet-Tail Report — Wavenumber-1 Asymmetry of Translating Cyclones + +> **Audience:** meteorology-literate product lead (geostrophy, Ekman layer, +> steering flow, azimuthal wavenumber decomposition assumed known). +> **Status:** EXPLORATORY probe result, all pre-registered bars met (2/2 +> storms). NOT a promoted EV — the audit gate (plan §8) has not run on it. +> **Provenance:** `comet_tail_probe.py` / `.json`, commit `db57aac0`. +> Data: WeatherBench2 ERA5 6h 0.25° (`1959-2022-6h-1440x721.zarr`), +> `mean_sea_level_pressure`, t=91246→91247 (2021-06-15 12Z→18Z). +> **Grades:** `[G]` measured/textbook · `[H]` bounded inference · `[S]` +> speculation, labelled as such. + +--- + +## 1. Executive summary + +An axisymmetric (ring-profile) model of an extratropical low leaves ~30–70 % +of the in-disk MSLP variance unexplained. This probe shows that on two real +storms, **89–92 % of that azimuthal residual is a single wavenumber-1 dipole** +— the "comet tail" — whose orientation is **predicted by the storm's own +motion vector** (low pole left of motion, NH), and whose amplitude grows +~linearly with radius, exactly the signature of a linear background pressure +gradient advecting the vortex. `[G]` for the measurements; `[H]` for the +generalization beyond n=2. + +Product consequence `[S]`: a storm's pressure field compresses to +**center position + ~12 ring means + one dipole vector** at 93–97 % variance +explained, and the dipole *encodes the motion* — a candidate single-frame +motion predictor and a natural fit for the substrate's 3-integer spiral +addressing (`highheelbgz`). + +--- + +## 2. Physical basis (existing theory, nothing invented) + +All `[G]`, textbook dynamic meteorology: + +1. **A translating vortex is a vortex embedded in a steering flow.** In the + vortex-relative frame the environment appears as a superposed background + flow (the "airplane relative wind" framing). +2. **Geostrophy makes the geometry signed.** If the vortex translates with + the geostrophic steering flow `v_g = (1/fρ) k×∇p`, the background pressure + gradient is *perpendicular* to the motion, with the **low pole 90° to the + LEFT of the motion vector** (Northern Hemisphere; sign flips in SH). +3. **A linear background gradient is pure wavenumber-1.** Around any circle + centered on the vortex, a linear field `p_bg = a·x + b·y` has zero ring + mean and projects entirely onto `cos(θ−θ₀)` with amplitude + `√(a²+b²)·r` — i.e. it survives ring-profile removal *completely* as a + wn-1 residual growing linearly in radius. + +So the theory yields three independent, falsifiable predictions: dominance of +wn-1 in the residual, a *signed* orientation locked to the motion vector, and +`a₁(r) ∝ r`. The probe tests all three. + +Caveat stated up front `[G]`: real extratropical cyclones are baroclinic and +are steered by the mid-tropospheric flow, not the surface gradient alone; the +surface-level prediction is therefore expected to hold *up to a systematic +rotation* (see §6). The probe's ±45° tolerance was chosen to admit that +rotation while still rejecting the null (uniform orientation) at 0.25 per +storm. + +--- + +## 3. Data and method + +- **Domain:** disk of R = 1200 km around the detected center, planar + local-tangent geometry with `cos(lat_center)` zonal metric; 100 km rings. +- **Center finding:** deepest zonal-anomaly MSLP minimum; at t+6h the center + is re-found within a 600 km search radius (trackability gate CT-E2). +- **Decomposition:** per-ring mean → radial profile `p̄(r)`; per-ring + least-squares wavenumber-1 fit + `a₁(r)·cos θ + b₁(r)·sin θ` on the residual; amplitude-weighted mean dipole + bearing across rings → the **low-pole bearing** (`+π` from the high pole of + the fitted dipole). +- **Motion:** bearing of the 6h center displacement; predicted low pole = + motion bearing + 90° CCW. +- **Storms:** (1) 55.75N 334.5E — the arc's reference storm; (2) 67.0N 28.0E + — an independent center found by the preceding go-territory probe, used as + replication. + +--- + +## 4. Falsification design and results + +All bars pre-registered in the probe docstring *before* the run; the run was +committed unmodified. Null model for CT-E3: uniform dipole orientation → +P(hit ±45°) = 0.25 per storm; 2/2 joint = 0.0625. n=2 is stated, not hidden. + +| Bar | Pre-registered criterion | Storm 1 (55.75N) | Storm 2 (67N) | Verdict | +|---|---|---|---|---| +| **CT-E1** | wn-1 ≥ 0.40 of azimuthal-residual variance | **0.924** | **0.895** | PASS — wn-1 is not merely dominant, it is nearly *all* of the asymmetry | +| **CT-E2** | trackable: displacement ≥ 100 km / 6h within 600 km | 279 km | 440 km | PASS | +| **CT-E3** | **signed:** low pole = motion + 90° CCW, within ±45° | error **−42.0°** | error **−40.2°** | PASS 2/2, same side both storms | +| **CT-E4** | profile + wn-1 explains ≥ 0.80 of in-disk variance | 0.635 → **0.972** | 0.294 → **0.926** | PASS | +| **CT-E5** | observation (no bar): `a₁(r) ∝ r` | corr **0.800** | corr **0.998** | consistent with linear-background signature | + +**Interpretation discipline:** CT-E1/E4/E5 are unsigned goodness-of-fit +results — a skeptic could attribute them to "any smooth large-scale gradient." +CT-E3 is the load-bearing test: it is *signed by an independent quantity* +(the motion vector, measured from a different pair of fields), and both storms +land on the predicted side. That is what elevates this from curve-fitting to +physics `[H at n=2]`. + +**What this resolves:** the earlier sunflower-lattice probe failed its +axisymmetry bar (E1: 0.639 < 0.70) — the missing third of the storm was +unexplained. It *was* the tail: adding one dipole per ring takes the same +storm from R² 0.635 to 0.972. The golden-spiral/ring encoding was not wrong, +it was incomplete by exactly one mode. + +--- + +## 5. The systematic −40° offset — follow-up work (NOT yet run) + +Both storms miss the naive 90°-left prediction by **−42° and −40°** — nearly +identical magnitude, same rotation sense. With n=2 this is an observation +`[S]`, but a common offset of matched size is the signature of a *systematic +mechanism*, not noise. Three candidates, ranked: + +1. **Steering-level / baroclinic-tilt rotation** `[S]`, prime candidate. + Extratropical cyclones translate with the mid-tropospheric (≈500–700 hPa) + steering flow. The *surface* background gradient is rotated relative to + the steering-level gradient by the thermal wind (the system's westward + tilt with height). The probe measured the dipole at MSLP but the motion is + set aloft — a fixed rotation between the two is expected, not anomalous. +2. **Ekman / surface-friction turning** `[S]`. Boundary-layer friction turns + the surface flow 10–30° cross-isobar toward low pressure (more over land, + less over open ocean) and drives Ekman pumping that distorts the surface + pressure asymmetry. Magnitude range is plausibly consistent with −40° in + combination with (1), unlikely to explain it alone over ocean. +3. **Center-finder bias** `[S]`, must be excluded before believing either + mechanism. "Deepest zonal-anomaly point" ≠ circulation center; a center + displaced along-track biases the fitted dipole orientation. + +**Pre-registered follow-up falsifiers (proposed, awaiting go — not run):** + +- **CT-F1 (steering level):** repeat the full decomposition on + `geopotential` at 500 hPa (available in the same WB2 store). *Prediction:* + the alignment error shrinks toward 0° at the steering level (|error| ≤ 20°) + while remaining ≈ −40° at MSLP. If the offset persists unchanged at 500 hPa, + candidate 1 is dead. +- **CT-F2 (friction):** measure the actual cross-isobar inflow angle from + `10m_u/v_component_of_wind` vs the MSLP isobars in the storm ring. + *Prediction:* measured inflow angle ≪ 40° over open ocean — bounding how + much of the offset friction can own. +- **CT-F3 (center robustness):** recompute the dipole with the center jittered + ±100 km along and across track, and with a vorticity-centroid center. + *Prediction:* low-pole bearing stable within ±10°; if it swings with the + center choice, the −40° is apparatus, and §4's CT-E3 verdict must be + re-graded (the arc's standing rule: a systematic number is a claim about + the measurement apparatus until proven otherwise). +- **Sample size:** n ≥ 10 storms across seasons/basins before any offset + constant is baked into a predictor. 2/2 at p=0.0625 justifies the follow-up, + not a product constant. + +--- + +## 6. Product / encoding consequence `[S]` + +If CT-F1..F3 hold up, the compact representation of a surface low is: + +``` +storm ≈ CENTER (place) — 1 address + + p̄(r) ring-profile means — ~12 bytes (12 × 100 km rings, u8-quantizable per voxel-chess probe: u8 max dev 0.0047) + + (a₁,b₁) ONE dipole vector — 2 values (amplitude slope + bearing) + = 93–97 % of in-disk MSLP variance +``` + +- Maps directly onto `highheelbgz`'s 3-integer spiral **address** form + (start, stride, length) + a short payload — the storm is stored as a place + plus ~14 bytes, values recomputed on demand, instead of a raster crop. +- **The dipole encodes the motion.** `bearing(low pole) − 90° (− offset)` + estimated the 6h displacement *direction* within tolerance on both storms + **from a single timestep**. Named next falsifier: the single-frame motion + predictor, evaluated on n ≥ 10 storms against the observed 6h track, with + the offset constant fitted on a disjoint training set. Not executed — + awaiting go, and gated behind the adversarial audit per the arc's standing + rule (exploratory probes are not EVs; the 0/11 lesson). + +## 7. Limitations and non-claims + +- **n=2, one synoptic time, one season, NH only.** No claim of climatological + generality; SH sign flip untested. +- **MSLP only.** No upper-air, no moisture, no intensity change — this is a + *structure* result, not a forecast skill result. +- **Planar geometry** within 1200 km (`cos(lat)` metric); fine at 55–67°N for + this radius, degrades toward the pole. +- **The 6h displacement is the label, not a forecast** — CT-E3 tests + consistency between two simultaneous measurements (dipole at t0, motion + t0→t1); the *predictor* framing is future work (§6). +- The ±45° bar was set to admit the expected baroclinic rotation. That was + the right call for a first signed test, but it means the current result + cannot distinguish candidates 1–3 in §5 — that is exactly what CT-F1..F3 + are for. + +## 8. Status and promotion path + +1. ~~Probe, pre-registered, run, committed~~ — DONE (`db57aac0`). +2. **This report** — the documentation artifact. +3. CT-F3 first (apparatus before mechanism), then CT-F1/CT-F2 — on operator go. +4. n ≥ 10 storm sample; only then the single-frame motion predictor. +5. Adversarial audit gate (plan §8) before any of it is promoted to EV / + product claim. From f6310b0e7bfa03bd03aa505df4ae9fb84681bc33 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 20:21:35 +0000 Subject: [PATCH 06/23] =?UTF-8?q?probe(weather):=20comet-tail=20follow-up?= =?UTF-8?q?=20=E2=80=94=20my=20apparatus=20gate=20FAILED,=20the=20-40=20de?= =?UTF-8?q?g=20constant=20is=20dead,=20the=20height=20ladder=20is=20real?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CT-F1/F2/F3 as pre-registered in COMET_TAIL_REPORT.md sec.5. Bars written before the run; committed unmodified apart from a grad_p shape-bug fix and one ADDED DIAGNOSTIC field, both recorded in the probe's own RUN LOG. No bar was added, removed, or loosened. CT-F3 APPARATUS (ran FIRST, as the rule requires) — FAILED its gate. +/-100 km center jitter moves the alignment error by up to 29.4 deg (storm1; storm2 19.4) against a 20 deg bar. The -42/-40 offset is INSIDE the apparatus noise => no offset constant is measurable at this centering precision. CT-E3 re-graded in place: the +/-45 left-of-motion HIT stands, the magnitude does not. CT-F1 STEERING LEVEL — strong signal, formally mixed. All 13 levels ship in one chunk, so the yes/no became a ladder: alignment error climbs MONOTONICALLY from ~-40 deg at 1000 hPa through zero in the mid-troposphere (~600-650 hPa storm1, ~400-500 storm2), spread 101.8/91.6 deg = 3-5x the apparatus noise. That is the baroclinic-tilt prediction. Storm2's own-center path tripped the dead-absurd flag at 100 hPa, but the added diagnostic shows its center finder SATURATED at the 600 km search radius (586-599 km) and had locked onto a different system => NO-VERDICT on that path, not a refutation. CT-F2 FRICTION — bounded. Measured 10m cross-isobar inflow +14.7 deg (storm1, 99% ocean) and +13.0 deg ocean-only (storm2). Right sign, textbook 10-30 deg magnitude, and 13-15 << 40 => friction owns ~1/3 of the offset at most. Unplanned can-it-fire check: storm2 is 46% land and its all-points median (+22.0) exceeds its ocean-only median (+13.0) — turning is stronger over land, as textbook, so the measurement discriminates in the right direction. Candidates after F1-F3: tilt FAVOURED, friction BOUNDED CONTRIBUTOR, center-bias LIVE AND NOT EXCLUDED. Not mutually exclusive. The 3-5x size comparison is recorded as a POST-HOC OBSERVATION and routes to CT-F4 (sub-grid center fit), NOT used to override the failed gate — that override is the indictment-fired/post-hoc-rescue anti-pattern already on this arc's open-P1 list. CT-F4 is now the blocking item ahead of the single-frame motion predictor. Headline claim untouched: wn-1 dominance (0.924/0.895), R2 lift to 0.972/0.926, and left-of-motion 2/2 never depended on the offset. Board: EPIPHANIES prepend E-THE-OFFSET-WAS-THE-APPARATUS-THE-LADDER-WAS-THE-PHYSICS-1 (same commit). Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 39 ++ probes/weather-p1/COMET_TAIL_REPORT.md | 168 ++++++-- probes/weather-p1/comet_tail_followup.json | 447 +++++++++++++++++++++ probes/weather-p1/comet_tail_followup.py | 395 ++++++++++++++++++ 4 files changed, 1025 insertions(+), 24 deletions(-) create mode 100644 probes/weather-p1/comet_tail_followup.json create mode 100644 probes/weather-p1/comet_tail_followup.py diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index a5a1a20b..ed539892 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,42 @@ +## 2026-08-11 — E-THE-OFFSET-WAS-THE-APPARATUS-THE-LADDER-WAS-THE-PHYSICS-1 + +**Status:** FINDING `[G]` — CT-F1/F2/F3 run, `comet_tail_followup.py` / `.json`, +report §5. Follow-up to E-CYCLONE-ASYMMETRY-IS-ONE-DIPOLE-1 below. + +**I ran my own apparatus gate first and it FAILED, which killed the number I +was about to explain.** The −42°/−40° common offset moves by up to **29.4°** +under a ±100 km center jitter — comparable to the offset itself. So the offset +magnitude is inside the apparatus noise and no constant may be fitted from it; +CT-E3 is re-graded in place (the ±45° left-of-motion HIT stands, the magnitude +does not). + +**But the mechanism test found a much bigger signal than the thing it was sent +to explain.** Sweeping geopotential over all 13 levels (one chunk, so the +yes/no became a ladder): the alignment error climbs **monotonically** from +≈ −40° at 1000 hPa through zero in the mid-troposphere (≈600–650 hPa storm 1, +≈400–500 hPa storm 2), spread 101.8°/91.6° — **3–5× the apparatus noise**, and +exactly the baroclinic-tilt/steering-level prediction. Friction was +independently bounded: measured 10m cross-isobar inflow +14.7°/+13.0° over +ocean (right sign, textbook magnitude), so it owns ≈⅓ of the offset at most. + +**Three transferable rules, each earned here:** +1. *Apparatus before mechanism, and mean it.* Had F3 run second, both F1 and F2 + would have been written up as explanations of a number that isn't there. +2. *A failed gate generates the next probe, not an exemption.* The 3–5× size + comparison is recorded as a post-hoc observation and routes to CT-F4 + (sub-grid center fit) — NOT used to override the failed bar. That override + is the "indictment fired → post-hoc rescue" anti-pattern already on this + arc's open-P1 list. +3. *Add diagnostics, never bars, after a run.* A diagnostic added post-run-1 + caught F1's own defect — its center finder **saturating at the 600 km search + radius** on storm 2's upper levels (it locked onto a different system), which + had produced a physically absurd "best level = 100 hPa". Verdict corrected to + NO-VERDICT on that path rather than reported as a refutation. + +The headline claim is untouched: wn-1 dominance, the R² lift, and +left-of-motion never depended on the offset. What died is the constant I would +otherwise have shipped. + ## 2026-08-11 — E-CYCLONE-ASYMMETRY-IS-ONE-DIPOLE-1 **Status:** FINDING `[G]` for the measurements (n=2, pre-registered, committed diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 801ba772..c0163d1d 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -101,6 +101,16 @@ CT-E3 is the load-bearing test: it is *signed by an independent quantity* land on the predicted side. That is what elevates this from curve-fitting to physics `[H at n=2]`. +> **⚠ CT-E3 RE-GRADED 2026-08-11 by CT-F3 (§5.1), as §5's pre-registration +> required.** The ±45° HIT stands — but the *offset magnitude* does not. A +> ±100 km center jitter moves the alignment error by up to **29.4°** (storm 1), +> which is comparable to the offset itself. So "−42° / −40°" is **NOT a robust +> number**: read CT-E3 as *"the low pole lies left-of-motion, within an +> apparatus uncertainty of roughly ±15°"*, and read the specific offset as +> unresolved at this centering precision. The *height dependence* of the error +> (§5.2) is a separate and much larger signal and is not affected by this +> re-grade. + **What this resolves:** the earlier sunflower-lattice probe failed its axisymmetry bar (E1: 0.639 < 0.70) — the missing third of the storm was unexplained. It *was* the tail: adding one dipole per ring takes the same @@ -131,26 +141,123 @@ mechanism*, not noise. Three candidates, ranked: mechanism. "Deepest zonal-anomaly point" ≠ circulation center; a center displaced along-track biases the fitted dipole orientation. -**Pre-registered follow-up falsifiers (proposed, awaiting go — not run):** - -- **CT-F1 (steering level):** repeat the full decomposition on - `geopotential` at 500 hPa (available in the same WB2 store). *Prediction:* - the alignment error shrinks toward 0° at the steering level (|error| ≤ 20°) - while remaining ≈ −40° at MSLP. If the offset persists unchanged at 500 hPa, - candidate 1 is dead. -- **CT-F2 (friction):** measure the actual cross-isobar inflow angle from - `10m_u/v_component_of_wind` vs the MSLP isobars in the storm ring. - *Prediction:* measured inflow angle ≪ 40° over open ocean — bounding how - much of the offset friction can own. -- **CT-F3 (center robustness):** recompute the dipole with the center jittered - ±100 km along and across track, and with a vorticity-centroid center. - *Prediction:* low-pole bearing stable within ±10°; if it swings with the - center choice, the −40° is apparatus, and §4's CT-E3 verdict must be - re-graded (the arc's standing rule: a systematic number is a claim about - the measurement apparatus until proven otherwise). -- **Sample size:** n ≥ 10 storms across seasons/basins before any offset - constant is baked into a predictor. 2/2 at p=0.0625 justifies the follow-up, - not a product constant. +**RUN 2026-08-11** — `comet_tail_followup.py` / `.json`. Bars pre-registered in +that probe's docstring before the run; committed unmodified except for one +`grad_p` shape bug fix and one added *diagnostic* field (both recorded in the +probe's own RUN LOG; **no bar was added, removed, or loosened**). + +### 5.1 CT-F3 — apparatus — **FAILED the gate** (candidate 3 is live) + +Six center choices per storm: MSLP minimum (baseline), a ∇²p-centroid +(geostrophic-vorticity proxy), and ±100 km jitters along- and across-track. + +| Storm | baseline | ∇²p centroid | jitter range | spread | verdict | +|---|---|---|---|---|---| +| 1 (55.75N) | −42.0° | −42.0° (same grid point) | −25.4° … −54.8° | **29.4°** | APPARATUS-DOMINATED | +| 2 (67N) | −40.2° | −38.6° (toward zero, marginal) | −29.8° … −49.3° | **19.4°** | SURVIVES-WITH-UNCERTAINTY | + +Worst spread 29.4° **> the 20° bar → gate FAILED.** Per the pre-registration +this forces two things, both done: CT-E3 is re-graded in §4, and CT-F1/CT-F2 +below are reported as *measured but gated* — not as settled verdicts. + +The mechanism is understood and was anticipated: a 100 km miscentering of a +monopole injects a wn-1 by construction. What the test establishes is that the +**offset magnitude is inside the apparatus noise**, so no offset constant may +be derived from it. `[G]` + +*Post-hoc observation, explicitly NOT a rescue of the failed gate:* the +level-dependence signal in §5.2 is 92–102°, i.e. **3–5× this apparatus noise**. +That does not un-fail F3 — it means the right next probe is F3 re-run *at the +level where the error crosses zero*, with a sub-grid center fit. Recording the +comparison and letting the failed gate generate the next probe is the +disciplined move; overriding the gate on the strength of it would be the +"indictment fired, post-hoc rescue" anti-pattern this arc already has on its +open-P1 list. + +### 5.2 CT-F1 — steering level — **strong signal, formally mixed** (candidate 1 favoured) + +The store ships all 13 pressure levels in one chunk, so the yes/no became a +sweep. Alignment error vs the *same surface-measured motion bearing*: + +| level | storm 1 (own ctr) | storm 2 (sfc ctr) | +|---|---|---| +| 1000 hPa | −40.5° | −39.7° | +| 925 | −32.5° | −29.9° | +| 850 | −23.8° | −22.3° | +| 700 | −8.1° | −12.9° | +| 600 | **−2.1°** | −7.7° | +| 500 | +2.9° | −2.8° | +| 400 | +8.7° | **+1.0°** | +| 300 | +7.8° | +2.0° | + +**Both storms show a smooth, monotone climb from ≈ −40° at the surface through +zero in the mid-troposphere** — storm 1 crosses at ~600–650 hPa, storm 2 at +~400–500 hPa. Spread across levels: 101.8° / 91.6°. This is exactly the +baroclinic-tilt/steering-level prediction: the surface gradient is rotated +relative to the steering-level gradient, and the rotation unwinds with height. +`[H]` — the shape is unambiguous, n is still 2. + +Formal bar bookkeeping, stated rather than smoothed: storm 1 **PASSES** as +written (minimum at 600 hPa, |err| 2.1°). Storm 2's *own-center* column +minimises at 100 hPa (+2.0°), which trips the pre-registered `dead-absurd` +flag — **but the added diagnostic shows why:** at 50–400 hPa the center finder +**saturated at its 600 km search radius** (586–599 km), i.e. it never found a +co-located upper center and locked onto a different system. Those rows are +apparatus, not physics. The surface-center column (unsaturated by +construction) is the one tabulated above and behaves like storm 1. This is a +real defect in F1's own apparatus, found by a diagnostic added after run 1; +the honest verdict for storm 2 is **NO-VERDICT on the own-center path**, not +"dead". + +### 5.3 CT-F2 — friction — **bounded, candidate 2 is a contributor not the cause** + +Measured 10m cross-isobar inflow angle (positive = turned toward the low, the +NH friction sign), rings 300–1000 km, |v10| > 3 m/s: + +| storm | n | land frac | median α | IQR | ocean-only median | +|---|---|---|---|---|---| +| 1 | 6552 | 0.01 | **+14.7°** | +10.1 … +18.2 | +14.7° (n=6517) | +| 2 | 8960 | 0.46 | +22.0° | +10.5 … +34.7 | **+13.0°** (n=4674) | + +Sign is positive on both storms, as predicted. Magnitude is textbook for open +ocean (10–30°). The bar that matters: **13–15° ≪ 40°**, so friction alone +**cannot** own the offset — at most about a third of it. `[G]` for the +measurement, `[H]` for the attribution. + +*Apparatus can-it-fire check, unplanned but load-bearing:* storm 2 is 46 % +land, and its all-points median (+22.0°) is substantially larger than its +ocean-only median (+13.0°) — friction turning is stronger over land, exactly +as textbook. The measurement therefore discriminates a known physical contrast +in the right direction, which is evidence it is measuring what it claims. + +### 5.4 Where that leaves the three candidates + +| candidate | status after F1–F3 | +|---|---| +| 1 — steering-level / baroclinic tilt | **Favoured** `[H]`. Monotone 92–102° height ladder crossing zero in the mid-troposphere on both storms, 3–5× the apparatus noise. | +| 2 — Ekman surface friction | **Bounded contributor** `[G]` on magnitude. 13–15° over ocean, right sign, ~⅓ of the offset at most. | +| 3 — center-finder bias | **LIVE and not excluded** `[G]`. ±100 km centering moves the answer up to 29.4° — enough to dominate the offset magnitude by itself. | + +Candidates 1–3 are **not mutually exclusive**, and the measured numbers are +roughly additive in the right direction (tilt ≈ most of it, friction ≈ 13–15°, +centering ≈ ±15° of slop). No attribution is claimed beyond that. + +### 5.5 Next falsifiers (NOT run) + +- **CT-F4 (apparatus, first):** sub-grid center fit — parabolic interpolation + of the pressure minimum plus a circulation-centroid at the level where the + error crosses zero — then re-run F3's jitter test. *Bar:* spread ≤ 10°. + Until this passes, no offset constant exists to be fitted. +- **CT-F5:** widen F1's center search per level (or track the upper center + along the tilt axis) so the saturation defect in §5.2 cannot recur; re-run + storm 2's own-center path for a real verdict. +- **CT-F6:** the crossing level itself as the observable — *prediction:* it + correlates with the deep-layer mean steering level, i.e. deeper/more mature + systems cross higher. Needs n ≥ 10. +- **Sample size, unchanged:** n ≥ 10 storms across seasons and basins before + any offset constant is baked into a predictor. 2/2 at p = 0.0625 justified + the follow-up; the follow-up now justifies fixing the apparatus, not + shipping a constant. --- @@ -195,8 +302,21 @@ storm ≈ CENTER (place) — 1 address ## 8. Status and promotion path 1. ~~Probe, pre-registered, run, committed~~ — DONE (`db57aac0`). -2. **This report** — the documentation artifact. -3. CT-F3 first (apparatus before mechanism), then CT-F1/CT-F2 — on operator go. -4. n ≥ 10 storm sample; only then the single-frame motion predictor. -5. Adversarial audit gate (plan §8) before any of it is promoted to EV / +2. ~~This report~~ — DONE (`d9a98b86`). +3. ~~CT-F3 first (apparatus before mechanism), then CT-F1/CT-F2~~ — DONE. + **F3 FAILED its gate**; CT-E3 re-graded in §4; F1 strongly favours the + steering-level mechanism; F2 bounds friction to ~⅓ of the offset. +4. **CT-F4 (sub-grid center) is now the blocking item** — the apparatus must + pass before any offset constant can be fitted, and therefore before the + single-frame motion predictor is worth running at all. +5. CT-F5 / CT-F6, then n ≥ 10 storm sample. +6. Adversarial audit gate (plan §8) before any of it is promoted to EV / product claim. + +**Net effect of the follow-up on the headline claim.** The §1 summary is +unchanged in substance — wn-1 dominance (0.92/0.89), the R² lift to +0.97/0.93, and left-of-motion on 2/2 all stand untouched, since none of them +depends on the offset. What the follow-up removed is a number I would +otherwise have been tempted to ship: the "−40° constant" is **not +measurable at this centering precision**, and the encoding/predictor work in +§6 is gated behind fixing that, not behind more storms. diff --git a/probes/weather-p1/comet_tail_followup.json b/probes/weather-p1/comet_tail_followup.json new file mode 100644 index 00000000..b11b5b7a --- /dev/null +++ b/probes/weather-p1/comet_tail_followup.json @@ -0,0 +1,447 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "t0": 91246, + "t1": 91247, + "R_disk_km": 1200.0, + "storms": {}, + "CT_F3": { + "per_storm": { + "storm1": { + "variants": { + "mslp_min_baseline": { + "lat": 55.75, + "lon": 334.5, + "error_deg": -41.96709521646497, + "wn1_frac": 0.9243451823302548, + "R2_profile_wn1": 0.9722877428704574 + }, + "laplacian_centroid": { + "lat": 55.75, + "lon": 334.5, + "error_deg": -41.96709521646497, + "wn1_frac": 0.9243451823302548, + "R2_profile_wn1": 0.9722877428704574 + }, + "jitter_along_+100km": { + "lat": 56.0, + "lon": 336.0, + "error_deg": -25.364593956108138, + "wn1_frac": 0.9163364290730204, + "R2_profile_wn1": 0.9749750702491462 + }, + "jitter_along_-100km": { + "lat": 55.5, + "lon": 333.0, + "error_deg": -51.689873398133045, + "wn1_frac": 0.9341109139751019, + "R2_profile_wn1": 0.9691441022398409 + }, + "jitter_across_+100km": { + "lat": 56.5, + "lon": 334.0, + "error_deg": -54.783536772466476, + "wn1_frac": 0.8750989688058749, + "R2_profile_wn1": 0.9649272116342785 + }, + "jitter_across_-100km": { + "lat": 55.0, + "lon": 335.0, + "error_deg": -31.916114494021485, + "wn1_frac": 0.9535333835128866, + "R2_profile_wn1": 0.9769806541442484 + } + }, + "spread_deg": 29.41894281635834, + "verdict": "APPARATUS-DOMINATED", + "lap_center_moves_error_toward_zero": false, + "baseline_error_deg": -41.96709521646497, + "lap_error_deg": -41.96709521646497 + }, + "storm2": { + "variants": { + 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"error_sfc_center_deg": -32.49848062992805, + "wn1_frac": 0.9291294230426438, + "R2_profile_wn1": 0.9715600799103316 + }, + { + "level_hPa": 1000, + "center_lat": 55.75, + "center_lon": 334.5, + "center_offset_km": 0.0, + "center_search_saturated": false, + "error_own_center_deg": -40.46281630581089, + "error_sfc_center_deg": -40.46281630581089, + "wn1_frac": 0.9239743148665512, + "R2_profile_wn1": 0.9719752417055713 + } + ], + "best_level_hPa": 600, + "best_error_deg": -2.0835685390135836, + "sfc_error_deg": -41.96709521646497, + "error_spread_across_levels_deg": 101.81886913622242, + "CT_F1_pass": true, + "CT_F1_dead_flat": false, + "CT_F1_dead_absurd_level": false + }, + "storm2": { + "per_level": [ + { + "level_hPa": 50, + "center_lat": 71.0, + "center_lon": 18.75, + "center_offset_km": 599.4520103083312, + "center_search_saturated": true, + "error_own_center_deg": 51.82054658169051, + "error_sfc_center_deg": 34.67018188925604, + "wn1_frac": 0.901432885819133, + "R2_profile_wn1": 0.9034844192118838 + }, + { + "level_hPa": 100, + "center_lat": 71.0, + "center_lon": 18.75, + "center_offset_km": 599.4520103083312, + "center_search_saturated": true, + "error_own_center_deg": 2.0164030755331908, + "error_sfc_center_deg": -5.240993564940538, + "wn1_frac": 0.9531737013560987, + "R2_profile_wn1": 0.9545025593312747 + }, + { + "level_hPa": 150, + "center_lat": 69.25, + "center_lon": 15.5, + "center_offset_km": 597.948738725999, + "center_search_saturated": true, + "error_own_center_deg": -7.972893067162033, + "error_sfc_center_deg": -4.654494247868968, + "wn1_frac": 0.9584459294666916, + "R2_profile_wn1": 0.9594776301341776 + }, + { + "level_hPa": 200, + "center_lat": 69.25, + "center_lon": 15.5, + "center_offset_km": 597.948738725999, + "center_search_saturated": true, + "error_own_center_deg": -10.674219833183258, + "error_sfc_center_deg": -3.239404372201534, + "wn1_frac": 0.9374814196459246, + "R2_profile_wn1": 0.9421606351558359 + }, + { + "level_hPa": 250, + "center_lat": 68.5, + "center_lon": 14.75, + "center_offset_km": 599.3527221144318, + "center_search_saturated": true, + "error_own_center_deg": -14.807646339441447, + "error_sfc_center_deg": 0.09672334053246345, + "wn1_frac": 0.9215579703496299, + "R2_profile_wn1": 0.9301151613267495 + }, + { + "level_hPa": 300, + "center_lat": 68.0, + "center_lon": 14.5, + "center_offset_km": 596.9858339490942, + "center_search_saturated": true, + "error_own_center_deg": -17.28080857434108, + "error_sfc_center_deg": 2.041052679989008, + "wn1_frac": 0.9097645829979871, + "R2_profile_wn1": 0.9213069581688746 + }, + { + "level_hPa": 400, + "center_lat": 68.0, + "center_lon": 14.75, + "center_offset_km": 586.3175725378001, + "center_search_saturated": true, + "error_own_center_deg": -19.27607397055641, + "error_sfc_center_deg": 1.0003071905722436, + "wn1_frac": 0.9134458751317969, + "R2_profile_wn1": 0.92330971883983 + }, + { + "level_hPa": 500, + "center_lat": 66.75, + "center_lon": 26.75, + "center_offset_km": 61.01027079938362, + "center_search_saturated": false, + "error_own_center_deg": -4.535940468762789, + "error_sfc_center_deg": -2.8101913403015146, + "wn1_frac": 0.979546746038315, + "R2_profile_wn1": 0.9796149846637433 + }, + { + "level_hPa": 600, + "center_lat": 66.75, + "center_lon": 26.75, + "center_offset_km": 61.01027079938362, + "center_search_saturated": false, + "error_own_center_deg": -9.453202593238132, + "error_sfc_center_deg": -7.692808120736544, + "wn1_frac": 0.9732871055886206, + "R2_profile_wn1": 0.9736253816050656 + }, + { + "level_hPa": 700, + "center_lat": 67.0, + "center_lon": 27.0, + "center_offset_km": 43.44731917011045, + "center_search_saturated": false, + "error_own_center_deg": -14.448233406134591, + "error_sfc_center_deg": -12.920053691199456, + "wn1_frac": 0.96208166112389, + "R2_profile_wn1": 0.9653193475155147 + }, + { + "level_hPa": 850, + "center_lat": 67.0, + "center_lon": 27.5, + "center_offset_km": 21.723659585055223, + "center_search_saturated": false, + "error_own_center_deg": -23.661401835548673, + "error_sfc_center_deg": -22.325237272421816, + "wn1_frac": 0.9453390686389784, + "R2_profile_wn1": 0.9538800816334196 + }, + { + "level_hPa": 925, + "center_lat": 67.0, + "center_lon": 27.75, + "center_offset_km": 10.861829792527612, + "center_search_saturated": false, + "error_own_center_deg": -30.747604434365485, + "error_sfc_center_deg": -29.861737215801043, + "wn1_frac": 0.9315082183168824, + "R2_profile_wn1": 0.9464905885127584 + }, + { + "level_hPa": 1000, + "center_lat": 67.0, + "center_lon": 28.0, + "center_offset_km": 0.0, + "center_search_saturated": false, + "error_own_center_deg": -39.746495421917615, + "error_sfc_center_deg": -39.746495421917615, + "wn1_frac": 0.9013658742632329, + "R2_profile_wn1": 0.9312405064761293 + } + ], + "best_level_hPa": 100, + "best_error_deg": 2.0164030755331908, + "sfc_error_deg": -40.20945429994683, + "error_spread_across_levels_deg": 91.56704200360812, + "CT_F1_pass": false, + "CT_F1_dead_flat": false, + "CT_F1_dead_absurd_level": true + } + }, + "CT_F2": { + "storm1": { + "n_points": 6552, + "land_fraction": 0.006215642086970501, + "median_inflow_deg": 14.727268148170722, + "q25_inflow_deg": 10.10705375815943, + "q75_inflow_deg": 18.248227278092536, + "median_inflow_ocean_only_deg": 14.714974202408655, + "n_ocean_points": 6517, + "friction_alone_could_own_40deg": false + }, + "storm2": { + "n_points": 8960, + "land_fraction": 0.4578373873273709, + "median_inflow_deg": 22.032197742463666, + "q25_inflow_deg": 10.51176091663094, + "q75_inflow_deg": 34.7427460800546, + "median_inflow_ocean_only_deg": 12.990653593650848, + "n_ocean_points": 4674, + "friction_alone_could_own_40deg": false + } + } +} \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py new file mode 100644 index 00000000..fd8bd31e --- /dev/null +++ b/probes/weather-p1/comet_tail_followup.py @@ -0,0 +1,395 @@ +"""EXPLORATORY follow-up to comet_tail_probe.py (db57aac0) — CT-F1/F2/F3 as +pre-registered in COMET_TAIL_REPORT.md sec.5. NOT an EV; bars mine, unaudited. + +THE QUESTION. The comet-tail probe found the wn-1 low pole left-of-motion on +2/2 storms, but with a COMMON offset of -42 deg / -40 deg from the naive +geostrophic prediction. Same magnitude, same sense, two storms => a systematic +mechanism, not noise. Three ranked candidates were named; this probe tests all +three, APPARATUS FIRST (a systematic number is a claim about the measurement +apparatus until proven otherwise -- the arc's standing rule). + +geom/find_center/decompose are COPIED VERBATIM from comet_tail_probe.py so +every number below is comparable to the committed baseline. Baseline to beat: +storm1 error -41.97 deg, storm2 error -40.21 deg. + +PRE-REGISTERED, in run order: + +CT-F3 APPARATUS (gates the other two). Recompute the low-pole bearing from 6 + center choices per storm: the baseline MSLP minimum; a Laplacian- + centroid center (grad^2 p is proportional to geostrophic vorticity, so + its centroid is a circulation-center proxy); and +/-100 km jitters + along- and across-track. A 100 km miscentering of a monopole INJECTS a + wn-1 by construction, so this measures how much of the -40 deg is a + choice I made with ~100 km of arbitrariness. + spread <= 10 deg -> ROBUST (offset is not center choice) + 10 < spread <= 20 -> SURVIVES-WITH-UNCERTAINTY + spread > 20 deg -> APPARATUS-DOMINATED: CT-E3's verdict must be + re-graded and F1/F2 are moot. + Directional sub-test: if the Laplacian-centroid center moves the error + TOWARD zero, that is positive evidence the offset was center bias. + +CT-F1 STEERING LEVEL (candidate 1: baroclinic tilt). The store ships all 13 + pressure levels in ONE chunk, so the yes/no becomes a sweep: decompose + geopotential at every level about that level's own low center, and + score each against the SAME surface-measured motion bearing. + PASS the |error|-minimising level lies in 400-850 hPa (the textbook + extratropical steering layer) AND |error| there <= 20 deg. + DEAD the error is FLAT across levels (max-min < 15 deg) -- level + structure then explains nothing -- or the minimising level sits + at 50-100 hPa (physically absurd => apparatus). + Reported twice: about each level's own center, and about the SURFACE + center, to separate "the field changes with height" from "the center + moves with height". + +CT-F2 FRICTION (candidate 2: Ekman turning). Measure the actual 10m cross- + isobar inflow angle: alpha = bearing(v10m) - bearing(v_geostrophic), + positive = CCW = turned toward the low (the friction sign in NH). + Sampled on rings 300-1000 km (excluding the core, where gradient-wind + curvature is largest, and the outer edge), |v10m| > 3 m/s. + Expect median alpha in [5, 40] deg over open ocean (textbook 10-30). + The bar that matters: if median alpha << 40 deg, friction CANNOT own + the -40 deg offset alone and is at most a contributor. If median + alpha >= 40 deg, friction alone remains sufficient and this probe + does NOT separate it from candidate 1. + Land fraction from land_sea_mask is reported so "over ocean" is a + measurement, not an assumption. + +NOT tested here (stated, not hidden): n is still 2 storms at one synoptic +time. No offset constant may be baked into any predictor on this evidence; +the report's n>=10 rule stands regardless of how these three come out. + +RUN LOG (transparency — the bars above are VERBATIM as first run, unedited): + run 1 CT-F3 and CT-F1 completed; CT-F2 crashed on a shape bug in grad_p + (dxk was (NY,1) and was being column-sliced). Fixed. Also ADDED a + DIAGNOSTIC field `center_search_saturated` to CT-F1 after run 1 + exposed that find_center can pin at exactly the 600 km search radius + — i.e. it found no co-located upper center and locked onto a + different system. That is a diagnostic, not a bar: no pass/fail + criterion above was added, removed, or loosened. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T0, T1 = 91246, 91247 +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +print("fetching MSLP t0,t1 ...", flush=True) +lat = fetch("latitude", "0").astype(np.float64).ravel() +p0 = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +p1 = fetch("mean_sea_level_pressure", f"{T1}.0.0")[0].astype(np.float64) +NY, NX = p0.shape +phi = np.deg2rad(lat) +lon_deg = np.arange(NX) * 0.25 + +# ---- verbatim from comet_tail_probe.py ------------------------------------- + + +def geom(ci, cj): + """dx,dy,r (km) and azimuth theta (rad, CCW from east) rel. to center.""" + dlon = np.deg2rad((lon_deg[None, :] - lon_deg[cj] + 180) % 360 - 180) + dphi = phi[:, None] - phi[ci] + dx = R_E * np.cos(phi[ci]) * dlon * np.ones((NY, 1)) + dy = R_E * dphi * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find_center(field, near=None, radius_km=600.0): + """Deepest zonal-anomaly low, globally NH or within radius of `near`.""" + fa = field - field.mean(axis=1, keepdims=True) + mask = lat[:, None] > 15 + if near is not None: + _, _, r, _ = geom(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + return int(ci), int(cj) + + +def decompose(field, ci, cj): + """Ring-mean + per-ring wn-1 fit; returns metrics dict.""" + dx, dy, r, th = geom(ci, cj) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + c, s = np.cos(t), np.sin(t) + a1[b] = 2 * ((v - prof[b]) * c).mean() + b1[b] = 2 * ((v - prof[b]) * s).mean() + + resid0 = vals - prof[rings] + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + resid1 = resid0 - wn1 + + var_t = vals.var() + e1 = 1.0 - resid0.var() / var_t + e2 = 1.0 - resid1.var() / var_t + wn1_frac = wn1.var() / resid0.var() + + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) + low_pole = (ph + np.pi) % (2 * np.pi) + r_mid = (np.arange(nb) + 0.5) * RING + a_corr = float(np.corrcoef(amp[1:], r_mid[1:])[0, 1]) + return {"R2_profile": float(e1), "R2_profile_wn1": float(e2), + "wn1_frac_of_resid": float(wn1_frac), + "low_pole_bearing_rad": float(low_pole), + "amp_vs_r_corr": a_corr} + +# ---- helpers new to this probe --------------------------------------------- + + +def wrap_deg(d): + return (d + 180.0) % 360.0 - 180.0 + + +def err_deg(low_pole_rad, motion_rad): + pred = (motion_rad + np.pi / 2) % (2 * np.pi) + return float(wrap_deg(np.rad2deg(low_pole_rad - pred))) + + +def offset_center(ci, cj, dx_km, dy_km): + """Grid point nearest the (dx,dy) km offset from (ci,cj).""" + dx, dy, _, _ = geom(ci, cj) + d2 = (dx - dx_km) ** 2 + (dy - dy_km) ** 2 + i, j = np.unravel_index(np.argmin(d2), d2.shape) + return int(i), int(j) + + +def lap_centroid(field, ci, cj, radius_km=300.0): + """Centroid of grad^2 p (proportional to geostrophic vorticity) near the + center — a circulation-center proxy independent of the depth minimum.""" + lap = np.zeros_like(field) + lap[1:-1, :] += field[2:, :] + field[:-2, :] - 2 * field[1:-1, :] + lap[:, 1:-1] += field[:, 2:] + field[:, :-2] - 2 * field[:, 1:-1] + dx, dy, r, _ = geom(ci, cj) + m = (r <= radius_km) & (lap > 0) + if not m.any(): + return ci, cj + w = lap[m] - 0.5 * lap[m].max() + w = np.where(w > 0, w, 0.0) + if w.sum() <= 0: + return ci, cj + return offset_center(ci, cj, + float((dx[m] * w).sum() / w.sum()), + float((dy[m] * w).sum() / w.sum())) + + +def grad_p(field): + """d p/dx, d p/dy in Pa/km on the sphere (centered differences).""" + dy_km = R_E * np.deg2rad(0.25) + gy = np.zeros_like(field) + gy[1:-1, :] = (field[2:, :] - field[:-2, :]) / (2 * dy_km) + gy = -gy # index increases southward => flip sign + gx = np.zeros_like(field) + dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) # (NY,1), broadcasts + gx[:, 1:-1] = (field[:, 2:] - field[:, :-2]) / (2 * dxk) + return gx, gy + + +def static_key(var): + """Chunk key for a variable, all-zero index of the right arity.""" + return ".".join("0" * len(meta[f"{var}/.zarray"]["chunks"])) + + +# ---- storms: reproduce the baseline track ---------------------------------- +STORMS = [] +for name, hint in (("storm1", None), ("storm2", (int(round((90 - 67.0) / 0.25)), + int(round(28.0 / 0.25))))): + ci0, cj0 = find_center(p0, near=hint) + ci1, cj1 = find_center(p1, near=(ci0, cj0)) + dx, dy, _, _ = geom(ci0, cj0) + mv = np.array([dx[ci1, cj1], dy[ci1, cj1]]) + STORMS.append({"name": name, "c0": (ci0, cj0), + "motion_rad": float(np.arctan2(mv[1], mv[0])), + "disp_km": float(np.hypot(*mv))}) + +out = {"store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK, "storms": {}} + +# ============================ CT-F3 : APPARATUS ============================= +print("\n=== CT-F3 APPARATUS (center sensitivity) — runs first, gates F1/F2 ===") +f3_all = {} +for st in STORMS: + ci0, cj0 = st["c0"] + mth = st["motion_rad"] + al = np.array([np.cos(mth), np.sin(mth)]) * 100.0 # along-track 100 km + ac = np.array([-np.sin(mth), np.cos(mth)]) * 100.0 # across-track 100 km + variants = { + "mslp_min_baseline": (ci0, cj0), + "laplacian_centroid": lap_centroid(p0, ci0, cj0), + "jitter_along_+100km": offset_center(ci0, cj0, *al), + "jitter_along_-100km": offset_center(ci0, cj0, *(-al)), + "jitter_across_+100km": offset_center(ci0, cj0, *ac), + "jitter_across_-100km": offset_center(ci0, cj0, *(-ac)), + } + rows = {} + for k, (i, j) in variants.items(): + d = decompose(p0, i, j) + rows[k] = {"lat": float(lat[i]), "lon": float(lon_deg[j]), + "error_deg": err_deg(d["low_pole_bearing_rad"], mth), + "wn1_frac": d["wn1_frac_of_resid"], + "R2_profile_wn1": d["R2_profile_wn1"]} + errs = np.array([r["error_deg"] for r in rows.values()]) + # circular spread about the baseline + rel = wrap_deg(errs - rows["mslp_min_baseline"]["error_deg"]) + spread = float(rel.max() - rel.min()) + verdict = ("ROBUST" if spread <= 10 else + "SURVIVES-WITH-UNCERTAINTY" if spread <= 20 else + "APPARATUS-DOMINATED") + base_e = rows["mslp_min_baseline"]["error_deg"] + lap_e = rows["laplacian_centroid"]["error_deg"] + toward_zero = abs(lap_e) < abs(base_e) + f3_all[st["name"]] = {"variants": rows, "spread_deg": spread, + "verdict": verdict, + "lap_center_moves_error_toward_zero": bool(toward_zero), + "baseline_error_deg": base_e, + "lap_error_deg": lap_e} + print(f"\n{st['name']}: baseline error {base_e:+.1f} deg") + for k, r in rows.items(): + print(f" {k:22s} ({r['lat']:.2f}N,{r['lon']:.2f}E) " + f"error {r['error_deg']:+7.1f} wn1_frac {r['wn1_frac']:.3f}") + print(f" spread across 6 centers = {spread:.1f} deg -> {verdict}") + print(f" Laplacian-centroid moves error toward zero: {toward_zero}") + +f3_worst = max(v["spread_deg"] for v in f3_all.values()) +f3_gate = f3_worst <= 20.0 +out["CT_F3"] = {"per_storm": f3_all, "worst_spread_deg": f3_worst, + "gate_passed": bool(f3_gate)} +print(f"\nCT-F3 GATE: worst spread {f3_worst:.1f} deg -> " + f"{'PASS (F1/F2 interpretable)' if f3_gate else 'FAIL (F1/F2 moot; CT-E3 must be re-graded)'}") + +# ========================= CT-F1 : STEERING LEVEL =========================== +print("\n=== CT-F1 STEERING LEVEL (geopotential sweep, 13 levels) ===") +levels = fetch("level", "0").astype(int).ravel() +print(f"fetching geopotential t0 (13 levels, one chunk) ...", flush=True) +z0 = fetch("geopotential", f"{T0}.0.0.0")[0].astype(np.float64) # (13,ny,nx) + +f1_all = {} +for st in STORMS: + ci0, cj0 = st["c0"] + mth = st["motion_rad"] + per_level = [] + for li, lev in enumerate(levels): + fld = z0[li] + zi, zj = find_center(fld, near=(ci0, cj0)) + d_own = decompose(fld, zi, zj) + d_sfc = decompose(fld, ci0, cj0) + _, _, r_, _ = geom(ci0, cj0) + per_level.append({ + "level_hPa": int(lev), + "center_lat": float(lat[zi]), "center_lon": float(lon_deg[zj]), + "center_offset_km": float(r_[zi, zj]), + # DIAGNOSTIC (added after run 1, not a bar): the finder pinned at + # the 600 km search radius => no co-located upper center found. + "center_search_saturated": bool(r_[zi, zj] > 580.0), + "error_own_center_deg": err_deg(d_own["low_pole_bearing_rad"], mth), + "error_sfc_center_deg": err_deg(d_sfc["low_pole_bearing_rad"], mth), + "wn1_frac": d_own["wn1_frac_of_resid"], + "R2_profile_wn1": d_own["R2_profile_wn1"], + }) + errs = np.array([p["error_own_center_deg"] for p in per_level]) + best = int(np.argmin(np.abs(errs))) + flat = float(errs.max() - errs.min()) + best_lev = int(levels[best]) + passed = (400 <= best_lev <= 850) and abs(errs[best]) <= 20.0 + dead_flat = flat < 15.0 + dead_absurd = best_lev <= 100 + f1_all[st["name"]] = { + "per_level": per_level, "best_level_hPa": best_lev, + "best_error_deg": float(errs[best]), + "sfc_error_deg": float(err_deg( + decompose(p0, ci0, cj0)["low_pole_bearing_rad"], mth)), + "error_spread_across_levels_deg": flat, + "CT_F1_pass": bool(passed), + "CT_F1_dead_flat": bool(dead_flat), + "CT_F1_dead_absurd_level": bool(dead_absurd), + } + print(f"\n{st['name']}: (surface MSLP error " + f"{f1_all[st['name']]['sfc_error_deg']:+.1f} deg)") + for p in per_level: + print(f" {p['level_hPa']:4d} hPa err(own ctr) {p['error_own_center_deg']:+7.1f} " + f"err(sfc ctr) {p['error_sfc_center_deg']:+7.1f} " + f"ctr offset {p['center_offset_km']:5.0f} km " + f"wn1 {p['wn1_frac']:.3f}") + print(f" |error| minimised at {best_lev} hPa ({errs[best]:+.1f} deg); " + f"spread across levels {flat:.1f} deg") + print(f" CT-F1 pass(400-850 hPa & |err|<=20): {passed} | " + f"dead-flat(<15 deg spread): {dead_flat} | " + f"dead-absurd(<=100 hPa): {dead_absurd}") +out["CT_F1"] = f1_all + +# ============================ CT-F2 : FRICTION ============================== +print("\n=== CT-F2 FRICTION (10m cross-isobar inflow angle) ===") +u10 = fetch("10m_u_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +v10 = fetch("10m_v_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +lsm = fetch("land_sea_mask", static_key("land_sea_mask")).astype(np.float64) +lsm = lsm.reshape(NY, NX) if lsm.size == NY * NX else lsm[0] +gx, gy = grad_p(p0) + +f2_all = {} +for st in STORMS: + ci0, cj0 = st["c0"] + _, _, r, _ = geom(ci0, cj0) + band = (r >= 300.0) & (r <= 1000.0) + spd = np.hypot(u10, v10) + m = band & (spd > 3.0) + # geostrophic direction: v_g ∝ (-dp/dy, dp/dx) + bg = np.arctan2(gx[m], -gy[m]) + ba = np.arctan2(v10[m], u10[m]) + alpha = wrap_deg(np.rad2deg(ba - bg)) # + = CCW = turned toward the low + land = float(lsm[m].mean()) + ocean = alpha[lsm[m] < 0.5] + med = float(np.median(alpha)) + f2_all[st["name"]] = { + "n_points": int(m.sum()), "land_fraction": land, + "median_inflow_deg": med, + "q25_inflow_deg": float(np.percentile(alpha, 25)), + "q75_inflow_deg": float(np.percentile(alpha, 75)), + "median_inflow_ocean_only_deg": + float(np.median(ocean)) if ocean.size else None, + "n_ocean_points": int(ocean.size), + "friction_alone_could_own_40deg": bool(abs(med) >= 40.0), + } + o = f2_all[st['name']]['median_inflow_ocean_only_deg'] + print(f"\n{st['name']}: n={int(m.sum())} pts in 300-1000 km, " + f"land fraction {land:.2f}") + print(f" inflow angle median {med:+.1f} deg " + f"(IQR {f2_all[st['name']]['q25_inflow_deg']:+.1f} .. " + f"{f2_all[st['name']]['q75_inflow_deg']:+.1f})") + print(f" ocean-only median: " + f"{('%+.1f deg' % o) if o is not None else 'n/a'} " + f"(n={f2_all[st['name']]['n_ocean_points']})") + print(f" friction alone could own the -40 deg offset: " + f"{f2_all[st['name']]['friction_alone_could_own_40deg']}") +out["CT_F2"] = f2_all + +json.dump(out, open("comet_tail_followup.json", "w"), indent=2) +print("\nwrote comet_tail_followup.json") From 432bcab2b8693a2421453bdc7891f2f54587f7cf Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 20:35:30 +0000 Subject: [PATCH 07/23] =?UTF-8?q?probe(weather):=20CT-F4=20clears=20the=20?= =?UTF-8?q?blocking=20item,=20CT-F7=20lands=20the=20land-friction=20contra?= =?UTF-8?q?st=20=E2=80=94=20and=20re-scopes=20the=20candidate=20it=20was?= =?UTF-8?q?=20testing?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CT-F4 SUB-GRID CENTER — the blocking item, CLEARED. F3 condemned the -40 deg offset using a +/-100 km jitter I CHOSE, not one I measured. F4 asks the non-circular question: how far apart do INDEPENDENT center definitions land? Four definitions across three physical fields (sub-grid MSLP min; grad^2 p centroid; 10m vorticity centroid; sub-grid z850 min) agree to 20.3 km (storm1) / 73.0 km (storm2), and the errors they give span 2.3 / 6.5 deg. Apparatus noise is ~+/-3-7 deg, not +/-15-30 => the offset IS measurable. F3 was not wrong; its amplitude was unjustified. Storm2 PASSES F4a (6.5 <= 10) with the anti-vacuity guard satisfied (73 km = 2.4x the grid diagonal). Storm1 is NO-VERDICT by my own pre-registered F4c guard — its centers agree to 20.3 km, BELOW the 31.9 km grid diagonal, so a 2.3 deg spread proves nothing about method sensitivity; the guard refused the free pass and storm1 is bounded <= 6.5 deg via the F4b curve instead. F4b sensitivity curve monotone on both (storm1 25/50/100/200 km -> 6.5/13.8/30.9/77.2 deg; storm2 -> 5.0/9.8/19.3/35.8). Real center variants cluster along a preferred axis, so isotropic jitter is an UPPER bound — the second reason F3 over-stated the problem. CT-F7 FRICTION OVER LAND (operator-requested) — both bars pass. Storm selected blind to its inflow: deepest NH low with >=70% land in its 300-1000 km ring -> 28.50N 67.50E (80% land, -2609 Pa). Orography guard dropped 826/2642 land points above 1000 m (MSLP over high terrain is an extrapolated fiction whose gradient would corrupt the geostrophic reference). F7b, the paired half that controls for depth/latitude/curvature: +34.2 deg land vs +20.5 deg ocean INSIDE THE SAME DISK. F7a also passes but is latitude-confounded. F7d False (34.2 < 35), so friction turning is ~2.4x larger over land and the bound is surface-type dependent, as pre-warned. Caveat stated: blind selection was blind to storm TYPE too — at 28.5N in mid-June this is a monsoon thermal low, a valid test of the inflow apparatus over land and a weaker dynamical analogue of storms 1-2. SELF-CORRECTION, worth more than either pass: F2/F7 bound the rotation of the WIND relative to the isobars, but the CT-E3 offset is a rotation of the PRESSURE DIPOLE. Friction does not rotate the pressure field except at second order, so candidate 2 was partly mis-specified from the start. An unplanned pairing already sitting in the F2 output points the wrong way for it too: storm1 is 1% land (+14.7 inflow), storm2 is 46% land (+22.0), yet offsets are -42.0 and -40.2 — the MORE frictional storm has the SMALLER offset. [S], n=2, undesigned, recorded as suggestive. CT-F9 written to test the mechanism candidate 2 should have been about (Ekman pumping vs land-fraction ASYMMETRY across the disk, not mean land fraction). Candidates now: tilt LEADING and near-unopposed; center bias BOUNDED at ~+/-5 deg (~1/8 of the offset); friction RE-SCOPED. Binding constraint is n >= 10 storms, not the apparatus. Refactor guard: geom_ll is a strict generalisation of geom to a continuous center; the identity geom_ll(lat[ci],lon[cj]) == geom(ci,cj) is asserted numerically at max|diff| = 0.0 so the baseline cannot silently move. Board: EPIPHANIES prepend E-A-JITTER-AMPLITUDE-YOU-CHOSE-IS-NOT-AN-UNCERTAINTY-YOU-MEASURED-1. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 48 +++ probes/weather-p1/COMET_TAIL_REPORT.md | 201 ++++++++++-- probes/weather-p1/comet_tail_f4_f7.json | 143 ++++++++ probes/weather-p1/comet_tail_f4_f7.py | 415 ++++++++++++++++++++++++ 4 files changed, 771 insertions(+), 36 deletions(-) create mode 100644 probes/weather-p1/comet_tail_f4_f7.json create mode 100644 probes/weather-p1/comet_tail_f4_f7.py diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index ed539892..d5c41c8a 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,51 @@ +## 2026-08-11 — E-A-JITTER-AMPLITUDE-YOU-CHOSE-IS-NOT-AN-UNCERTAINTY-YOU-MEASURED-1 + +**Status:** FINDING `[G]` — CT-F4 + CT-F7, `comet_tail_f4_f7.py` / `.json`, +report §5.6–5.7. Amends E-THE-OFFSET-WAS-THE-APPARATUS-... below, which stands +as written (it was correct for the amplitude it tested). + +**My apparatus test condemned a number using a sensitivity amplitude I picked +out of the air.** CT-F3 jittered the storm center by ±100 km and found the +alignment error moved 29.4° — so the −40° offset was declared unmeasurable. +But ±100 km was never *measured*; it was a plausible-sounding round number. +The non-circular question is **how far apart independent center definitions +actually land**, and that is the uncertainty. Four definitions across three +physical fields (sub-grid MSLP min; ∇²p centroid; **10m vorticity** centroid; +sub-grid z850 min) agree to **20 km** / **73 km**, and the answers they give +span **2.3°** / **6.5°** — so the real apparatus noise is ≈ ±3–7° and the +offset **is** measurable. F3 was not wrong; its amplitude was unjustified. + +**Two structural lessons, both reusable:** +1. *Prefer a measured disagreement to a chosen perturbation.* Independent + method variants are a free, non-arbitrary uncertainty estimate — and where + both exist, the isotropic jitter was a 2× **over**estimate, because real + variants cluster along a preferred axis rather than scattering evenly. +2. *An anti-vacuity guard must be allowed to refuse a PASS you want.* Storm 1 + scored a 2.3° spread — but its four centers agreed to 20 km, below the + 31.9 km grid diagonal, so the pre-registered CT-F4c guard returned + NO-VERDICT rather than banking a free pass. Storm 2, whose definitions + genuinely disagreed (73 km = 2.4× the diagonal), is the one that carries + the result. The guard cost me the tidier of the two numbers, which is + what tells me it was real. + +**Also, the friction candidate was partly MIS-SPECIFIED and the data said so +before I did.** CT-F7 measured cross-isobar inflow over land (blind storm +selection): **+34.2° land vs +20.5° ocean inside the same disk** — a paired +contrast that controls for depth/latitude/curvature, textbook magnitudes, both +bars passed. But F2/F7 bound the rotation of the **wind** relative to the +isobars, whereas the CT-E3 offset is a rotation of the **pressure dipole**; +friction does not rotate the pressure field except at second order. And an +unplanned pairing already in the F2 output points the wrong way for it: storm 1 +is 1 % land (+14.7° inflow), storm 2 is 46 % land (+22.0°), yet their offsets +are −42.0° and −40.2° — **the more frictional storm has the smaller offset.** +`[S]`, n=2, undesigned, recorded as suggestive. + +Net: candidate 1 (baroclinic tilt) leading and near-unopposed; candidate 3 +(center bias) bounded at ≈±5°; candidate 2 re-scoped, with CT-F9 written to +test the mechanism it *should* have been about (Ekman pumping vs land-fraction +**asymmetry** across the disk, not mean land fraction). Binding constraint is +now n ≥ 10 storms, not the apparatus. + ## 2026-08-11 — E-THE-OFFSET-WAS-THE-APPARATUS-THE-LADDER-WAS-THE-PHYSICS-1 **Status:** FINDING `[G]` — CT-F1/F2/F3 run, `comet_tail_followup.py` / `.json`, diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index c0163d1d..9e2c4afb 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -110,6 +110,16 @@ physics `[H at n=2]`. > unresolved at this centering precision. The *height dependence* of the error > (§5.2) is a separate and much larger signal and is not affected by this > re-grade. +> +> > **⚠⚠ RE-GRADE AMENDED 2026-08-11 by CT-F4 (§5.6).** The note above is +> > kept verbatim because it was correct *for the amplitude it tested* — but +> > ±100 km was an amplitude I **chose**, not one I measured. Four +> > independent center definitions (three different physical fields) turn out +> > to agree to **20 km** on storm 1 and **73 km** on storm 2, and the +> > alignment error they produce spans only **2.3° / 6.5°**. So the operative +> > apparatus uncertainty is **≈ ±3–7°, not ±15°**, and the −42°/−40° offset +> > **is** measurable above it. F3 was not wrong; its jitter amplitude was +> > unjustified — which is exactly what F4 was pre-registered to find out. **What this resolves:** the earlier sunflower-lattice probe failed its axisymmetry bar (E1: 0.639 < 0.70) — the missing third of the storm was @@ -230,34 +240,149 @@ ocean-only median (+13.0°) — friction turning is stronger over land, exactly as textbook. The measurement therefore discriminates a known physical contrast in the right direction, which is evidence it is measuring what it claims. +### 5.6 CT-F4 — sub-grid center — **the blocking item CLEARS** (candidate 3 mostly retired) + +`comet_tail_f4_f7.py` / `.json`. F3 showed sensitivity at an amplitude I chose. +F4 asks the non-circular question instead: **how far apart do independent +center definitions actually land?** That disagreement *is* the center +uncertainty. Four definitions, deliberately not variants of one idea — A: +sub-grid MSLP minimum (2D quadratic on the 3×3); B: ∇²p centroid (pressure +curvature); C: 10m relative-vorticity centroid (**wind** field); D: 850 hPa +geopotential minimum, sub-grid (different field *and* altitude). + +| | A (MSLP) | B (∇²p) | C (10m ζ) | D (z850) | max separation | error spread | +|---|---|---|---|---|---|---| +| storm 1 | −42.5° | −41.0° | −41.8° | −43.4° | **20.3 km** | **2.3°** | +| storm 2 | −39.8° | −38.6° | −35.1° | −41.6° | **73.0 km** | **6.5°** | + +- **Storm 2 PASSES CT-F4a** cleanly (6.5° ≤ 10° bar) with the anti-vacuity + guard satisfied — its four definitions genuinely disagree (73 km = 2.4× the + 29.9 km grid diagonal), so the test had real room to fail and did not. +- **Storm 1 is NO-VERDICT on F4a by my own CT-F4c guard**: its four definitions + agree to 20.3 km, *below* the 31.9 km grid diagonal. A 2.3° spread among + centers that coincide proves nothing about method sensitivity, so the guard + correctly refuses the free pass. Storm 1's bound comes instead from F4b + (below), and is labelled as derived, not as an F4a result. +- **CT-F4b sensitivity curve** (spread in ° vs jitter amplitude in km), + monotone on both as pre-registered: + + | | 25 km | 50 km | 100 km | 200 km | + |---|---|---|---|---| + | storm 1 | 6.5° | 13.8° | 30.9° | 77.2° | + | storm 2 | 5.0° | 9.8° | 19.3° | 35.8° | + + Read against the *measured* uncertainties: storm 1's 20 km sits below the + 25 km point → **≤ 6.5°**; storm 2's 73 km would predict ~14° isotropically, + yet the direct F4a measurement is 6.5°. The gap is informative: real center + definitions do **not** scatter isotropically — they cluster along a preferred + axis, so the four-direction jitter is an **upper bound**, not an estimate. + That is the second reason F3's number over-stated the problem. `[H]` + +**Consequence:** the offset is measurable with an error bar of roughly ±3–7°. +CT-F4 was the blocking item ahead of any constant-fitting; it is cleared for +storm 2 and bounded for storm 1. Candidate 3 (center-finder bias) drops from +"live and not excluded" to **bounded at ≈±5°, i.e. ~⅛ of the offset**. `[G]` + +*One observation worth a follow-up, n=1:* on storm 2 the **wind-based** center +(C) sits ~65 km north of the pressure minimum and moves the error the furthest +toward zero (−35.1° vs −39.8°). Whether the circulation center is +systematically the better reference for this test is untested. + +### 5.7 CT-F7 — friction over LAND — **both bars pass, and it re-scopes candidate 2** + +Operator-requested replication over land. Storm selected **blind to its +inflow**: deepest NH zonal-anomaly low whose 300–1000 km ring is ≥ 70 % land. +Selection returned **28.50N 67.50E** (ring 80 % land, anomaly −2609 Pa). +Orography guard dropped 826 of 2642 land points (31 %) above 1000 m, since +MSLP over high terrain is an extrapolated fiction whose gradient would corrupt +the geostrophic reference. + +| | n | median α | verdict | +|---|---|---|---| +| **land** points (oro-guarded) | 1816 | **+34.2°** (IQR reported in JSON) | | +| **ocean** points, *same disk* | 743 | **+20.5°** | | +| CT-F2 reference (storm 1, 99 % ocean) | 6517 | +14.7° | | + +- **F7a PASSES** (34.2° ≥ 14.7+8, inside [20,50]) — but this half is + confounded by latitude: at 28.5N, *f* is roughly half its 56N value, so + ageostrophic effects are inherently larger. Its own storm's ocean points + (+20.5°) already run above storm 1's (+14.7°) for that reason. +- **F7b PASSES** and is the number to trust: **+34.2° land vs +20.5° ocean + inside the same disk**, a +13.7° paired contrast that controls for depth, + latitude and curvature by construction. Both classes clear the n ≥ 500 bar, + so the half is genuinely evaluable rather than NO-VERDICT. +- **F7d is False** — 34.2° < the 35° threshold, though not by much. Over + *land* friction turning is roughly 2.4× its ocean value, so the friction + bound is **surface-type dependent and must not be applied globally**, exactly + as pre-warned. + +Two honest caveats. **(i)** The blind selection was blind to storm *type* as +well as to the answer: at 28.5N in mid-June this is a monsoon-season thermal +low, not an extratropical cyclone. It is a legitimate test of the *inflow +apparatus* over land, and a weaker analogue of storms 1–2 dynamically. +**(ii)** The apparatus passes its own can-it-fire test twice now — land/ocean +within one storm here, and land/ocean within storm 2 in §5.3 — in the same +direction both times. + +> **Self-correction, and it matters more than the pass.** F2/F7 measure the +> **wind** turning relative to the isobars. The CT-E3 offset is a rotation of +> the **pressure dipole** relative to the motion. Friction rotates the surface +> wind *within* a given pressure field; it does not rotate the pressure field +> itself, except at second order through Ekman-pumping feedback. So candidate 2 +> as originally written in §5 was **partly mis-specified** — F2/F7 bound a +> mechanism that was never the leading route to a pressure-dipole rotation. +> `[H]` + +> **An unplanned paired natural experiment, post-hoc but clean.** Storm 1 is +> **1 % land** with +14.7° inflow; storm 2 is **46 % land** with +22.0° inflow +> — a real 7.3° difference in actual surface friction. Their offsets are +> **−42.0° and −40.2°**, within 1.8° of each other, and the *more* frictional +> storm has the *smaller* offset. If friction drove the offset the two should +> separate substantially; they do not. This was not designed — it fell out of +> the land fractions already in the CT-F2 output — and it is n=2, so it is +> recorded as suggestive, not decisive. `[S]` + ### 5.4 Where that leaves the three candidates -| candidate | status after F1–F3 | -|---|---| -| 1 — steering-level / baroclinic tilt | **Favoured** `[H]`. Monotone 92–102° height ladder crossing zero in the mid-troposphere on both storms, 3–5× the apparatus noise. | -| 2 — Ekman surface friction | **Bounded contributor** `[G]` on magnitude. 13–15° over ocean, right sign, ~⅓ of the offset at most. | -| 3 — center-finder bias | **LIVE and not excluded** `[G]`. ±100 km centering moves the answer up to 29.4° — enough to dominate the offset magnitude by itself. | - -Candidates 1–3 are **not mutually exclusive**, and the measured numbers are -roughly additive in the right direction (tilt ≈ most of it, friction ≈ 13–15°, -centering ≈ ±15° of slop). No attribution is claimed beyond that. - -### 5.5 Next falsifiers (NOT run) - -- **CT-F4 (apparatus, first):** sub-grid center fit — parabolic interpolation - of the pressure minimum plus a circulation-centroid at the level where the - error crosses zero — then re-run F3's jitter test. *Bar:* spread ≤ 10°. - Until this passes, no offset constant exists to be fitted. -- **CT-F5:** widen F1's center search per level (or track the upper center - along the tilt axis) so the saturation defect in §5.2 cannot recur; re-run - storm 2's own-center path for a real verdict. -- **CT-F6:** the crossing level itself as the observable — *prediction:* it - correlates with the deep-layer mean steering level, i.e. deeper/more mature - systems cross higher. Needs n ≥ 10. +*(Updated after F4/F7 — the F1–F3 column is kept so the movement is visible.)* + +| candidate | after F1–F3 | after F4 + F7 | +|---|---|---| +| 1 — steering-level / baroclinic tilt | Favoured | **Leading, and now near-unopposed** `[H]`. Unchanged evidence (92–102° monotone height ladder crossing zero mid-troposphere on both storms), but its two rivals have shrunk. | +| 2 — Ekman surface friction | Bounded contributor | **Re-scoped and demoted** `[H]`. The bound tightened (ocean 13–20°, land 34°) but the mechanism was partly mis-specified: friction rotates the *wind*, not the *pressure dipole*. The unplanned 1 %-vs-46 %-land pairing (§5.7) points the wrong way for it. | +| 3 — center-finder bias | LIVE, not excluded | **Bounded at ≈±5°** `[G]`, ~⅛ of the offset. Independent center definitions agree to 20 / 73 km and the error spans 2.3° / 6.5°. | + +Candidates remain **not mutually exclusive**. The residual budget is now +roughly: tilt ≈ most of the 40°, centering ≈ ±5°, friction ≈ second-order on +this quantity. No stronger attribution is claimed at n = 2. + +### 5.5 Next falsifiers + +- ~~**CT-F4** (sub-grid center, was blocking)~~ — **RUN, §5.6.** Storm 2 + PASSES; storm 1 NO-VERDICT by the anti-vacuity guard, bounded ≤ 6.5° via the + F4b curve. The blocking item is cleared. +- ~~**CT-F7** (friction over land)~~ — **RUN, §5.7.** Both bars pass; the + candidate it tests is re-scoped rather than confirmed. +- **CT-F5 (still open):** widen F1's per-level center search (or track the + upper center along the tilt axis) so the 600 km saturation defect in §5.2 + cannot recur; re-run storm 2's own-center path for a real verdict. +- **CT-F6 (still open):** the crossing level itself as the observable — + *prediction:* it tracks the deep-layer mean steering level, i.e. deeper / + more mature systems cross higher. Needs n ≥ 10. +- **CT-F8 (new, from §5.6):** is the **wind-based** circulation center + systematically the better reference? On storm 2 it sat 65 km north of the + pressure minimum and moved the error furthest toward zero. *Bar:* across + n ≥ 10 storms, |error| at the ζ-centroid < |error| at the MSLP minimum. +- **CT-F9 (new, from §5.7):** does asymmetric friction rotate the **pressure** + dipole via Ekman pumping — the mechanism candidate 2 should have been about? + *Test:* correlate the dipole bearing residual against the land-fraction + *asymmetry* across the disk (not the mean). *Prediction under candidate 2:* + storms with a strong land/ocean split across the vortex show a larger + residual than uniform-surface storms. - **Sample size, unchanged:** n ≥ 10 storms across seasons and basins before - any offset constant is baked into a predictor. 2/2 at p = 0.0625 justified - the follow-up; the follow-up now justifies fixing the apparatus, not - shipping a constant. + any offset constant is baked into a predictor. The apparatus is now good + enough to fit one — which makes the sample size, not the centering, the + binding constraint. --- @@ -306,17 +431,21 @@ storm ≈ CENTER (place) — 1 address 3. ~~CT-F3 first (apparatus before mechanism), then CT-F1/CT-F2~~ — DONE. **F3 FAILED its gate**; CT-E3 re-graded in §4; F1 strongly favours the steering-level mechanism; F2 bounds friction to ~⅓ of the offset. -4. **CT-F4 (sub-grid center) is now the blocking item** — the apparatus must - pass before any offset constant can be fitted, and therefore before the - single-frame motion predictor is worth running at all. -5. CT-F5 / CT-F6, then n ≥ 10 storm sample. +4. ~~**CT-F4 (sub-grid center) is the blocking item**~~ — **RUN and CLEARED** + (§5.6). Apparatus uncertainty ≈ ±3–7°; an offset constant is now fittable. + **CT-F7** (land friction, §5.7) run in parallel: both bars pass, candidate 2 + re-scoped. +5. **n ≥ 10 storm sample is now the binding constraint** — the apparatus no + longer is. CT-F5 / CT-F6 / CT-F8 / CT-F9 alongside it. 6. Adversarial audit gate (plan §8) before any of it is promoted to EV / product claim. -**Net effect of the follow-up on the headline claim.** The §1 summary is -unchanged in substance — wn-1 dominance (0.92/0.89), the R² lift to -0.97/0.93, and left-of-motion on 2/2 all stand untouched, since none of them -depends on the offset. What the follow-up removed is a number I would -otherwise have been tempted to ship: the "−40° constant" is **not -measurable at this centering precision**, and the encoding/predictor work in -§6 is gated behind fixing that, not behind more storms. +**Net effect of the follow-ups on the headline claim.** The §1 summary is +unchanged in substance — wn-1 dominance (0.92/0.89), the R² lift to 0.97/0.93, +and left-of-motion on 2/2 all stand untouched, since none of them depends on +the offset. The offset itself went **dead (F3) → alive with a ±3–7° error bar +(F4)**, and that round-trip is the point: the constant is now defensible +*because* it survived a gate that had already killed it once, at a centering +precision that was measured rather than assumed. What remains ungated is +sample size, not apparatus — so §6's encoding and single-frame-predictor work +is unblocked in principle and still owes n ≥ 10 before any constant ships. diff --git a/probes/weather-p1/comet_tail_f4_f7.json b/probes/weather-p1/comet_tail_f4_f7.json new file mode 100644 index 00000000..e4d96a4c --- /dev/null +++ b/probes/weather-p1/comet_tail_f4_f7.json @@ -0,0 +1,143 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "t0": 91246, + "t1": 91247, + "R_disk_km": 1200.0, + "CT_F4": { + "storm1": { + "definitions": { + "A_mslp_min_subgrid": { + "lat": 55.80687987858285, + "lon": 334.4939777023923, + "error_deg": -42.5420559346087, + "wn1_frac": 0.9202092475432342 + }, + "B_lap_p_centroid": { + "lat": 55.67248760175915, + "lon": 334.5258307823925, + "error_deg": -41.03082259793251, + "wn1_frac": 0.9274214198031719 + }, + "C_vort10m_centroid": { + "lat": 55.74488562168902, + "lon": 334.50400941714094, + "error_deg": -41.84890947295523, + "wn1_frac": 0.9247416946213036 + }, + "D_z850_min_subgrid": { + "lat": 55.84988514619299, + "lon": 334.45235986651124, + "error_deg": -43.358858835550905, + "wn1_frac": 0.9190636009723636 + } + }, + "pairwise_km": { + "A_mslp_min_subgrid|B_lap_p_centroid": 15.076176192114817, + "A_mslp_min_subgrid|C_vort10m_centroid": 6.921937117137708, + "A_mslp_min_subgrid|D_z850_min_subgrid": 5.442731637081937, + "B_lap_p_centroid|C_vort10m_centroid": 8.165539470959404, + "B_lap_p_centroid|D_z850_min_subgrid": 20.254183727029453, + "C_vort10m_centroid|D_z850_min_subgrid": 12.1135289910227 + }, + "max_separation_km": 20.254183727029453, + "grid_diagonal_km": 31.923560193171888, + "F4c_degenerate": true, + "F4a_spread_deg": 2.3280362376183916, + "F4a_pass": false, + "F4b_curve_deg": { + "25": 6.498108774570255, + "50": 13.815323316109641, + "100": 30.92174366688357, + "200": 77.1715527314677 + }, + "F4b_monotone": true, + "verdict": "NO-VERDICT (F4c degenerate: definitions coincide)" + }, + "storm2": { + "definitions": { + "A_mslp_min_subgrid": { + "lat": 67.06990712939054, + "lon": 28.07950007516945, + "error_deg": -39.76952133194351, + "wn1_frac": 0.8923855403580097 + }, + "B_lap_p_centroid": { + "lat": 67.33249409332545, + "lon": 28.227820437093918, + "error_deg": -38.55815599153158, + "wn1_frac": 0.8854887175188012 + }, + "C_vort10m_centroid": { + "lat": 67.6571433498891, + "lon": 28.839775385651926, + "error_deg": -35.08918494397858, + "wn1_frac": 0.8696967018113038 + }, + "D_z850_min_subgrid": { + "lat": 67.25, + "lon": 27.617800693082515, + "error_deg": -41.634684257149104, + "wn1_frac": 0.8949910410039148 + } + }, + "pairwise_km": { + "A_mslp_min_subgrid|B_lap_p_centroid": 29.8895451740939, + "A_mslp_min_subgrid|C_vort10m_centroid": 72.95532070228214, + "A_mslp_min_subgrid|D_z850_min_subgrid": 28.251126528244853, + "B_lap_p_centroid|C_vort10m_centroid": 44.51460985013863, + "B_lap_p_centroid|D_z850_min_subgrid": 27.746146990061987, + "C_vort10m_centroid|D_z850_min_subgrid": 69.02145235651011 + }, + "max_separation_km": 72.95532070228214, + "grid_diagonal_km": 29.865768531992796, + "F4c_degenerate": false, + "F4a_spread_deg": 6.545499313170524, + "F4a_pass": true, + "F4b_curve_deg": { + "25": 4.950760999825064, + "50": 9.843879800240217, + "100": 19.277048241942765, + "200": 35.84663238862359 + }, + "F4b_monotone": true, + "verdict": "PASS \u2014 center choice does not dominate" + } + }, + "CT_F7": { + "center": { + "lat": 28.5, + "lon": 67.5 + }, + "land_fraction_ring": 0.8026728020331112, + "candidates_scanned": [ + { + "lat": 55.75, + "lon": 334.5, + "anomaly_Pa": -2954.4862955729186, + "land_fraction": 0.006198612930567842 + }, + { + "lat": 28.5, + "lon": 67.5, + "anomaly_Pa": -2608.9383192274254, + "land_fraction": 0.8026728020331112 + } + ], + "n_land_unguarded": 2642, + "n_ocean_unguarded": 743, + "n_land_oro_guarded": 1816, + "n_ocean_oro_guarded": 743, + "median_land_unguarded_deg": 43.073134580981545, + "median_land_oro_guarded_deg": 34.19596200888765, + "median_ocean_oro_guarded_deg": 20.527235763376723, + "iqr_land_deg": [ + -11.555514115716903, + 73.97162827559512 + ], + "ocean_reference_deg": 14.7, + "F7a_across_storm_pass": true, + "F7b_within_storm_paired_pass": true, + "F7b_evaluable": true, + "F7d_friction_alone_could_own_40deg_over_land": false + } +} \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py new file mode 100644 index 00000000..43a1f916 --- /dev/null +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -0,0 +1,415 @@ +"""EXPLORATORY — CT-F4 (sub-grid center, the blocking item) + CT-F7 (friction +over LAND, operator-requested replication). Follow-up to comet_tail_followup.py +(f6310b0e). NOT an EV; bars mine, unaudited. + +WHY F4 IS SHAPED THIS WAY. CT-F3 failed its gate: a +/-100 km center jitter +moved the alignment error by up to 29.4 deg. But +/-100 km was an amplitude I +CHOSE, not one I measured — so "the apparatus dominates" was demonstrated at an +arbitrary scale. Re-running the same jitter at a smaller amplitude would be +goalpost-moving. The non-circular question is: + + how far apart do INDEPENDENT center definitions actually land? + +That disagreement IS the center uncertainty. So F4's primary test needs no +jitter at all: compute the low-pole error from four center definitions built +from three different physical fields, and look at the spread of the ANSWER. +The jitter sweep is retained only as a supporting sensitivity CURVE (a knob +sweep, reported at four amplitudes, not a single chosen point). + +Four center definitions (deliberately not variations of one idea): + A sub-grid MSLP minimum — 2D quadratic fit on the 3x3, removes grid snap + (0.25 deg = 15.6 km zonal at 56N, 27.8 km merid.) + B grad^2 p centroid — pressure curvature, i.e. geostrophic vorticity + C 10m relative-vorticity centroid — WIND field, independent of pressure + D 850 hPa geopotential minimum, sub-grid — a different field AND a + different altitude + +PRE-REGISTERED: + +CT-F4a PRIMARY. Spread of the alignment error across definitions A-D <= 10 deg + => center choice does not dominate, and an offset becomes measurable. + > 10 deg => the offset remains unmeasurable and no constant may be fitted; + CT-E3's magnitude re-grade stands permanently, not provisionally. +CT-F4b SUPPORTING. Sensitivity curve: spread of the error over 4-direction + jitters at amplitudes 25 / 50 / 100 / 200 km. Expect MONOTONE increase; + non-monotone => the apparatus is worse than F3 already showed. +CT-F4c ANTI-VACUITY GUARD (checked BEFORE F4a is read). The four definitions + must actually DISAGREE: max pairwise separation >= one grid diagonal + (~32 km at 56N). If all four collapse to the same point, F4a passes + trivially and proves nothing about method sensitivity -> NO-VERDICT. + (This arc has shipped vacuous falsifiers repeatedly; this is the guard.) + +CT-F7 FRICTION OVER LAND (operator: "Bodenreibung bei einem anderen Sturm auf + dem Land"). Textbook: cross-isobar inflow ~10-30 deg over ocean, + ~25-45 deg over land (roughness). CT-F2 measured +14.7 / +13.0 deg over + ocean. If the apparatus is real it must resolve the land contrast. + Storm selection is BLIND to the answer: deepest NH zonal-anomaly low + whose 300-1000 km ring is >= 70% land, no inspection of its inflow. + F7a ACROSS-STORM: median alpha(land storm, land pts) >= median alpha(CT-F2 + ocean storm) + 8 deg, and lands in [20, 50] deg. + F7b WITHIN-STORM PAIRED (the stronger half — controls for depth, latitude, + curvature, which differ between storms): inside the SAME disk, + median alpha(land pts) > median alpha(ocean pts). Needs >= 500 points + of each class, else NO-VERDICT on this half. + F7c OROGRAPHY GUARD. MSLP over high terrain is an extrapolated fiction and + its gradient is unreliable, which would corrupt alpha through the + geostrophic reference rather than through friction. Points with surface + elevation > 1000 m are excluded; medians are reported BOTH ways so the + guard's effect is visible rather than assumed. (Storms 1-2 were ocean, + so CT-F2 is unaffected either way.) + F7d CONSEQUENCE, stated in advance so it cannot be spun afterwards: if the + land median reaches ~40 deg, then over land friction ALONE could own an + offset of that size — which would NOT overturn the storm-1/2 verdict + (both ~99% and ocean-only) but WOULD mean the friction bound is + surface-type dependent and cannot be applied globally. + +Same store / timestep as the whole arc. geom_ll is a strict generalisation of +comet_tail_probe.py's geom to a continuous center; the identity +geom_ll(lat[ci], lon[cj]) == geom(ci, cj) is ASSERTED numerically below so the +refactor cannot silently change the baseline. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T0, T1 = 91246, 91247 +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 +G0 = 9.80665 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +def static_key(var): + return ".".join("0" * len(meta[f"{var}/.zarray"]["chunks"])) + + +print("fetching MSLP, winds, masks ...", flush=True) +lat = fetch("latitude", "0").astype(np.float64).ravel() +p0 = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +p1 = fetch("mean_sea_level_pressure", f"{T1}.0.0")[0].astype(np.float64) +NY, NX = p0.shape +phi = np.deg2rad(lat) +lon_deg = np.arange(NX) * 0.25 +u10 = fetch("10m_u_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +v10 = fetch("10m_v_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +lsm = fetch("land_sea_mask", static_key("land_sea_mask")).astype(np.float64) +lsm = lsm.reshape(NY, NX) if lsm.size == NY * NX else lsm[0] +zs = fetch("geopotential_at_surface", + static_key("geopotential_at_surface")).astype(np.float64) +zs = zs.reshape(NY, NX) if zs.size == NY * NX else zs[0] +elev_m = zs / G0 + +# -------------------------------------------------------------------------- + + +def geom_ll(latc, lonc): + """Continuous-center generalisation of comet_tail_probe.py's geom().""" + phic = np.deg2rad(latc) + dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) + dphi = phi[:, None] - phic + dx = R_E * np.cos(phic) * dlon * np.ones((NY, 1)) + dy = R_E * dphi * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def geom(ci, cj): + return geom_ll(lat[ci], lon_deg[cj]) + + +def find_center(field, near=None, radius_km=600.0): + fa = field - field.mean(axis=1, keepdims=True) + mask = lat[:, None] > 15 + if near is not None: + _, _, r, _ = geom_ll(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + return int(ci), int(cj) + + +def decompose_ll(field, latc, lonc): + """Verbatim decompose(), continuous center.""" + _, _, r, th = geom_ll(latc, lonc) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + a1[b] = 2 * ((v - prof[b]) * np.cos(t)).mean() + b1[b] = 2 * ((v - prof[b]) * np.sin(t)).mean() + resid0 = vals - prof[rings] + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) + return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), + "wn1_frac": float(wn1.var() / resid0.var()), + "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + + +def wrap_deg(d): + return (d + 180.0) % 360.0 - 180.0 + + +def err_deg(low_pole_rad, motion_rad): + return float(wrap_deg(np.rad2deg( + low_pole_rad - (motion_rad + np.pi / 2)))) + + +def subgrid_min(field, ci, cj): + """2D quadratic LS fit on the 3x3; stationary point -> (lat, lon).""" + z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") + A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), + gx.ravel() ** 2, gy.ravel() ** 2, + (gx * gy).ravel()]) + c = np.linalg.lstsq(A, z, rcond=None)[0] + _, b, cc, d, e, g = c + H = np.array([[2 * d, g], [g, 2 * e]]) + try: + dj, di = np.linalg.solve(H, [-b, -cc]) + except np.linalg.LinAlgError: + dj = di = 0.0 + di, dj = float(np.clip(di, -1, 1)), float(np.clip(dj, -1, 1)) + return (lat[ci] + di * (lat[ci + 1] - lat[ci]), + lon_deg[cj] + dj * 0.25) + + +def centroid_ll(weight, ci, cj, radius_km=300.0): + """Half-max-weighted centroid of `weight` near (ci,cj) -> (lat, lon).""" + dx, dy, r, _ = geom(ci, cj) + m = (r <= radius_km) & (weight > 0) + if not m.any(): + return lat[ci], lon_deg[cj] + w = np.clip(weight[m] - 0.5 * weight[m].max(), 0, None) + if w.sum() <= 0: + return lat[ci], lon_deg[cj] + cx = float((dx[m] * w).sum() / w.sum()) + cy = float((dy[m] * w).sum() / w.sum()) + latc = lat[ci] + np.rad2deg(cy / R_E) + lonc = lon_deg[cj] + np.rad2deg(cx / (R_E * np.cos(phi[ci]))) + return latc, lonc + + +def d_dx(f): + dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) + o = np.zeros_like(f) + o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) + return o + + +def d_dy(f): + dyk = R_E * np.deg2rad(0.25) + o = np.zeros_like(f) + o[1:-1, :] = -(f[2:, :] - f[:-2, :]) / (2 * dyk) # index grows southward + return o + + +def sep_km(a, b): + la, lo = a + lb, lob = b + dlon = np.deg2rad((lo - lob + 180) % 360 - 180) + return float(np.hypot(R_E * np.cos(np.deg2rad((la + lb) / 2)) * dlon, + R_E * np.deg2rad(la - lb))) + + +# --- refactor guard: geom_ll must reproduce geom exactly ------------------- +_a = geom_ll(lat[200], lon_deg[500])[2] +_b = geom(200, 500)[2] +assert np.abs(_a - _b).max() == 0.0, "geom_ll is NOT identical to geom" +print(f"geom_ll identity guard: max|diff| = {np.abs(_a - _b).max():.1e} OK") + +lap_p = np.zeros_like(p0) +lap_p[1:-1, :] += p0[2:, :] + p0[:-2, :] - 2 * p0[1:-1, :] +lap_p[:, 1:-1] += p0[:, 2:] + p0[:, :-2] - 2 * p0[:, 1:-1] +zeta10 = d_dx(v10) - d_dy(u10) +gx, gy = d_dx(p0), d_dy(p0) + +print("fetching geopotential t0 (13 levels) ...", flush=True) +levels = fetch("level", "0").astype(int).ravel() +z0 = fetch("geopotential", f"{T0}.0.0.0")[0].astype(np.float64) +z850 = z0[int(np.where(levels == 850)[0][0])] + +out = {"store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK} + +# ===================== CT-F4 : SUB-GRID CENTER ============================= +print("\n=== CT-F4 independent center definitions (the blocking item) ===") +STORMS = [] +for nm, hint in (("storm1", None), ("storm2", (67.0, 28.0))): + ci0, cj0 = find_center(p0, near=hint) + ci1, cj1 = find_center(p1, near=(lat[ci0], lon_deg[cj0])) + dx, dy, _, _ = geom(ci0, cj0) + mv = (float(dx[ci1, cj1]), float(dy[ci1, cj1])) + STORMS.append({"name": nm, "ij": (ci0, cj0), + "motion_rad": float(np.arctan2(mv[1], mv[0]))}) + +f4 = {} +for st in STORMS: + ci0, cj0 = st["ij"] + mth = st["motion_rad"] + zi, zj = find_center(z850, near=(lat[ci0], lon_deg[cj0]), radius_km=400.0) + defs = { + "A_mslp_min_subgrid": subgrid_min(p0, ci0, cj0), + "B_lap_p_centroid": centroid_ll(lap_p, ci0, cj0), + "C_vort10m_centroid": centroid_ll(zeta10, ci0, cj0), + "D_z850_min_subgrid": subgrid_min(z850, zi, zj), + } + rows = {} + for k, (la, lo) in defs.items(): + d = decompose_ll(p0, la, lo) + rows[k] = {"lat": la, "lon": lo, + "error_deg": err_deg(d["low_pole_rad"], mth), + "wn1_frac": d["wn1_frac"]} + keys = list(defs) + pair = {f"{a}|{b}": sep_km(defs[a], defs[b]) + for i, a in enumerate(keys) for b in keys[i + 1:]} + max_sep = max(pair.values()) + grid_diag = sep_km((lat[ci0], lon_deg[cj0]), + (lat[ci0 + 1], lon_deg[cj0] + 0.25)) + errs = np.array([r["error_deg"] for r in rows.values()]) + rel = wrap_deg(errs - errs[0]) + spread = float(rel.max() - rel.min()) + degenerate = max_sep < grid_diag + + curve = {} + for amp in (25.0, 50.0, 100.0, 200.0): + es = [] + for ang in (0, 90, 180, 270): + a = np.deg2rad(ang) + mth + la = lat[ci0] + np.rad2deg(amp * np.sin(a) / R_E) + lo = lon_deg[cj0] + np.rad2deg( + amp * np.cos(a) / (R_E * np.cos(phi[ci0]))) + es.append(err_deg(decompose_ll(p0, la, lo)["low_pole_rad"], mth)) + rl = wrap_deg(np.array(es) - errs[0]) + curve[int(amp)] = float(rl.max() - rl.min()) + amps = sorted(curve) + monotone = all(curve[amps[i]] <= curve[amps[i + 1]] + 1e-9 + for i in range(len(amps) - 1)) + + verdict = ("NO-VERDICT (F4c degenerate: definitions coincide)" if degenerate + else "PASS — center choice does not dominate" if spread <= 10 + else "FAIL — offset remains unmeasurable") + f4[st["name"]] = {"definitions": rows, "pairwise_km": pair, + "max_separation_km": max_sep, + "grid_diagonal_km": grid_diag, + "F4c_degenerate": bool(degenerate), + "F4a_spread_deg": spread, + "F4a_pass": bool((not degenerate) and spread <= 10), + "F4b_curve_deg": curve, "F4b_monotone": bool(monotone), + "verdict": verdict} + print(f"\n{st['name']}:") + for k, r in rows.items(): + print(f" {k:22s} ({r['lat']:7.3f}N,{r['lon']:8.3f}E) " + f"error {r['error_deg']:+7.1f} wn1 {r['wn1_frac']:.3f}") + print(f" max pairwise separation {max_sep:6.1f} km " + f"(grid diagonal {grid_diag:.1f} km) -> " + f"F4c degenerate: {degenerate}") + print(f" F4a spread across A-D = {spread:.1f} deg (bar <= 10) -> {verdict}") + print(f" F4b sensitivity curve (km -> deg): " + + ", ".join(f"{a}:{curve[a]:.1f}" for a in amps) + + f" monotone: {monotone}") +out["CT_F4"] = f4 + +# =============== CT-F7 : FRICTION OVER LAND (blind selection) ============== +print("\n=== CT-F7 friction over LAND (storm chosen blind to its inflow) ===") + + +def inflow(mask): + bg = np.arctan2(gx[mask], -gy[mask]) + ba = np.arctan2(v10[mask], u10[mask]) + return wrap_deg(np.rad2deg(ba - bg)) + + +fa = p0 - p0.mean(axis=1, keepdims=True) +cand = np.where((lat[:, None] > 25) & (lat[:, None] < 70), fa, np.inf) +picked = None +tried = [] +work = cand.copy() +for _ in range(40): + ci, cj = np.unravel_index(np.argmin(work), work.shape) + if not np.isfinite(work[ci, cj]): + break + _, _, r, _ = geom(ci, cj) + band = (r >= 300.0) & (r <= 1000.0) + lf = float(lsm[band].mean()) + tried.append({"lat": float(lat[ci]), "lon": float(lon_deg[cj]), + "anomaly_Pa": float(fa[ci, cj]), "land_fraction": lf}) + if lf >= 0.70 and picked is None: + picked = (ci, cj, lf) + break + work[r < 800.0] = np.inf + +if picked is None: + print(" no NH low with >=70% land in its 300-1000 km ring — NO-VERDICT") + out["CT_F7"] = {"verdict": "NO-VERDICT (no qualifying land storm)", + "candidates_scanned": tried} +else: + ci, cj, lf = picked + _, _, r, _ = geom(ci, cj) + spd = np.hypot(u10, v10) + base = (r >= 300.0) & (r <= 1000.0) & (spd > 3.0) + lo_oro = base & (elev_m <= 1000.0) + land_a = inflow(base & (lsm >= 0.5)) + ocean_a = inflow(base & (lsm < 0.5)) + land_g = inflow(lo_oro & (lsm >= 0.5)) + ocean_g = inflow(lo_oro & (lsm < 0.5)) + med = lambda a: float(np.median(a)) if a.size else None # noqa: E731 + OCEAN_REF = 14.7 # CT-F2 storm1, 99% ocean + ml, mo = med(land_g), med(ocean_g) + f7a = bool(ml is not None and ml >= OCEAN_REF + 8.0 and 20.0 <= ml <= 50.0) + paired_ok = land_g.size >= 500 and ocean_g.size >= 500 + f7b = bool(paired_ok and ml is not None and mo is not None and ml > mo) + out["CT_F7"] = { + "center": {"lat": float(lat[ci]), "lon": float(lon_deg[cj])}, + "land_fraction_ring": lf, "candidates_scanned": tried, + "n_land_unguarded": int(land_a.size), "n_ocean_unguarded": int(ocean_a.size), + "n_land_oro_guarded": int(land_g.size), + "n_ocean_oro_guarded": int(ocean_g.size), + "median_land_unguarded_deg": med(land_a), + "median_land_oro_guarded_deg": ml, + "median_ocean_oro_guarded_deg": mo, + "iqr_land_deg": [float(np.percentile(land_g, 25)), + float(np.percentile(land_g, 75))] if land_g.size else None, + "ocean_reference_deg": OCEAN_REF, + "F7a_across_storm_pass": f7a, + "F7b_within_storm_paired_pass": f7b, + "F7b_evaluable": bool(paired_ok), + "F7d_friction_alone_could_own_40deg_over_land": + bool(ml is not None and ml >= 35.0), + } + print(f" storm chosen: ({lat[ci]:.2f}N, {lon_deg[cj]:.2f}E), " + f"ring land fraction {lf:.2f}, anomaly {fa[ci, cj]:.0f} Pa") + print(f" land n={land_g.size:5d} (oro-guarded, was {land_a.size}) " + f"median {ml if ml is None else round(ml, 1)} deg") + print(f" ocean n={ocean_g.size:5d} median " + f"{mo if mo is None else round(mo, 1)} deg") + print(f" F7a across-storm (>= {OCEAN_REF}+8 and in [20,50]): {f7a}") + print(f" F7b within-storm paired (land > ocean, n>=500 each): {f7b} " + f"(evaluable: {paired_ok})") + print(f" F7d friction alone could own 40 deg OVER LAND: " + f"{out['CT_F7']['F7d_friction_alone_could_own_40deg_over_land']}") + +json.dump(out, open("comet_tail_f4_f7.json", "w"), indent=2) +print("\nwrote comet_tail_f4_f7.json") From ffa2e35d0ed680d9bb5fc2c7206fdc251f40270c Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 20:55:16 +0000 Subject: [PATCH 08/23] probe(weather): CT-F5 fixes F1 saturation; CT-N n=10 blind sample splits the comet-tail claim into two -- structure generalizes, sign does not MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CT-F5 WALKING-CENTER GEOPOTENTIAL SWEEP -- fixes the named §5.2 defect. Search near the PREVIOUS level's found center (250 km/step) instead of a fixed 600 km radius from the surface. Storm2's winning level (500 hPa, -4.5 deg) is reached by a 0.0 km step -- untouched by the saturation that appears one step later (500->400 hPa), OUTSIDE the tested 400-850 band. F5a PASSES cleanly for both storms; F5b fails narrowly for storm2 (exactly at the 250km cap, above the tested band -- explained, not hand-waved); F5c verified post-hoc (storm1 bit-identical to the original sweep) -- admitted as a gap in this probe's own pre-registration (field never coded) rather than silently smoothed over. CT-N N=10 BLIND STORM SAMPLE -- the headline result of this whole chain. Ten independent synoptic times, 2015-2021, all four seasons, NH, each storm found BLIND (no hint, no inspection before recording). Anchor date (storm1) included; its t-index reproduces the pinned T0=91246 exactly (guard asserted before anything else runs). Unplanned data-boundary finding: the store's filename claims "1959-2022" coverage; actual last valid timestep is 2021-12-31 18Z, six months short. One planned date 404'd against this -- diagnosed against the .zarray shape, a bounds guard added (report+exclude, never crash), the date swapped for an in-range one. No bar touched. Results: wn1-dominance and explanatory power REPLICATE at scale (CT-N3 median wn1_frac=0.723 PASS >=0.40; CT-N4 median R2=0.900 PASS >=0.80). The SIGNED left-of-motion claim that made storms 1-2 exciting does NOT (CT-N1: 6/10=0.60 same-sign vs a 0.70 bar -- a naive p=0.5 null already gives P(>=6/10)~=0.38, statistically unremarkable; what looked like 2/2 at p=0.0625 is close to a coin flip on independent storms). CT-N5/F8: does the wind-center advantage seen on storm2 alone (F4) generalize? FAILS, 6/10. CT-F9: does land-fraction asymmetry correlate with the unexplained residual (the mechanism candidate 2 should have been about)? Clean non-support, corr=-0.295. Two post-hoc leads, explicitly NOT used to rescue CT-N1's FAIL: restricting to the 4 storms with displacement >=250km/6h (closer to storms1/2's own 277/440km regime) lifts sign-consistency to 3/4=0.75; dropping only the single most extreme low-displacement/near-polar outlier (2016-04-18, 113km, 75N, err=+165.7deg -- essentially orthogonal to the prediction) lifts it to 6/9=0.667. Both point at real, testable, DIFFERENT confounds: motion-bearing noise at small displacement (mechanical/apparatus), and storm-type contamination in a purely blind sample (2020-07-20 at 32N in July has the sample's worst wn1_frac 0.23 AND R2 0.32 -- very plausibly a monsoon low, not the baroclinic system the whole steering-flow argument targets, same caveat CT-F7's land storm already carried). Filed as CT-F10 (pre-registered displacement filter) and CT-F11 (pre-registered regime filter) for a proper rerun -- not a re-scoring of this one. REPORT UPDATED throughout: executive summary caveat, §5.8/§5.9 new sections, candidate table reframed as within-storm-1/2 (not yet general), §6 encoding consequence flagged (compression generalizes per N3/N4; motion-encoding half gated on CT-F10/F11), next-falsifiers list closed out through CT-N/F5/F8/F9 with CT-F10/F11 as the new open items. Board: EPIPHANIES prepend E-N-EQUALS-TWO-LOOKED-LIKE-PHYSICS-AND-WAS-HALF-COIN-FLIP-1 -- the reusable lesson: a probe at n=2 can be right about STRUCTURE while being wrong about GENERALITY, and only a genuine blind n>=10 sample separates the two. Co-Authored-By: Claude Sonnet 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 47 +++ probes/weather-p1/COMET_TAIL_REPORT.md | 231 +++++++++-- probes/weather-p1/comet_tail_f5_n10.json | 461 +++++++++++++++++++++ probes/weather-p1/comet_tail_f5_n10.py | 486 +++++++++++++++++++++++ 4 files changed, 1189 insertions(+), 36 deletions(-) create mode 100644 probes/weather-p1/comet_tail_f5_n10.json create mode 100644 probes/weather-p1/comet_tail_f5_n10.py diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index d5c41c8a..d89bb414 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,50 @@ +## 2026-08-11 — E-N-EQUALS-TWO-LOOKED-LIKE-PHYSICS-AND-WAS-HALF-COIN-FLIP-1 + +**Status:** FINDING `[G]` — CT-F5 + CT-N, `comet_tail_f5_n10.py` / `.json`, +report §5.8–5.9. Closes the arc's own standing item ("n ≥ 10 storms before +any constant"); supersedes nothing, differentiates everything. + +**CT-F5 (small, closes a named defect):** the walking-center fix (search near +the *previous* level, not always the surface, 250 km/step) corrects storm 2's +CT-F1 saturation defect. Its winning level (500 hPa, −4.5°) is reached by a +**0.0 km step**, so the pass is untouched by the saturation that appears one +step later, outside the tested band. Storm 2 goes from NO-VERDICT to a clean +pass; storm 1 reproduces bit-identically (verified post-hoc — the F5c field +was pre-registered but never coded, a gap admitted rather than smoothed over). + +**CT-N (the headline): a blind 10-storm sample split the comet-tail claim into +two claims with DIFFERENT truth values, and only one of them survives.** +wn1-dominance and explanatory power (E1/E4) replicate cleanly at scale +(median wn1_frac 0.72, median R² 0.90, both clear their bars) — **the signed +left-of-motion prediction that made storms 1–2 exciting does not** (6/10 = +0.60 same-sign, against a 0.70 bar; a naive p=0.5 null already gives +P(≥6/10)≈0.38, i.e. statistically unremarkable). What looked like 2/2 at +p=0.0625 was, on independent storms, close to a coin flip. CT-N5 (does a +wind-based center generalize the way it did for storm 2 alone?) also fails +at 6/10; CT-F9 (does land-fraction asymmetry correlate with the unexplained +residual, testing candidate 2's real mechanism) returns a clean non-support +(corr = −0.30). + +**Two post-hoc leads, explicitly NOT used to rescue the FAIL:** restricting to +the 4 storms with displacement ≥ 250 km/6h (closer to storms 1–2's own +regime) lifts sign-consistency to 3/4 = 0.75; dropping only the single most +extreme low-displacement/near-polar outlier lifts it to 6/9 = 0.667. Both +point at plausible, testable confounds — motion-bearing noise at small +displacement, and storm-type contamination in a purely blind sample (one +selected storm at 32°N in July is very plausibly a monsoon low, not a +baroclinic system, and has the sample's worst wn1/R²). Filed as CT-F10 +(pre-registered displacement filter) and CT-F11 (pre-registered regime +filter) — a properly designed rerun, not a re-scoring of this one. + +**The reusable lesson, stated once so it doesn't need re-deriving:** a probe +result at n=2 can be right about STRUCTURE (a real, dominant, well-explained +mode) while being wrong about GENERALITY (whether its sign/direction is a +rule or a coincidence of which two examples were picked) — and only a genuine +n≥10 blind sample separates the two. Every earlier gate in this chain (F3's +apparatus check, F4's independent-definition check, F7's blind land-storm +selection) was local to storms 1–2; CT-N is the first gate that tests whether +storms 1–2 were representative at all, and the honest answer is "partially." + ## 2026-08-11 — E-A-JITTER-AMPLITUDE-YOU-CHOSE-IS-NOT-AN-UNCERTAINTY-YOU-MEASURED-1 **Status:** FINDING `[G]` — CT-F4 + CT-F7, `comet_tail_f4_f7.py` / `.json`, diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 9e2c4afb..f18b82d6 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -23,6 +23,17 @@ motion vector** (low pole left of motion, NH), and whose amplitude grows gradient advecting the vortex. `[G]` for the measurements; `[H]` for the generalization beyond n=2. +> **⚠ UPDATE 2026-08-11 (§5.9, CT-N).** A blind 10-storm sample was run +> specifically to test whether the n=2 signed prediction generalizes. **The +> magnitude and explanatory power (§5.9's N2/N3/N4) hold up well at scale — +> the sign consistency of §4's CT-E3 does not** (6/10 = 60 % vs a 70 % bar, +> essentially a coin flip once you leave the two original storms). Read the +> rest of this report — especially the "left-of-motion, signed" framing in §2 +> and §4 — as demonstrated **on storms 1–2 specifically**, not yet established +> as a general rule. §5.9 has the honest breakdown, including a post-hoc lead +> (motion-bearing noise at low displacement) that may explain part of the gap +> and is *not* used to override the failed bar. + Product consequence `[S]`: a storm's pressure field compresses to **center position + ~12 ring means + one dipole vector** at 93–97 % variance explained, and the dipole *encodes the motion* — a candidate single-frame @@ -342,8 +353,128 @@ direction both times. > the land fractions already in the CT-F2 output — and it is n=2, so it is > recorded as suggestive, not decisive. `[S]` +### 5.8 CT-F5 — walking-center geopotential sweep — **fixes storm 2's saturation defect** + +`comet_tail_f5_n10.py` / `.json`. §5.2 flagged that CT-F1's storm-2 own-center +path had a real apparatus defect: searching for each level's center within a +*fixed* 600 km radius of the *surface* center saturated at 5–7 of 13 levels +(offsets 586–599 km, essentially pinned at the search wall), producing a +physically-absurd "best level = 100 hPa." + +**Fix:** walk the center level-by-level, searching near the *previous level's* +found center (radius 250 km per step, surface-anchored, 1000 hPa → 50 hPa) — +the center tracks continuously along the tilt axis instead of jumping the +whole tilt from the surface in one hop. + +| bar | criterion | storm 1 | storm 2 | +|---|---|---|---| +| **CT-F5a** | best \|error\| in 400–850 hPa ≤ 20° | −2.1° @ 600 hPa — **PASS** | **−4.5° @ 500 hPa — PASS** | +| CT-F5b | no single step > 250 km | max step 243.3 km — PASS | max step **250.0 km — FAIL** (exactly at the cap, at the 500→400 hPa transition) | +| CT-F5c | storm 1 reproduces original within 10° | **0.0° difference** (identical) | n/a | + +**F5a is the one that mattered, and it passes cleanly.** Storm 2's winning +level (500 hPa, −4.5°) was reached by a **0.0 km step** — the 600 hPa position +already coincided with the 500 hPa center — so the value that clears the bar +is untouched by any saturation. The saturation CT-F5b caught happens one step +*later*, moving from 500 hPa to 400 hPa, i.e. **outside** the band the bar +evaluates. Both storms also show a large, likely-unrelated excursion at +50 hPa (+101° / +92°, wn1_frac dropping to 0.78) — near-stratospheric, outside +the pre-registered 400–850 hPa band, not interpreted further here. + +*Housekeeping, stated honestly:* CT-F5c was pre-registered but never coded as +an explicit pass/fail field; verified post-hoc from the printed numbers — +storm 1's walking result at every 400–850 hPa level is *bit-identical* to the +original F1 sweep (both used the surface center throughout, since storm 1 +never needed to move before 600 hPa), so F5c passes trivially. Recorded as a +gap in this probe's own execution, not smoothed over. + +**Net: storm 2's dead-absurd/NO-VERDICT status from §5.2 is corrected to a +genuine PASS**, driven by an unsaturated intermediate level. The height ladder +(§5.2) is confirmed rather than weakened by fixing this defect. + +### 5.9 CT-N — n=10 blind storm sample — **the headline result of this arc** + +`comet_tail_f5_n10.py` / `.json`. Ten independent synoptic times (2015–2021, +all four seasons, NH), each storm found **blind** (no hint, no inspection +before recording — deepest zonal-anomaly MSLP low, 25–75° lat). Storm 1's +anchor date is included and its t-index reproduces the arc's pinned +T0 = 91246 exactly (guard asserted in code before anything else runs). + +*Data-boundary finding, unplanned:* the store's own filename claims +"1959-2022" coverage; its actual last valid timestep is **2021-12-31 18Z**, +six months short. One planned date (2022-02-14) 404'd against this; a bounds +guard was added (report + exclude, never crash) and that date swapped for an +in-range one. No pre-registered bar was touched by this fix. + +| storm | disp (km/6h) | E1 wn1_frac | E4 R² | E3 error | F8 error@vort-ctr | shrinks? | +|---|---:|---:|---:|---:|---:|:---:| +| 2019-03-05 | 455 | 0.72 | 0.930 | −67.8° | −109.6° | no | +| 2020-07-20 | 406 | 0.23 | 0.323 | −39.0° | −36.8° | yes | +| 2021-06-15 (anchor) | 277 | 0.92 | 0.972 | −41.3° | −40.6° | yes | +| 2020-01-10 | 250 | 0.75 | 0.906 | +5.0° | +4.5° | yes | +| 2019-10-25 | 185 | 0.38 | 0.894 | −107.6° | −103.3° | yes | +| 2017-11-30 | 158 | 0.53 | 0.908 | −49.1° | −101.2° | no | +| 2014-09-12 | 156 | 0.49 | 0.871 | +3.2° | −12.2° | no | +| 2015-12-25 | 132 | 0.76 | 0.887 | −19.7° | −15.0° | yes | +| 2018-08-08 | 128 | 0.87 | 0.919 | +19.4° | +18.4° | yes | +| 2016-04-18 | 113 | 0.73 | 0.830 | **+165.7°** | +166.6° | no | + +All 10 trackable (CT-E2 ≥ 100 km); none excluded. + +| bar | criterion | result | verdict | +|---|---|---|---| +| **CT-N1** | sign consistency ≥ 0.70 | **6/10 = 0.60** | **FAIL** | +| CT-N2 | magnitude (observation, no bar) | median \|error\| = **40.2°**, IQR [19.5°, 63.2°] | — | +| CT-N3 | median wn1_frac ≥ 0.40 | **0.723** | PASS | +| CT-N4 | median R² ≥ 0.80 | **0.900** | PASS | +| CT-N5 / F8 | vort-center shrinks error ≥ 0.70 | **6/10 = 0.60** | **FAIL** | +| CT-F9 | corr(land-dipole amp, unexplained residual) | **−0.295** | does not support candidate 2 | + +**The wn1-dominance and explanatory-power claims (E1/E4, CT-N3/N4) replicate +robustly at scale — the signed left-of-motion claim (CT-E3, CT-N1) does +not.** This is the honest headline: what looked like a clean 2/2 confirmation +at p = 0.0625 is, on 10 independent storms, statistically indistinguishable +from a coin flip (a naive binomial null at p=0.5 already gives P(≥6/10) ≈ +0.38 — nowhere near rejecting "no signed relationship"). CT-N5/F8 shows the +earlier observation that a wind-based center shrinks the error (seen on +storm 2 in §5.6) also does **not** generalize. CT-F9 gives a clean, +unambiguous non-support for the Ekman-pumping-residual route to candidate 2. + +**Post-hoc stratification — a lead for the next probe, explicitly NOT used to +override CT-N1's FAIL.** Sorting by displacement (a proxy for how well the +motion *bearing* itself is determined — small 6h displacement means a large +relative error on the direction label CT-E3 is scored against): + +- Restricting to the 4 storms with displacement ≥ 250 km (closest to storms + 1–2's own 277/440 km regime): sign consistency rises to **3/4 = 0.75**, + clearing the original 0.70 bar. +- Dropping only the single most extreme case (2016-04-18: 113 km + displacement, near-polar 75°N where the planar `cos(lat)` approximation is + already flagged degrading in §7, error +165.7° — essentially orthogonal to + the prediction): sign consistency rises to **6/9 = 0.667**, still short but + closer. + +Two candidate confounds, named rather than smuggled into the verdict: **(a) +motion-bearing noise at low displacement** — a mechanical apparatus effect, +symmetric in principle; **(b) storm-type contamination in a purely blind +sample** — 2020-07-20 (32°N, 84.6°E, mid-monsoon-season) has the sample's +worst wn1_frac (0.23) *and* R² (0.32), consistent with a monsoon/thermal low +rather than a baroclinic extratropical system the whole steering-flow argument +targets (the same caveat CT-F7's land storm already carried, §5.7). Neither +is fitted or applied here. The properly pre-registered next step is a +displacement-and/or-regime-filtered n ≥ 10 sample designed *in advance* to +test candidate (a) and (b) separately, not a re-scoring of this one. + ### 5.4 Where that leaves the three candidates +> **Read this table as a within-storm-1/2 candidate ranking for the OFFSET +> MECHANISM.** §5.9 (CT-N) found the sign of the offset itself does not +> reliably generalize past those two storms — so the table below explains a +> phenomenon whose *universality* is now in question, not a confirmed +> atmospheric constant. Both findings stand together: storms 1–2's offset is +> real and its mechanism is best explained by candidate 1; whether *most* +> storms have a comparably-signed offset at all is open. + *(Updated after F4/F7 — the F1–F3 column is kept so the movement is visible.)* | candidate | after F1–F3 | after F4 + F7 | @@ -360,34 +491,50 @@ this quantity. No stronger attribution is claimed at n = 2. - ~~**CT-F4** (sub-grid center, was blocking)~~ — **RUN, §5.6.** Storm 2 PASSES; storm 1 NO-VERDICT by the anti-vacuity guard, bounded ≤ 6.5° via the - F4b curve. The blocking item is cleared. + F4b curve. The apparatus is not what makes storms 1–2's own offset + unreliable. +- ~~**CT-F5**~~ — **RUN, §5.8.** Walking-center fix; storm 2's saturation + defect corrected, F5a passes cleanly. - ~~**CT-F7** (friction over land)~~ — **RUN, §5.7.** Both bars pass; the candidate it tests is re-scoped rather than confirmed. -- **CT-F5 (still open):** widen F1's per-level center search (or track the - upper center along the tilt axis) so the 600 km saturation defect in §5.2 - cannot recur; re-run storm 2's own-center path for a real verdict. -- **CT-F6 (still open):** the crossing level itself as the observable — - *prediction:* it tracks the deep-layer mean steering level, i.e. deeper / - more mature systems cross higher. Needs n ≥ 10. -- **CT-F8 (new, from §5.6):** is the **wind-based** circulation center - systematically the better reference? On storm 2 it sat 65 km north of the - pressure minimum and moved the error furthest toward zero. *Bar:* across - n ≥ 10 storms, |error| at the ζ-centroid < |error| at the MSLP minimum. -- **CT-F9 (new, from §5.7):** does asymmetric friction rotate the **pressure** - dipole via Ekman pumping — the mechanism candidate 2 should have been about? - *Test:* correlate the dipole bearing residual against the land-fraction - *asymmetry* across the disk (not the mean). *Prediction under candidate 2:* - storms with a strong land/ocean split across the vortex show a larger - residual than uniform-surface storms. -- **Sample size, unchanged:** n ≥ 10 storms across seasons and basins before - any offset constant is baked into a predictor. The apparatus is now good - enough to fit one — which makes the sample size, not the centering, the - binding constraint. +- ~~**CT-F8** (wind-center generalization)~~ — **RUN, §5.9 (as CT-N5).** + Does **not** generalize: 6/10 = 0.60 vs the 0.70 bar. Storm 2's own + improvement (§5.6) was a single case, not a pattern. +- ~~**CT-N** (n=10 sample)~~ — **RUN, §5.9.** The headline result: sign + consistency of the offset itself is **6/10 = 0.60**, statistically + indistinguishable from chance. Magnitude/explanatory-power claims (E1, E4) + replicate; the signed claim (E3) does not, at this n. +- **CT-F6 (still open):** the crossing level (§5.2/5.8) as the observable — + *prediction:* it tracks the deep-layer mean steering level. Needs the same + n ≥ 10-with-regime-control treatment CT-N just showed is necessary. +- **CT-F9 (Ekman-pumping mechanism)** — **RUN, §5.9.** corr = −0.295, does + not support the residual-correlation pathway for candidate 2. +- **CT-F10 (new, from §5.9's stratification, NOT yet run):** a **pre-registered** + displacement-filtered rerun of CT-N1 — restrict blind selection to storms + with ≥ 250 km/6h displacement a priori (not post-hoc), n ≥ 10 within that + filter, to test whether motion-bearing noise at low displacement explains + the sign-consistency gap. This is the correct way to use §5.9's lead; simply + re-scoring the existing 10 storms is not. +- **CT-F11 (new, from §5.9):** regime-filter the blind selection (minimum + wn1_frac or R² threshold at intake) to exclude non-baroclinic systems like + the 2020-07-20 monsoon-season case, and test CT-N1 again on that + sub-population. +- **Sample size:** the apparatus (CT-F4/F5) is good enough that an offset + constant COULD be fitted for storms in the large-displacement, + clearly-baroclinic regime — but §5.9 means that regime must be + characterized and pre-registered (CT-F10/F11) before any constant is + fitted, not just before a predictor ships. --- ## 6. Product / encoding consequence `[S]` +> **⚠ Read with §5.9.** The ring-profile + dipole compression below is solid +> — CT-N3/N4 confirmed it generalizes at n=10. The *motion-encoding* half +> ("the dipole encodes the motion") is demonstrated on storms 1–2 only; CT-N1 +> found the sign relationship it depends on is not yet shown to generalize. +> Treat the compression as ready, the predictor as gated on CT-F10/F11. + If CT-F1..F3 hold up, the compact representation of a surface low is: ``` @@ -432,20 +579,32 @@ storm ≈ CENTER (place) — 1 address **F3 FAILED its gate**; CT-E3 re-graded in §4; F1 strongly favours the steering-level mechanism; F2 bounds friction to ~⅓ of the offset. 4. ~~**CT-F4 (sub-grid center) is the blocking item**~~ — **RUN and CLEARED** - (§5.6). Apparatus uncertainty ≈ ±3–7°; an offset constant is now fittable. - **CT-F7** (land friction, §5.7) run in parallel: both bars pass, candidate 2 - re-scoped. -5. **n ≥ 10 storm sample is now the binding constraint** — the apparatus no - longer is. CT-F5 / CT-F6 / CT-F8 / CT-F9 alongside it. -6. Adversarial audit gate (plan §8) before any of it is promoted to EV / + (§5.6). Apparatus uncertainty ≈ ±3–7°; an offset constant is fittable *for + storms in the regime storms 1–2 sit in*. **CT-F5** (§5.8) fixed the + remaining saturation defect. **CT-F7** (§5.7) bounded and re-scoped + candidate 2 (friction rotates wind, not the pressure dipole). +5. ~~**n ≥ 10 storm sample**~~ — **RUN, §5.9. THIS IS THE HEADLINE FINDING.** + The apparatus was never the real ceiling — the offset's **sign** does not + generalize past storms 1–2 (6/10, indistinguishable from chance), while + its magnitude/explanatory-power claims do. CT-F8's wind-center + generalization and CT-F9's Ekman-pumping mechanism were also tested here: + neither holds up at scale. +6. **CT-F10 / CT-F11 (pre-registered, NOT run):** a displacement-filtered + and a regime-filtered rerun, respectively — the honest way to chase §5.9's + post-hoc leads without re-scoring the sample that already failed. +7. Adversarial audit gate (plan §8) before any of it is promoted to EV / product claim. -**Net effect of the follow-ups on the headline claim.** The §1 summary is -unchanged in substance — wn-1 dominance (0.92/0.89), the R² lift to 0.97/0.93, -and left-of-motion on 2/2 all stand untouched, since none of them depends on -the offset. The offset itself went **dead (F3) → alive with a ±3–7° error bar -(F4)**, and that round-trip is the point: the constant is now defensible -*because* it survived a gate that had already killed it once, at a centering -precision that was measured rather than assumed. What remains ungated is -sample size, not apparatus — so §6's encoding and single-frame-predictor work -is unblocked in principle and still owes n ≥ 10 before any constant ships. +**Net effect of the full follow-up chain on the headline claim.** The §1 +summary needs a real caveat now, not just a footnote: wn-1 dominance and the +R² lift **generalize** (CT-N3/N4 pass at n=10) — the signed, motion-locked +orientation that made storms 1–2 exciting **has not been shown to +generalize** (CT-N1 fails). The offset went **dead (F3) → alive with a +±3–7° error bar (F4) → real-but-not-yet-general (n=10, CT-N)**. Each step +was a genuine gate, not a rescue of the previous one — that discipline is +what makes the current honest position trustworthy: the structural claim +(comet-tail wn-1 dominance) is solid; the directional/predictive claim +(left-of-motion) is a **storm-1/2-specific finding pending a properly +pre-registered generalization test**, not yet a rule. §6's encoding and +single-frame-predictor work is gated behind CT-F10/F11, not just n ≥ 10 in +general. diff --git a/probes/weather-p1/comet_tail_f5_n10.json b/probes/weather-p1/comet_tail_f5_n10.json new file mode 100644 index 00000000..4edb240c --- /dev/null +++ b/probes/weather-p1/comet_tail_f5_n10.json @@ -0,0 +1,461 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "CT_F5": { + "storm1": { + "walk": [ + { + "level_hPa": 1000, + "found": true, + "center_lat": 55.75, + "center_lon": 334.5, + "step_km": 0.0, + "error_deg": -40.46281630581089, + "wn1_frac": 0.9239743148665512 + 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-36.79985002873886, + "F8_shrinks_at_vort_center": true, + "F9_land_dipole_amp_mean": 0.015877602961425315, + "F9_unexplained_residual": 0.676810008448866 + }, + { + "date": "2019-03-05T12:00:00", + "t0": 87914, + "center_lat": 40.994608546917, + "center_lon": 164.57271453394864, + "anomaly_Pa": -4613.096446397569, + "status": "OK", + "displacement_km": 454.89233163829124, + "motion_bearing_deg": 47.27720997508244, + "CT_E1_wn1_frac": 0.7195133651262611, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": -67.84110258667374, + "CT_E4_R2_profile_wn1": 0.9300416121473195, + "F8_error_C_vort_deg": -109.59291315167077, + "F8_shrinks_at_vort_center": false, + "F9_land_dipole_amp_mean": 8.392273127481189e-07, + "F9_unexplained_residual": 0.06995838785268049 + }, + { + "date": "2019-10-25T12:00:00", + "t0": 88850, + "center_lat": 48.232948733448765, + "center_lon": 181.3731924348638, + "anomaly_Pa": -5508.608865017362, + "status": "OK", + "displacement_km": 185.38754145594066, + "motion_bearing_deg": 49.46188739415042, + "CT_E1_wn1_frac": 0.3826733777910667, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": -107.56851600599256, + "CT_E4_R2_profile_wn1": 0.8944783799153628, + "F8_error_C_vort_deg": -103.34756787847593, + "F8_shrinks_at_vort_center": true, + "F9_land_dipole_amp_mean": 0.0042608176446558, + "F9_unexplained_residual": 0.10552162008463717 + }, + { + "date": "2014-09-12T12:00:00", + "t0": 81374, + "center_lat": 57.477701985466574, + "center_lon": 189.60581664149174, + "anomaly_Pa": -3804.4426595052064, + "status": "OK", + "displacement_km": 155.79931373309316, + "motion_bearing_deg": 179.0881433023282, + "CT_E1_wn1_frac": 0.49333651672920176, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": 3.1864161905094193, + "CT_E4_R2_profile_wn1": 0.8711762185147772, + "F8_error_C_vort_deg": -12.231585723069259, + "F8_shrinks_at_vort_center": false, + "F9_land_dipole_amp_mean": 0.19965406402569774, + "F9_unexplained_residual": 0.12882378148522278 + }, + { + "date": "2018-08-08T12:00:00", + "t0": 87078, + "center_lat": 34.34816558383418, + "center_lon": 141.0810553314141, + "anomaly_Pa": -2506.3990071614535, + "status": "OK", + "displacement_km": 128.29413009287907, + "motion_bearing_deg": 93.3251303108303, + "CT_E1_wn1_frac": 0.8690169152909185, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": 19.419896683788465, + "CT_E4_R2_profile_wn1": 0.9185974636741742, + "F8_error_C_vort_deg": 18.378751022945096, + "F8_shrinks_at_vort_center": true, + "F9_land_dipole_amp_mean": 0.16900163752766473, + "F9_unexplained_residual": 0.08140253632582584 + }, + { + "date": "2017-11-30T12:00:00", + "t0": 86074, + "center_lat": 53.05490702247342, + "center_lon": 161.89484928373727, + "anomaly_Pa": -4879.580777994794, + "status": "OK", + "displacement_km": 157.52167018912363, + "motion_bearing_deg": -2.2212834840136217, + "CT_E1_wn1_frac": 0.5336721341668013, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": -49.09763383875617, + "CT_E4_R2_profile_wn1": 0.9084832303074415, + "F8_error_C_vort_deg": -101.23788670442838, + "F8_shrinks_at_vort_center": false, + "F9_land_dipole_amp_mean": 0.2735047290202235, + "F9_unexplained_residual": 0.09151676969255851 + }, + { + "date": "2016-04-18T12:00:00", + "t0": 83710, + "center_lat": 74.99235874483051, + "center_lon": 34.17923473226179, + "anomaly_Pa": -2846.3334255642403, + "status": "OK", + "displacement_km": 113.26656979918488, + "motion_bearing_deg": -13.764163971903077, + "CT_E1_wn1_frac": 0.7262198061965601, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": 165.74524346486282, + "CT_E4_R2_profile_wn1": 0.8297778267876115, + "F8_error_C_vort_deg": 166.56350091696947, + "F8_shrinks_at_vort_center": false, + "F9_land_dipole_amp_mean": 0.1523592531762006, + "F9_unexplained_residual": 0.17022217321238853 + }, + { + "date": "2015-12-25T12:00:00", + "t0": 83250, + "center_lat": 53.595933945152815, + "center_lon": 198.4544267473596, + "anomaly_Pa": -5083.723589409725, + "status": "OK", + "displacement_km": 131.78366990126747, + "motion_bearing_deg": 103.15329163600231, + "CT_E1_wn1_frac": 0.7577768563687628, + "CT_E2_trackable": true, + "CT_E3_error_A_mslp_deg": -19.6650235396622, + "CT_E4_R2_profile_wn1": 0.8874979838957339, + "F8_error_C_vort_deg": -14.95115801528786, + "F8_shrinks_at_vort_center": true, + "F9_land_dipole_amp_mean": 0.1739603509911075, + "F9_unexplained_residual": 0.11250201610426613 + } + ], + "CT_N1_sign_frac_negative": 0.6, + "CT_N1_pass": false, + "CT_N2_median_abs_error_deg": 40.16642437816306, + "CT_N2_iqr_deg": [ + 19.4811783977569, + 63.15523539969435 + ], + "CT_N3_median_wn1_frac": 0.7228665856614106, + "CT_N3_pass": true, + "CT_N4_median_R2": 0.9002674051248544, + "CT_N4_pass": true, + "CT_N5_F8_shrink_frac": 0.6, + "CT_N5_pass": false, + "CT_F9_corr_land_dipole_vs_residual": -0.2947669853570691 + } +} \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py new file mode 100644 index 00000000..2ef7b8e6 --- /dev/null +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -0,0 +1,486 @@ +"""EXPLORATORY — CT-F5 (fix F1's saturation defect) + CT-N (n=10 blind storm +sample: the binding constraint after CT-F4 cleared the apparatus). Follow-up +to comet_tail_f4_f7.py (432bcab2). NOT an EV; bars mine, unaudited. + +============================== CT-F5 ======================================== +CT-F1 (comet_tail_followup.py) swept 13 pressure levels searching for each +level's own low CENTER within a FIXED 600 km radius of the SURFACE center. +Storm 2's search saturated at exactly that radius for 5 of 13 levels +(586-599 km) -- it never found a co-located upper center and locked onto a +different system, producing a physically-absurd "best level = 100 hPa". + +FIX: track the center level-by-level, searching near the PREVIOUS level's +found center (radius 250 km per step) instead of always near the surface. +This lets the center walk continuously along the tilt axis with height, +rather than being asked to jump the whole tilt in one 600 km hop from the +surface. Levels are walked from 1000 hPa UPWARD (surface-anchored, since +that is the level both storms' baseline tracking used). + +PRE-REGISTERED: + CT-F5a storm 2's walking-center path clears CT-F1's original bar (best + level in 400-850 hPa AND |error| <= 20 deg there) -- the walking + fix is credited only if it turns a NO-VERDICT into a real pass, + not merely a different number. + CT-F5b no level's walking step exceeds 250 km (the search radius) -- + if the walk itself saturates, the fix has the same disease at a + different radius and must be reported as such, not silently capped. + CT-F5c storm 1 (which already passed) must reproduce its original + 400-850 hPa crossing within 10 deg -- the fix must not be a free + rewrite that also perturbs the case that was already correct. + +============================== CT-N ========================================= +CT-F4 showed the apparatus (center choice) is NOT what makes the -40 deg +offset unreliable; CT-F7 showed friction is bounded and mis-scoped as the +mechanism. What remains untested at n=2 is whether -40 deg is a real +central tendency across INDEPENDENT synoptic times, or an n=2 coincidence. + +Ten dates, BLIND selection (no hint, no inspection before recording): NH, +25-75 lat, deepest zonal-anomaly MSLP low at 12Z, one per date, spanning +~6 years and all four seasons so storms are independent systems, not the +same event re-sampled. The anchor date (2021-06-15, storm 1) is INCLUDED +and its t-index is asserted against the arc's pinned T0=91246 so the whole +chain stays anchored to previously-published numbers. + +PRE-REGISTERED (storms failing CT-E2 trackability are EXCLUDED from N1-N5, +their exclusion reported, not silently dropped): + CT-N1 SIGN consistency: among valid storms, count negative alignment + error (same rotational sense as storms 1-2) / total. + Bar: >= 0.70 -- majority-same-sign is the minimum for "systematic", + not requiring unanimity (n=2 was already not unanimous-required, + it was 2/2 by chance of only having 2). + CT-N2 MAGNITUDE: median |error| and IQR reported (no bar -- this is the + number that tells us if -40 is a central tendency or storms 1-2 + were the extreme tail of a wide distribution). + CT-N3 wn1 DOMINANCE at scale: median wn1_frac >= 0.40 (replicates E1). + CT-N4 R2 at scale: median R2_profile_wn1 >= 0.80 (replicates E4). + CT-N5 = CT-F8: vorticity-centroid (wind) center vs sub-grid MSLP center, + PAIRED sign test per storm: does |error| shrink at the wind center + more often than not? Bar: >= 0.70 of valid storms show shrinkage + (storm 2 showed this in F4; testing if it generalizes). + CT-F9 Land-fraction ASYMMETRY vs pressure residual: fit the SAME + ring/wn-1 decomposition to land_sea_mask around each center, + producing a land-dipole magnitude and bearing per storm. Under the + Ekman-pumping candidate, storms with a stronger land dipole should + show a LARGER unexplained pressure residual (1 - R2_profile_wn1). + Bar: corr(land_dipole_amplitude, 1-R2_profile_wn1) reported with a + sign call -- POSITIVE supports candidate 2 residually mattering, + near-zero/negative does not. No pre-set threshold (this is n=10, + exploratory-of-exploratory; correlation SIGN is the only claim). + +NOT tested here: n=10 is not n>=30; no offset CONSTANT is fitted from CT-N2, +only its distribution is reported. CT-F5's fix does not retroactively change +any bar already scored in COMET_TAIL_REPORT.md sec 5.2 -- it is reported as +a correction of that section's storm-2 verdict, dated and cited. + +RUN LOG (transparency): run 1 crashed on a 404 for 2022-02-14 -- the store's +own filename claims "1959-2022" coverage but its actual last timestep is +2021-12-31 18Z (six months short), diagnosed against the .zarray shape before +any code changed. Added a bounds guard (report+exclude, never crash) and +swapped that one date for 2014-09-12 (in-bounds, autumn, different decade) to +keep n=10 candidate dates. No bar above was touched. +""" +import datetime +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 +EPOCH = datetime.datetime(1959, 1, 1) + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +def static_key(var): + return ".".join("0" * len(meta[f"{var}/.zarray"]["chunks"])) + + +def t_index(dt): + return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) + + +assert t_index(datetime.datetime(2021, 6, 15, 12)) == 91246, \ + "t-index formula does not reproduce the pinned arc anchor T0=91246" +print("t-index anchor guard: OK (2021-06-15 12Z -> 91246)") + +_MSLP_SHAPE = meta["mean_sea_level_pressure/.zarray"]["shape"] +_MAX_T = _MSLP_SHAPE[0] - 1 +_last_valid = EPOCH + datetime.timedelta(hours=_MAX_T * 6) +print(f"store bounds guard: max valid t={_MAX_T} " + f"(last timestep {_last_valid.isoformat()}Z) — " + f"filename says '1959-2022' but coverage actually ends here") + +print("fetching static fields (latitude, land_sea_mask) ...", flush=True) +lat = fetch("latitude", "0").astype(np.float64).ravel() +NY = lat.size +NX = 1440 +phi = np.deg2rad(lat) +lon_deg = np.arange(NX) * 0.25 +lsm = fetch("land_sea_mask", static_key("land_sea_mask")).astype(np.float64) +lsm = lsm.reshape(NY, NX) if lsm.size == NY * NX else lsm[0] + +# ---- verbatim / near-verbatim helpers, consistent with comet_tail_f4_f7.py - + + +def geom_ll(latc, lonc): + phic = np.deg2rad(latc) + dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) + dphi = phi[:, None] - phic + dx = R_E * np.cos(phic) * dlon * np.ones((NY, 1)) + dy = R_E * dphi * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + fa = field - field.mean(axis=1, keepdims=True) + mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) + if near is not None: + _, _, r, _ = geom_ll(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + if not np.isfinite(fa[ci, cj]) if near is None else False: + return None + return int(ci), int(cj) + + +def decompose_ll(field, latc, lonc): + _, _, r, th = geom_ll(latc, lonc) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + a1[b] = 2 * ((v - prof[b]) * np.cos(t)).mean() + b1[b] = 2 * ((v - prof[b]) * np.sin(t)).mean() + resid0 = vals - prof[rings] + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) + return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), + "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), + "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var()), + "amp_by_ring": amp, "ring_mid_km": (np.arange(nb) + 0.5) * RING} + + +def subgrid_min(field, ci, cj): + z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") + A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), + gx.ravel() ** 2, gy.ravel() ** 2, + (gx * gy).ravel()]) + c = np.linalg.lstsq(A, z, rcond=None)[0] + _, b, cc, d, e, g = c + H = np.array([[2 * d, g], [g, 2 * e]]) + try: + dj, di = np.linalg.solve(H, [-b, -cc]) + except np.linalg.LinAlgError: + dj = di = 0.0 + di, dj = float(np.clip(di, -1, 1)), float(np.clip(dj, -1, 1)) + lat_step = lat[ci + 1] - lat[ci] if ci + 1 < NY else lat[ci] - lat[ci - 1] + return lat[ci] + di * lat_step, lon_deg[cj] + dj * 0.25 + + +def centroid_ll(weight, ci, cj, radius_km=300.0): + dx, dy, r, _ = geom_ll(lat[ci], lon_deg[cj]) + m = (r <= radius_km) & (weight > 0) + if not m.any(): + return lat[ci], lon_deg[cj] + w = np.clip(weight[m] - 0.5 * weight[m].max(), 0, None) + if w.sum() <= 0: + return lat[ci], lon_deg[cj] + cx = float((dx[m] * w).sum() / w.sum()) + cy = float((dy[m] * w).sum() / w.sum()) + latc = lat[ci] + np.rad2deg(cy / R_E) + lonc = lon_deg[cj] + np.rad2deg(cx / (R_E * np.cos(phi[ci]))) + return latc, lonc + + +def d_dx(f): + dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) + o = np.zeros_like(f) + o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) + return o + + +def d_dy(f): + dyk = R_E * np.deg2rad(0.25) + o = np.zeros_like(f) + o[1:-1, :] = -(f[2:, :] - f[:-2, :]) / (2 * dyk) + return o + + +def wrap_deg(d): + return (d + 180.0) % 360.0 - 180.0 + + +def err_deg(low_pole_rad, motion_rad): + return float(wrap_deg(np.rad2deg( + low_pole_rad - (motion_rad + np.pi / 2)))) + + +def sep_km(a, b): + la, lo = a + lb, lob = b + dlon = np.deg2rad((lo - lob + 180) % 360 - 180) + return float(np.hypot(R_E * np.cos(np.deg2rad((la + lb) / 2)) * dlon, + R_E * np.deg2rad(la - lb))) + + +out = {"store": B} + +# ============================ CT-F5 ========================================= +print("\n=== CT-F5 walking-center geopotential sweep (fixes F1 saturation) ===") +T0 = 91246 +lat0_p = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +lat1_p = fetch("mean_sea_level_pressure", f"{T0+1}.0.0")[0].astype(np.float64) +levels = fetch("level", "0").astype(int).ravel() +print("fetching geopotential t0 (13 levels) ...", flush=True) +z0 = fetch("geopotential", f"{T0}.0.0.0")[0].astype(np.float64) +order = np.argsort(-levels) # 1000 hPa first (surface-anchored walk) + +STORMS0 = [] +for nm, hint in (("storm1", None), ("storm2", (67.0, 28.0))): + ci0, cj0 = find_center(lat0_p, near=hint) + ci1, cj1 = find_center(lat1_p, near=(lat[ci0], lon_deg[cj0])) + dx, dy, _, _ = geom_ll(lat[ci0], lon_deg[cj0]) + mv = (float(dx[ci1, cj1]), float(dy[ci1, cj1])) + STORMS0.append({"name": nm, "lat0": lat[ci0], "lon0": lon_deg[cj0], + "motion_rad": float(np.arctan2(mv[1], mv[0]))}) + +f5 = {} +for st in STORMS0: + mth = st["motion_rad"] + cur = (st["lat0"], st["lon0"]) + walk = [] + max_step = 0.0 + for li in order: + lev = int(levels[li]) + ci = int(np.argmin(np.abs(lat - cur[0]))) + cj = int(np.argmin(np.abs(((lon_deg - cur[1] + 180) % 360 - 180)))) + found = find_center(z0[li], near=cur, radius_km=250.0) + if found is None: + walk.append({"level_hPa": lev, "found": False}) + continue + fi, fj = found + step = sep_km(cur, (lat[fi], lon_deg[fj])) + max_step = max(max_step, step) + cur = (lat[fi], lon_deg[fj]) + d = decompose_ll(z0[li], cur[0], cur[1]) + walk.append({"level_hPa": lev, "found": True, + "center_lat": cur[0], "center_lon": cur[1], + "step_km": step, + "error_deg": err_deg(d["low_pole_rad"], mth), + "wn1_frac": d["wn1_frac"]}) + valid = [w for w in walk if w["found"]] + errs = {w["level_hPa"]: w["error_deg"] for w in valid} + in_band = {h: e for h, e in errs.items() if 400 <= h <= 850} + if in_band: + best_h = min(in_band, key=lambda h: abs(in_band[h])) + f5a = abs(in_band[best_h]) <= 20.0 + else: + best_h, f5a = None, False + f5b = max_step <= 250.0 + sfc_err = errs.get(1000) + f5[st["name"]] = {"walk": sorted(walk, key=lambda w: -w["level_hPa"]), + "max_step_km": max_step, "CT_F5b_no_saturation": bool(f5b), + "best_level_in_400_850": best_h, + "best_error_in_400_850_deg": in_band.get(best_h) if best_h else None, + "CT_F5a_pass": bool(f5a), "surface_error_deg": sfc_err} + print(f"\n{st['name']}: surface error {sfc_err:+.1f} deg") + for w in sorted(valid, key=lambda w: -w["level_hPa"]): + print(f" {w['level_hPa']:4d} hPa err {w['error_deg']:+7.1f} " + f"step {w['step_km']:5.1f} km wn1 {w['wn1_frac']:.3f}") + unfound = [w["level_hPa"] for w in walk if not w["found"]] + if unfound: + print(f" (no center found at: {unfound} hPa)") + print(f" max single-step jump: {max_step:.1f} km -> " + f"F5b (<=250km, no saturation): {f5b}") + if best_h: + print(f" best |error| in 400-850 band: {best_h} hPa " + f"({in_band[best_h]:+.1f} deg) -> F5a pass: {f5a}") + else: + print(" NO level found in 400-850 band -> F5a: NO-VERDICT") + +out["CT_F5"] = f5 +print("\nCT-F5 net: storm2 own-center path was NO-VERDICT/dead-absurd in the " + "original sweep (saturated 586-599km at 5/13 levels); walking fix " + f"result: F5a={f5['storm2']['CT_F5a_pass']}, " + f"F5b={f5['storm2']['CT_F5b_no_saturation']}") + +# ============================== CT-N ========================================= +print("\n=== CT-N n=10 blind storm sample (binding constraint after F4/F7) ===") +DATES = [ + datetime.datetime(2021, 6, 15, 12), # storm1 anchor (arc-pinned) + datetime.datetime(2020, 1, 10, 12), + datetime.datetime(2020, 7, 20, 12), + datetime.datetime(2019, 3, 5, 12), + datetime.datetime(2019, 10, 25, 12), + datetime.datetime(2014, 9, 12, 12), # was 2022-02-14: out of store bounds + # (see store-bounds guard above) + datetime.datetime(2018, 8, 8, 12), + datetime.datetime(2017, 11, 30, 12), + datetime.datetime(2016, 4, 18, 12), + datetime.datetime(2015, 12, 25, 12), +] + +n_rows = [] +for dt in DATES: + t0 = t_index(dt) + if t0 < 0 or t0 + 1 > _MAX_T: + print(f"\n{dt.date()}: t0={t0} outside store bounds [0,{_MAX_T}] " + "-> excluded (data unavailable)") + n_rows.append({"date": dt.isoformat(), "t0": t0, + "status": "OUT-OF-STORE-BOUNDS"}) + continue + p0 = fetch("mean_sea_level_pressure", f"{t0}.0.0")[0].astype(np.float64) + p1 = fetch("mean_sea_level_pressure", f"{t0+1}.0.0")[0].astype(np.float64) + u10 = fetch("10m_u_component_of_wind", f"{t0}.0.0")[0].astype(np.float64) + v10 = fetch("10m_v_component_of_wind", f"{t0}.0.0")[0].astype(np.float64) + zeta10 = d_dx(v10) - d_dy(u10) + + found0 = find_center(p0) + row = {"date": dt.isoformat(), "t0": t0} + if found0 is None: + row["status"] = "NO-CENTER-FOUND" + n_rows.append(row) + print(f"\n{dt.date()}: no NH low found -> excluded") + continue + ci0, cj0 = found0 + la_a, lo_a = subgrid_min(p0, ci0, cj0) + found1 = find_center(p1, near=(la_a, lo_a)) + row.update({"center_lat": la_a, "center_lon": lo_a, + "anomaly_Pa": float(p0[ci0, cj0] - p0[ci0].mean())}) + if found1 is None: + row["status"] = "NOT-TRACKABLE" + n_rows.append(row) + print(f"\n{dt.date()}: center ({la_a:.2f}N,{lo_a:.2f}E) " + "not trackable at t+6h -> excluded from N1-N5") + continue + ci1, cj1 = found1 + dx, dy, _, _ = geom_ll(la_a, lo_a) + disp = (float(dx[ci1, cj1]), float(dy[ci1, cj1])) + dist = float(np.hypot(*disp)) + mth = float(np.arctan2(disp[1], disp[0])) + trackable = dist >= 100.0 + + dA = decompose_ll(p0, la_a, lo_a) + la_c, lo_c = centroid_ll(zeta10, ci0, cj0) + dC = decompose_ll(p0, la_c, lo_c) + dland = decompose_ll(lsm.astype(np.float64), la_a, lo_a) + + errA = err_deg(dA["low_pole_rad"], mth) + errC = err_deg(dC["low_pole_rad"], mth) + row.update({ + "status": "OK" if trackable else "SUBTHRESHOLD-DISPLACEMENT", + "displacement_km": dist, "motion_bearing_deg": float(np.rad2deg(mth)), + "CT_E1_wn1_frac": dA["wn1_frac"], "CT_E2_trackable": bool(trackable), + "CT_E3_error_A_mslp_deg": errA, "CT_E4_R2_profile_wn1": dA["R2_profile_wn1"], + "F8_error_C_vort_deg": errC, + "F8_shrinks_at_vort_center": bool(abs(errC) < abs(errA)), + }) + del dland # decompose_ll's own amp_by_ring omitted b1; land dipole recomputed below + _, _, r_, th_ = geom_ll(la_a, lo_a) + disk = r_ <= R_DISK + lv, rr, tt = lsm[disk].astype(np.float64), r_[disk], th_[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + a1l = np.zeros(nb) + b1l = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + vv, t = lv[m], tt[m] + pr = vv.mean() + a1l[b] = 2 * ((vv - pr) * np.cos(t)).mean() + b1l[b] = 2 * ((vv - pr) * np.sin(t)).mean() + land_dipole_amp = float(np.hypot(a1l, b1l).mean()) + row["F9_land_dipole_amp_mean"] = land_dipole_amp + row["F9_unexplained_residual"] = 1.0 - dA["R2_profile_wn1"] + + n_rows.append(row) + print(f"\n{dt.date()}: center ({la_a:.2f}N,{lo_a:.2f}E), " + f"disp {dist:.0f} km/6h, trackable={trackable}") + print(f" E1 wn1_frac={dA['wn1_frac']:.3f} E4 R2={dA['R2_profile_wn1']:.3f} " + f"E3 error(A)={errA:+.1f} deg F8 error(C)={errC:+.1f} deg " + f"({'shrinks' if abs(errC) < abs(errA) else 'grows'})") + print(f" F9 land_dipole_amp={land_dipole_amp:.4f} " + f"unexplained_residual={row['F9_unexplained_residual']:.3f}") + +valid = [r for r in n_rows if r.get("status") == "OK"] +excluded = [r for r in n_rows if r.get("status") != "OK"] +print(f"\n{len(valid)}/{len(n_rows)} storms valid (CT-E2 trackable); " + f"{len(excluded)} excluded: " + + ", ".join(f"{r['date'][:10]}={r['status']}" for r in excluded)) + +if valid: + errs = np.array([r["CT_E3_error_A_mslp_deg"] for r in valid]) + neg_frac = float((errs < 0).mean()) + n1_pass = neg_frac >= 0.70 + med_abs_err = float(np.median(np.abs(errs))) + iqr = [float(np.percentile(np.abs(errs), 25)), + float(np.percentile(np.abs(errs), 75))] + wn1s = np.array([r["CT_E1_wn1_frac"] for r in valid]) + n3_pass = bool(np.median(wn1s) >= 0.40) + r2s = np.array([r["CT_E4_R2_profile_wn1"] for r in valid]) + n4_pass = bool(np.median(r2s) >= 0.80) + shrink = np.array([r["F8_shrinks_at_vort_center"] for r in valid]) + n5_pass = bool(shrink.mean() >= 0.70) + lda = np.array([r["F9_land_dipole_amp_mean"] for r in valid]) + resid = np.array([r["F9_unexplained_residual"] for r in valid]) + f9_corr = float(np.corrcoef(lda, resid)[0, 1]) if len(valid) >= 3 else None + + print(f"\nCT-N1 sign consistency: {neg_frac:.2f} negative " + f"({int((errs<0).sum())}/{len(valid)}) -> " + f"{'PASS' if n1_pass else 'FAIL'} (bar >= 0.70)") + print(f"CT-N2 magnitude: median|error|={med_abs_err:.1f} deg, " + f"IQR=[{iqr[0]:.1f},{iqr[1]:.1f}] (no bar, observation)") + print(f"CT-N3 wn1 dominance at scale: median={np.median(wn1s):.3f} -> " + f"{'PASS' if n3_pass else 'FAIL'} (bar >= 0.40)") + print(f"CT-N4 R2 at scale: median={np.median(r2s):.3f} -> " + f"{'PASS' if n4_pass else 'FAIL'} (bar >= 0.80)") + print(f"CT-N5/F8 vort-center shrinks error: {shrink.mean():.2f} " + f"({int(shrink.sum())}/{len(valid)}) -> " + f"{'PASS' if n5_pass else 'FAIL'} (bar >= 0.70)") + print(f"CT-F9 corr(land_dipole_amp, unexplained_residual) = " + f"{f9_corr if f9_corr is not None else 'n/a (n<3)'} " + f"-> {'supports candidate 2 residually' if (f9_corr or 0) > 0.2 else 'does not support / inconclusive'}") + + out["CT_N"] = {"n_dates_scanned": len(DATES), "n_valid": len(valid), + "n_excluded": len(excluded), "excluded": excluded, + "rows": valid, + "CT_N1_sign_frac_negative": neg_frac, "CT_N1_pass": n1_pass, + "CT_N2_median_abs_error_deg": med_abs_err, "CT_N2_iqr_deg": iqr, + "CT_N3_median_wn1_frac": float(np.median(wn1s)), "CT_N3_pass": n3_pass, + "CT_N4_median_R2": float(np.median(r2s)), "CT_N4_pass": n4_pass, + "CT_N5_F8_shrink_frac": float(shrink.mean()), "CT_N5_pass": n5_pass, + "CT_F9_corr_land_dipole_vs_residual": f9_corr} +else: + out["CT_N"] = {"n_dates_scanned": len(DATES), "n_valid": 0, + "excluded": excluded, "verdict": "NO-VERDICT: zero valid storms"} + +json.dump(out, open("comet_tail_f5_n10.json", "w"), indent=2) +print("\nwrote comet_tail_f5_n10.json") From e24ecf3d1ce96dcf81f842b427997060b3301319 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 21:08:13 +0000 Subject: [PATCH 09/23] probe(weather): CT-F10/F11/F13 -- a second independent sample reverses the n=10 verdict, proper stats keep it borderline, the regime hypothesis fails its own check while the apparatus hypothesis strengthens MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CT-F13 RAW REPLICATION on a fresh, mechanically-generated sample (fixed start 1980-02-10 + fixed 411-day stride x 15 candidates, chosen before any code ran, landing entirely in 1980-1995, zero overlap with the 2015-2021 sample): 8/10 = 0.80 sign-consistency, REVERSING the first sample's 6/10=0.60 FAIL on its own 0.70 bar. 5/15 candidates (33%) failed CT-E2 trackability this time vs 0/10 before -- 4 of those 5 cluster at 26-33N,67-134E in Jun-Sep, the same monsoon-season geography already flagged twice (F7's land storm, N's worst storm). PROPER STATISTICS, not just the pass/fail bar: neither sample clears a conventional two-sided 0.05 alone (sample1 p=0.754, sample2 p=0.109). Pooled across BOTH fully independent samples: 14/20=0.70, one-sided p~=0.058 -- landing almost exactly on the pre-set 0.70 bar by coincidence, still short of significance. The reversal is real; it converts a FAIL into a STILL-BORDERLINE with double the sample. THE TWO COMPETING EXPLANATIONS FROM THE PRIOR ENTRY WERE BOTH CHECKED, and the answer INVERTED which one looks better supported: - Regime-contamination (favoured after the first n=10 sample): checked by retroactively applying wn1_frac>=0.40 to SAMPLE 1's own data. Removes its two lowest-structure storms (2020-07-20, 2019-10-25) -- BOTH were negative-signed, agreeing with the prediction. Removing two hits, not two misses, drops sample1 from 6/10 (0.60) to 4/8 = EXACTLY 0.500. Does not survive contact with its own predicted mechanism. - Apparatus (motion-bearing noise on slow storms): pooling the displacement>=250km/6h subset from BOTH independent samples (n=4+3=7, two decades) gives 6/7=0.857, one-sided p~=0.0625 -- the single most consistent number in the whole chain (n=7 still small; this pooling was NOT itself pre-registered before either sample ran, stated plainly). CT-F10 (displacement filter) on sample2 alone: NO-VERDICT, n=3 below the pre-registered minimum of 6 -- correctly withheld rather than reported as a misleading 3/3=1.00. CT-F11 (regime filter) on sample2 alone: 8/9=0.89 PASSES -- but this is the number that does NOT survive the cross-sample check above. Report updated: §5.10 new section with the full statistical treatment, both executive-summary caveats layered (not replaced), next-falsifiers list closed out through F10/F11/F13 with CT-F14 (a single properly-powered n>=25-30 displacement-filtered-ONLY sample) as the correctly-scoped next step, CT-F15 (geo-fence the monsoon band) as a secondary lead. Closing status section rewritten to state the full non-monotone arc honestly: dead(F3) -> alive with error bar(F4) -> apparently general(first N run misread in isolation) -> reversed(second sample) -> converging on real-for-fast/undetermined-for-slow. Board: EPIPHANIES prepend E-THE-RESCUE-THAT-WEAKENED-ITSELF-UNDER-SCRUTINY-1 -- the reusable lesson: apply the SAME scrutiny to a result that helps your preferred story as to one that hurts it. The regime-contamination hypothesis was mine, felt right after the prior entry, and did not survive being checked against the data that was supposed to support it -- reported as a weakening of my own prior explanation, not smoothed into a win. Co-Authored-By: Claude Sonnet 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 59 +++++ probes/weather-p1/COMET_TAIL_REPORT.md | 188 ++++++++++++--- probes/weather-p1/comet_tail_f10_f11.json | 207 ++++++++++++++++ probes/weather-p1/comet_tail_f10_f11.py | 273 ++++++++++++++++++++++ 4 files changed, 692 insertions(+), 35 deletions(-) create mode 100644 probes/weather-p1/comet_tail_f10_f11.json create mode 100644 probes/weather-p1/comet_tail_f10_f11.py diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index d89bb414..37f73d5d 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,62 @@ +## 2026-08-11 — E-THE-RESCUE-THAT-WEAKENED-ITSELF-UNDER-SCRUTINY-1 + +**Status:** FINDING `[G]` — CT-F10/F11/F13, `comet_tail_f10_f11.py` / `.json`, +report §5.10. Direct follow-up to E-N-EQUALS-TWO-... below; the discipline +this entry demonstrates is the point as much as the numbers. + +**A second, fully independent blind sample REVERSED the previous sample's +verdict on its own bar — 8/10 = 0.80 (vs the first sample's 6/10 = 0.60) on +15 mechanically-generated candidate dates (fixed start + fixed 411-day +stride, chosen before any code ran, landing entirely in 1980–1995, zero +overlap with the 2015–2021 sample).** This is exactly the moment a session is +tempted to declare victory. Instead: proper statistics first. Neither sample +clears a conventional two-sided 0.05 alone (sample 1 p=0.754; sample 2 +p=0.109). **Pooled across both fully independent samples: 14/20 = 0.70, +one-sided p≈0.058 — landing almost exactly on the pre-set 0.70 bar by +coincidence, and still short of significance.** The reversal is real, but it +does not resolve anything on its own; it converts a FAIL into a +STILL-BORDERLINE with a doubled sample size. + +**Then the two competing explanations from the previous entry were BOTH +checked against the new evidence — and the answer inverted which one looked +better supported.** §5.9 (previous) favored regime-contamination (a monsoon +low corrupting one sample) as the likely driver of the sign-consistency gap. +Checked directly: retroactively applying the wn1_frac≥0.40 regime filter to +**sample 1's own data** removes its two lowest-structure storms +(2020-07-20, 2019-10-25) — and **both were negative-signed, agreeing with +the prediction.** Removing two hits, not two misses, drops sample 1 from +6/10 (0.60) to **4/8 = exactly 0.500.** The regime explanation does not +survive contact with its own predicted mechanism — checked, not assumed, and +reported as a weakening even though it was MY preferred explanation from the +prior entry. + +**Meanwhile the apparatus explanation (motion-bearing noise on slow storms) +strengthened under the same treatment.** Pooling the displacement≥250km/6h +subset from BOTH independent samples (n=4+3=7, spanning two decades) gives +**6/7 = 0.857, one-sided p≈0.0625** — the single most consistent number in +the entire chain, though n=7 keeps it suggestive rather than decisive, and +this pooling was not itself pre-registered before either sample ran (stated +plainly). An unplanned bonus: 4 of 5 storms excluded from the second sample +by the pre-existing trackability gate cluster at 26–33°N/67–134°E in +June–September — the exact monsoon-season geography flagged as +contamination-prone twice before (§5.7's blind land storm, §5.9's worst +storm) — showing the trackability gate is already doing real filtering work +for free, on a completely different axis than the wn1_frac regime filter. + +**The reusable lesson, and why this entry exists separately from the last +one:** *when a follow-up result HELPS your preferred story, run the same +scrutiny you'd run if it hurt it — apply the retroactive check, do the +statistics, report the weakening if the mechanism doesn't survive contact +with its own prediction.* This session's regime-contamination hypothesis was +mine, felt right after §5.9, and did not survive being checked against the +data that was supposed to support it. The number that DID hold up (apparatus, +6/7) is not the flashier "we found the real mechanism" story — it is the +more mundane "the label itself is noisy when storms move slowly" story, and +it earned its position by surviving a check the flashier story failed. +CT-F14 (a single properly-powered displacement-filtered sample, n≥25–30) is +now the correctly-scoped next step — not a third exploratory rerun of a +sample that already ran twice. + ## 2026-08-11 — E-N-EQUALS-TWO-LOOKED-LIKE-PHYSICS-AND-WAS-HALF-COIN-FLIP-1 **Status:** FINDING `[G]` — CT-F5 + CT-N, `comet_tail_f5_n10.py` / `.json`, diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index f18b82d6..c5501ff9 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -33,6 +33,20 @@ generalization beyond n=2. > as a general rule. §5.9 has the honest breakdown, including a post-hoc lead > (motion-bearing noise at low displacement) that may explain part of the gap > and is *not* used to override the failed bar. +> +> **⚠⚠ UPDATE 2026-08-11 (§5.10, a SECOND independent sample).** A fresh, +> mechanically-generated 1980–1995 sample scores 8/10 = 0.80 unfiltered — +> reversing §5.9's verdict on its own bar. Proper statistics keep this +> **borderline, not resolved**: neither sample alone clears a two-sided 0.05, +> and the combined pooled figure (14/20) lands right at the noise floor +> (one-sided p ≈ 0.058). The one number that *does* strengthen — pooling both +> samples' large-displacement storms, 6/7 = 0.857 — supports an **apparatus** +> explanation (motion-bearing noise on slow-moving storms) over the +> **regime-contamination** explanation §5.9 initially favored: checked +> directly, the regime filter does NOT rescue sample 1 (drops it to exactly +> chance, 0.500). Read §4/§5.9's "not established" verdict as still current, +> now with a specific, falsifiable next step (CT-F14) rather than a closed +> question. Product consequence `[S]`: a storm's pressure field compresses to **center position + ~12 ring means + one dipole vector** at 93–97 % variance @@ -465,6 +479,88 @@ is fitted or applied here. The properly pre-registered next step is a displacement-and/or-regime-filtered n ≥ 10 sample designed *in advance* to test candidate (a) and (b) separately, not a re-scoring of this one. +### 5.10 CT-F10/F11/F13 — a second independent sample — **borderline, in a specific and honest way** + +`comet_tail_f10_f11.py` / `.json`. §5.9 named two post-hoc leads and filed +them as pre-registered reruns rather than a re-scoring. This is that rerun, +on a **fresh, mechanically-generated** date set — fixed start (1980-02-10) ++ fixed stride (411 days, chosen for no reason tied to any outcome) × +15 candidates, landing entirely in **1980–1995**, zero overlap with the +2015–2021 sample. The stride was picked once, before any code ran that could +see a result; the dates were never inspected before recording. + +**Attrition was real and different this time:** 5 of 15 candidates (33 %) +failed CT-E2 trackability — versus 0 of 10 in the first sample. **4 of those +5** cluster at 26–33°N, 67–134°E in June–September — the same South/East-Asian +monsoon-season geography already flagged as contamination-prone (§5.9's worst +storm at 32°N/85°E; §5.7's blind land storm at 28.5°N/68°E). The 5th +(1995-11-12, 54°N/161°E, November) sits outside that band and is an ordinary +mid-latitude exclusion. This is a genuine, unplanned, cross-sample-consistent +pattern about **where blind NH-wide selection breaks**, not yet exploited by +any filter. + +| test | n | sign-neg fraction | median \|error\| | one-sided p (H₀: p=0.5) | verdict | +|---|---:|---:|---:|---:|---| +| **CT-F13** (raw, unfiltered — direct replication check) | 10 | **0.80** (8/10) | 41.3° | 0.055 | **PASS** (≥0.70 bar) | +| CT-F10 (disp ≥ 250 km) | 3 | 1.00 (3/3) | — | — | **NO-VERDICT** (n<6, pre-registered minimum) | +| **CT-F11** (wn1_frac ≥ 0.40) | 9 | **0.89** (8/9) | 41.9° | — | **PASS** | +| CT-F12 (both filters) | 3 | 1.00 (3/3) | — | — | reported only, n too small | + +**The headline number reverses §5.9's verdict on its own bar** — 0.80 vs the +first sample's 0.60. That reversal is exactly why it needs more scrutiny, not +less, and the honest statistics do not let it stand alone: + +- **Neither sample is significant on its own** at a conventional two-sided + 0.05: sample 1 (6/10) two-sided p = 0.754; sample 2 (8/10) two-sided + p = 0.109. One-sided (the physically motivated direction, pre-registered + from §2 onward) sample 2 gives p = 0.055 — genuinely borderline, not a + clean win. +- **Combined across both fully independent samples: 14/20 = 0.70, + one-sided p = 0.058.** Still borderline. Landing exactly on the 0.70 bar is + a coincidence worth naming, not a rescue: the bar was picked before either + sample ran. +- **The regime filter (CT-F11) does NOT rescue sample 1 — checked, and it + makes things worse.** Applying wn1_frac ≥ 0.40 retroactively to sample 1 + removes 2020-07-20 and 2019-10-25, **both of which were negative-signed** + (−39.0° and −107.6°). Removing two "hits" drops sample 1 from 6/10 (0.60) + to **4/8 = 0.500 — exactly chance.** This directly contradicts the + regime-contamination story as an explanation for sample 1's specific FAIL: + its two lowest-wn1_frac storms happened to agree with the prediction, not + disagree with it. Recorded as measured, not smoothed into the flattering + reading. +- **The strongest single number in this whole probe chain is a post-hoc + combination, clearly labeled as such — not pre-registered:** pooling the + displacement ≥ 250 km subsets from *both* independent samples (n=4 + n=3 = + 7, spanning two different decades) gives **6/7 = 0.857 negative, + one-sided p = 0.0625.** Displacement is a *mechanism-motivated* filter + (it bounds motion-bearing noise, §5.9), not an outcome-based one, which is + why this number carries more weight than F13's raw 0.80 despite being + numerically less extreme — but n=7 is still small and this combination + was not itself pre-registered before either sample ran. + +**Net read, stated as precisely as the evidence allows:** the signed +left-of-motion claim is **not dead** (§5.9 was right to fail CT-N1 as +written, and this rerun does not overturn that FAIL under its own bar with +proper statistics) but it is **not established** either. What *does* survive +scrutiny is a consistent pattern across two independent decades: **storms +with well-determined motion (large 6h displacement) show a much stronger, +borderline-significant left-of-motion signature (6/7 combined) than storms +with poorly-determined motion or unfiltered blind selection (14/20, exactly +at the noise floor).** That is evidence *for* the apparatus explanation (§5.9 +candidate (a), motion-bearing noise) and *against* the regime explanation +(§5.9 candidate (b), storm-type contamination) as the dominant driver — the +opposite weighting from what seemed most plausible after §5.9 alone, and +worth stating exactly because it cuts against the tidier story. + +**CT-F14 (new, not run):** a properly powered (n ≥ 25–30), pre-registered, +displacement-filtered-ONLY sample — the single test that would move the +combined 6/7 either toward significance or back to noise. This is now the +correctly-scoped next step, not a third exploratory rerun. +**CT-F15 (new, not run):** geo-fence the blind selection away from +26–33°N/67–134°E in June–September (or add an explicit baroclinicity +proxy at intake) and re-check whether CT-E2's own trackability gate keeps +doing this filtering job for free, as the exclusion pattern above suggests. + ### 5.4 Where that leaves the three candidates > **Read this table as a within-storm-1/2 candidate ranking for the OFFSET @@ -509,21 +605,33 @@ this quantity. No stronger attribution is claimed at n = 2. n ≥ 10-with-regime-control treatment CT-N just showed is necessary. - **CT-F9 (Ekman-pumping mechanism)** — **RUN, §5.9.** corr = −0.295, does not support the residual-correlation pathway for candidate 2. -- **CT-F10 (new, from §5.9's stratification, NOT yet run):** a **pre-registered** - displacement-filtered rerun of CT-N1 — restrict blind selection to storms - with ≥ 250 km/6h displacement a priori (not post-hoc), n ≥ 10 within that - filter, to test whether motion-bearing noise at low displacement explains - the sign-consistency gap. This is the correct way to use §5.9's lead; simply - re-scoring the existing 10 storms is not. -- **CT-F11 (new, from §5.9):** regime-filter the blind selection (minimum - wn1_frac or R² threshold at intake) to exclude non-baroclinic systems like - the 2020-07-20 monsoon-season case, and test CT-N1 again on that - sub-population. +- ~~**CT-F10** (displacement-filtered rerun)~~ — **RUN, §5.10.** NO-VERDICT + standalone (n=3, below the pre-registered minimum) — but pooled with + sample 1's own displacement-filtered subset, 6/7 = 0.857, the strongest + number in the chain (still small-n, still not pre-registered as a pooled + test). +- ~~**CT-F11** (regime-filtered rerun)~~ — **RUN, §5.10.** Passes on sample 2 + (8/9), but **checked directly against sample 1 and found NOT to rescue + it** — retroactively applied, it drops sample 1 to exactly 0.500. The + regime-contamination explanation is weaker evidence than it looked after + §5.9 alone. +- **CT-F13 (raw replication, RUN as part of §5.10):** 8/10 on an independent + sample, reversing §5.9's verdict on its own bar — but not significant on + proper statistics (two-sided p=0.109), and the pooled combined figure with + sample 1 (14/20) sits right at the noise floor. +- **CT-F14 (new, from §5.10, NOT yet run):** the correctly-scoped next step — + a single, properly powered (n ≥ 25–30) **displacement-filtered-only** + pre-registered sample, since displacement is now the mechanism-motivated + filter with the strongest (if still small-n) support. +- **CT-F15 (new, from §5.10, NOT yet run):** geo-fence blind selection away + from 26–33°N/67–134°E in June–September (4 of 5 exclusions in the second + sample clustered there) and check whether this simply reproduces what + CT-E2's trackability gate is already doing for free. - **Sample size:** the apparatus (CT-F4/F5) is good enough that an offset - constant COULD be fitted for storms in the large-displacement, - clearly-baroclinic regime — but §5.9 means that regime must be - characterized and pre-registered (CT-F10/F11) before any constant is - fitted, not just before a predictor ships. + constant COULD be fitted for storms in the large-displacement regime — but + §5.10 means even that regime is only *suggestively* supported (n=7, p≈0.06 + pooled) and CT-F14 is the gate before any constant is fitted, not general + n ≥ 10 in the abstract. --- @@ -583,28 +691,38 @@ storm ≈ CENTER (place) — 1 address storms in the regime storms 1–2 sit in*. **CT-F5** (§5.8) fixed the remaining saturation defect. **CT-F7** (§5.7) bounded and re-scoped candidate 2 (friction rotates wind, not the pressure dipole). -5. ~~**n ≥ 10 storm sample**~~ — **RUN, §5.9. THIS IS THE HEADLINE FINDING.** - The apparatus was never the real ceiling — the offset's **sign** does not - generalize past storms 1–2 (6/10, indistinguishable from chance), while - its magnitude/explanatory-power claims do. CT-F8's wind-center - generalization and CT-F9's Ekman-pumping mechanism were also tested here: - neither holds up at scale. -6. **CT-F10 / CT-F11 (pre-registered, NOT run):** a displacement-filtered - and a regime-filtered rerun, respectively — the honest way to chase §5.9's - post-hoc leads without re-scoring the sample that already failed. -7. Adversarial audit gate (plan §8) before any of it is promoted to EV / +5. ~~**n ≥ 10 storm sample**~~ — **RUN, §5.9.** The offset's **sign** does not + generalize on that first blind sample (6/10, indistinguishable from + chance), while its magnitude/explanatory-power claims do. CT-F8's + wind-center generalization and CT-F9's Ekman-pumping mechanism also + tested here: neither holds up at scale. +6. ~~**CT-F10 / CT-F11 / CT-F13**~~ — **RUN, §5.10. A second independent + sample, borderline in a specific direction.** A fresh 1980–1995 sample + scores 8/10 unfiltered — but proper statistics keep the combined figure + right at the noise floor (14/20, one-sided p≈0.058), and the + regime-filter explanation is checked and found NOT to rescue sample 1 + (drops it to exactly chance). What *does* strengthen is the + displacement-filtered pooled subset (6/7, p≈0.0625) — apparatus, not + regime, is now the better-supported explanation for the gap. +7. **CT-F14 (pre-registered, NOT run) is the correctly-scoped next step:** + a single, properly powered (n≥25–30) displacement-filtered-only sample. +8. Adversarial audit gate (plan §8) before any of it is promoted to EV / product claim. **Net effect of the full follow-up chain on the headline claim.** The §1 summary needs a real caveat now, not just a footnote: wn-1 dominance and the -R² lift **generalize** (CT-N3/N4 pass at n=10) — the signed, motion-locked -orientation that made storms 1–2 exciting **has not been shown to -generalize** (CT-N1 fails). The offset went **dead (F3) → alive with a -±3–7° error bar (F4) → real-but-not-yet-general (n=10, CT-N)**. Each step -was a genuine gate, not a rescue of the previous one — that discipline is -what makes the current honest position trustworthy: the structural claim -(comet-tail wn-1 dominance) is solid; the directional/predictive claim -(left-of-motion) is a **storm-1/2-specific finding pending a properly -pre-registered generalization test**, not yet a rule. §6's encoding and -single-frame-predictor work is gated behind CT-F10/F11, not just n ≥ 10 in -general. +R² lift **generalize** cleanly across two independent decades — the signed, +motion-locked orientation that made storms 1–2 exciting is **borderline**: +not established (combined 14/20 sits at the noise floor), not dead either +(the displacement-filtered pooled subset, 6/7, is the most consistent signal +in the entire chain). The offset went **dead (F3) → alive with a ±3–7° error +bar (F4) → apparently general at n=10 → reversed on n=10 more → converging +on "real for fast-moving storms, undetermined for slow ones" (n=10+10, CT-N + +CT-F10/13)**. Each step was a genuine gate, not a rescue of the previous +one — including the moment (§5.10) a result that *helped* the headline claim +got the same scrutiny as one that hurt it (the regime-filter check on +sample 1 was run and reported even though it weakened the more flattering +story). That symmetry is what makes the current position — "solid structural +finding, borderline-and-narrowing directional finding, one clean next +falsifier (CT-F14)" — trustworthy. §6's encoding and single-frame-predictor +work is gated behind CT-F14, not general n ≥ 10. diff --git a/probes/weather-p1/comet_tail_f10_f11.json b/probes/weather-p1/comet_tail_f10_f11.json new file mode 100644 index 00000000..9b47696b --- /dev/null +++ b/probes/weather-p1/comet_tail_f10_f11.json @@ -0,0 +1,207 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "sample_start": "1980-02-10T12:00:00", + "stride_days": 411, + "n_candidates": 15, + "rows": [ + { + "date": "1980-02-10T12:00:00", + "t0": 30842, + "center_lat": 46.69223787871181, + "center_lon": 321.36432542323297, + "displacement_km": 135.63575106831914, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.628856457656897, + "R2_profile_wn1": 0.8545934458405746, + "error_deg": -92.97109364728313, + "sign_negative": true + }, + { + "date": "1981-03-27T12:00:00", + "t0": 32486, + "center_lat": 48.92486050236569, + "center_lon": 345.01823101795355, + "displacement_km": 222.0045582791306, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.9045030528074844, + "R2_profile_wn1": 0.9438245000977159, + "error_deg": -20.88097918876568, + "sign_negative": true + }, + { + "date": "1982-05-12T12:00:00", + "t0": 34130, + "center_lat": 47.97766935852807, + "center_lon": 153.37771288174426, + "displacement_km": 131.92171455168986, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.5916269721362224, + "R2_profile_wn1": 0.9607952352984042, + "error_deg": -153.31708981303188, + "sign_negative": true + }, + { + "date": "1983-06-27T12:00:00", + "t0": 35774, + "center_lat": 28.9105918421428, + "center_lon": 67.83593520767262, + "displacement_km": 12.99254627435683, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "1984-08-11T12:00:00", + "t0": 37418, + "center_lat": 63.0, + "center_lon": 315.9560617272064, + "displacement_km": 118.19750326644628, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.8417670116416098, + "R2_profile_wn1": 0.9143783031954809, + "error_deg": -72.21558022018336, + "sign_negative": true + }, + { + "date": "1985-09-26T12:00:00", + "t0": 39062, + "center_lat": 29.997842013566018, + "center_lon": 284.6331381239523, + "displacement_km": 163.15273443482124, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.8481833256690069, + "R2_profile_wn1": 0.966594833549946, + "error_deg": 18.124329929345407, + "sign_negative": false + }, + { + "date": "1986-11-11T12:00:00", + "t0": 40706, + "center_lat": 55.380629609588915, + "center_lon": 312.59062947910223, + "displacement_km": 277.5031914159023, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.7596036253145539, + "R2_profile_wn1": 0.8539855095982779, + "error_deg": -41.91781393062573, + "sign_negative": true + }, + { + "date": "1987-12-27T12:00:00", + "t0": 42350, + "center_lat": 50.04223886424398, + "center_lon": 329.871606898506, + "displacement_km": 343.30238430474316, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.9222191170016778, + "R2_profile_wn1": 0.9436538419987199, + "error_deg": -17.356647012543647, + "sign_negative": true + }, + { + "date": "1989-02-10T12:00:00", + "t0": 43994, + "center_lat": 55.61303453356034, + "center_lon": 318.7445443332808, + "displacement_km": 438.4521705711391, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.8695689494523423, + "R2_profile_wn1": 0.9364385109562684, + "error_deg": -46.04671589769973, + "sign_negative": true + }, + { + "date": "1990-03-28T12:00:00", + "t0": 45638, + "center_lat": 52.53971016691554, + "center_lon": 182.66171472593436, + "displacement_km": 192.03898301009167, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.6890623749942085, + "R2_profile_wn1": 0.8866657330465724, + "error_deg": -40.64972313837734, + "sign_negative": true + }, + { + "date": "1991-05-13T12:00:00", + "t0": 47282, + "center_lat": 52.189441129488266, + "center_lon": 250.15073954493647, + "displacement_km": 134.3856569016244, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.13792706578736133, + "R2_profile_wn1": 0.7632855741063911, + "error_deg": 37.23294432626912, + "sign_negative": false + }, + { + "date": "1992-06-27T12:00:00", + "t0": 48926, + "center_lat": 30.962186946701625, + "center_lon": 85.7428667774291, + "displacement_km": 4.259276779660015, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "1993-08-12T12:00:00", + "t0": 50570, + "center_lat": 32.66686855435837, + "center_lon": 83.84407774117935, + "displacement_km": 66.4636172410943, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "1994-09-27T12:00:00", + "t0": 52214, + "center_lat": 26.30630015082043, + "center_lon": 132.4714451359455, + "displacement_km": 54.04994018738056, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "1995-11-12T12:00:00", + "t0": 53858, + "center_lat": 53.73237073051221, + "center_lon": 160.71640607645267, + "displacement_km": 35.12238805382691, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + } + ], + "n_valid": 10, + "n_excluded": 5, + "CT_F13": { + "n": 10, + "sign_neg_frac": 0.8, + "median_abs_error_deg": 41.28376853450153, + "pass": true + }, + "CT_F10": { + "n": 3, + "verdict": "NO-VERDICT-INSUFFICIENT-N" + }, + "CT_F11": { + "n": 9, + "sign_neg_frac": 0.8888888888888888, + "median_abs_error_deg": 41.91781393062573, + "pass": true + }, + "CT_F12": { + "n": 3, + "sign_neg_frac": 1.0, + "median_abs_error_deg": 41.91781393062573, + "pass": true + } +} \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py new file mode 100644 index 00000000..d4e5067a --- /dev/null +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -0,0 +1,273 @@ +"""EXPLORATORY — CT-F10 (displacement-filtered rerun) + CT-F11 (regime- +filtered rerun) + CT-F13 (raw replication check), all pre-registered. +Follow-up to comet_tail_f5_n10.py (ffa2e35d). NOT an EV; bars mine, unaudited. + +CT-N (previous probe) found sign-consistency of the left-of-motion offset +FAILED at n=10 (6/10=0.60) despite structure claims (wn1 dominance, R2) +replicating cleanly. Two post-hoc leads were named but explicitly NOT used +to override that FAIL: (a) restricting to storms with >=250 km/6h +displacement raised consistency to 3/4=0.75; (b) dropping the single most +extreme low-displacement/near-polar outlier raised it to 6/9=0.667. The +correct way to chase a post-hoc lead is a FRESH pre-registered sample with +the filter applied a priori (at least at the reporting stage) -- not a +re-scoring of the sample that already failed. This probe is that fresh +sample. + +SAMPLE INDEPENDENCE (load-bearing): dates are MECHANICALLY generated (fixed +start + fixed stride, no hand-picking) and land entirely in 1980-1997, +non-overlapping with the original 10-storm sample (2015-2021). A human did +not choose dates likely to produce a particular sign-consistency number. + +PRE-REGISTERED (computed on ONE blind draw of candidate dates; all storms +found are reported, none discarded except by the stated CT-E2 trackability +gate that was already standard before any filter is applied): + +CT-F13 RAW REPLICATION (no filter). Does the ~0.60 sign-consistency from + the first n=10 sample reproduce on an independent second blind + sample? No bar (this IS the replication test) -- report sign + fraction and median|error| for direct comparison against the first + sample's 0.60 / 40.2 deg [19.5,63.2]. + +CT-F10 DISPLACEMENT FILTER. Among valid (trackable) storms, restrict to + displacement >= 250 km/6h (the threshold that separated storms 1-2's + own regime in the post-hoc stratification). Bar: sign-consistency + >= 0.70. Minimum n=6 for a real verdict (below that: NO-VERDICT, + stated in advance -- a subset of 3-4 cannot meaningfully test a 0.70 + rate). + +CT-F11 REGIME FILTER. Among valid storms, restrict to wn1_frac >= 0.40 + (CT-E1's own dominance bar -- a storm without a dominant wn-1 mode + does not have a well-defined dipole bearing for the signed test to + apply to). Bar: sign-consistency >= 0.70, same n=6 minimum. + +CT-F12 INTERSECTION (both filters). No bar -- reported for completeness, + likely too small an n for a real verdict, stated as such. + +NOT tested here: this is n=~10-15 candidates yielding filtered subsets of +n=4-10 -- still far short of a properly powered sample for any of these +sub-questions. A PASS on CT-F10/F11 here is evidence the leads are worth a +dedicated large-n follow-up, not proof of a general rule. A FAIL closes the +lead, same as CT-N1 closed the unfiltered claim. +""" +import datetime +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 +EPOCH = datetime.datetime(1959, 1, 1) + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +def t_index(dt): + return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) + + +assert t_index(datetime.datetime(2021, 6, 15, 12)) == 91246, \ + "t-index formula does not reproduce the pinned arc anchor T0=91246" +print("t-index anchor guard: OK") + +_MSLP_SHAPE = meta["mean_sea_level_pressure/.zarray"]["shape"] +_MAX_T = _MSLP_SHAPE[0] - 1 +print(f"store bounds guard: max valid t={_MAX_T}") + +print("fetching static fields (latitude) ...", flush=True) +lat = fetch("latitude", "0").astype(np.float64).ravel() +NY = lat.size +NX = 1440 +phi = np.deg2rad(lat) +lon_deg = np.arange(NX) * 0.25 + +# ---- verbatim helpers (matches comet_tail_f5_n10.py) ----------------------- + + +def geom_ll(latc, lonc): + phic = np.deg2rad(latc) + dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) + dphi = phi[:, None] - phic + dx = R_E * np.cos(phic) * dlon * np.ones((NY, 1)) + dy = R_E * dphi * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + fa = field - field.mean(axis=1, keepdims=True) + mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) + if near is not None: + _, _, r, _ = geom_ll(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + return int(ci), int(cj) + + +def decompose_ll(field, latc, lonc): + _, _, r, th = geom_ll(latc, lonc) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + a1[b] = 2 * ((v - prof[b]) * np.cos(t)).mean() + b1[b] = 2 * ((v - prof[b]) * np.sin(t)).mean() + resid0 = vals - prof[rings] + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) + return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), + "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), + "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + + +def subgrid_min(field, ci, cj): + z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") + A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), + gx.ravel() ** 2, gy.ravel() ** 2, + (gx * gy).ravel()]) + c = np.linalg.lstsq(A, z, rcond=None)[0] + _, b, cc, d, e, g = c + H = np.array([[2 * d, g], [g, 2 * e]]) + try: + dj, di = np.linalg.solve(H, [-b, -cc]) + except np.linalg.LinAlgError: + dj = di = 0.0 + di, dj = float(np.clip(di, -1, 1)), float(np.clip(dj, -1, 1)) + lat_step = lat[ci + 1] - lat[ci] if ci + 1 < NY else lat[ci] - lat[ci - 1] + return lat[ci] + di * lat_step, lon_deg[cj] + dj * 0.25 + + +def wrap_deg(d): + return (d + 180.0) % 360.0 - 180.0 + + +def err_deg(low_pole_rad, motion_rad): + return float(wrap_deg(np.rad2deg( + low_pole_rad - (motion_rad + np.pi / 2)))) + + +# ---- fresh, mechanically-generated candidate dates ------------------------- +START = datetime.datetime(1980, 2, 10, 12) +STRIDE_DAYS = 411 # non-round, decorrelates from the annual cycle; + # drifts ~1.5 months/sample -> natural season mix +N_CANDIDATES = 15 +DATES = [START + datetime.timedelta(days=STRIDE_DAYS * i) + for i in range(N_CANDIDATES)] +print(f"\ncandidate dates ({N_CANDIDATES}, stride={STRIDE_DAYS}d, " + f"mechanically generated, non-overlapping with the 2015-2021 sample):") +for d in DATES: + print(f" {d.date()}") + +rows = [] +for dt in DATES: + t0 = t_index(dt) + if t0 < 0 or t0 + 1 > _MAX_T: + rows.append({"date": dt.isoformat(), "status": "OUT-OF-STORE-BOUNDS"}) + print(f"\n{dt.date()}: out of store bounds -> excluded") + continue + p0 = fetch("mean_sea_level_pressure", f"{t0}.0.0")[0].astype(np.float64) + p1 = fetch("mean_sea_level_pressure", f"{t0+1}.0.0")[0].astype(np.float64) + + ci0, cj0 = find_center(p0) + la_a, lo_a = subgrid_min(p0, ci0, cj0) + ci1, cj1 = find_center(p1, near=(la_a, lo_a)) + dx, dy, _, _ = geom_ll(la_a, lo_a) + disp = (float(dx[ci1, cj1]), float(dy[ci1, cj1])) + dist = float(np.hypot(*disp)) + mth = float(np.arctan2(disp[1], disp[0])) + trackable = dist >= 100.0 + + row = {"date": dt.isoformat(), "t0": t0, + "center_lat": la_a, "center_lon": lo_a, + "displacement_km": dist, "CT_E2_trackable": bool(trackable)} + if not trackable: + row["status"] = "NOT-TRACKABLE" + rows.append(row) + print(f"\n{dt.date()}: center ({la_a:.2f}N,{lo_a:.2f}E) " + f"disp={dist:.0f}km -> not trackable, excluded") + continue + + d = decompose_ll(p0, la_a, lo_a) + err = err_deg(d["low_pole_rad"], mth) + row.update({"status": "OK", "wn1_frac": d["wn1_frac"], + "R2_profile_wn1": d["R2_profile_wn1"], "error_deg": err, + "sign_negative": bool(err < 0)}) + rows.append(row) + print(f"\n{dt.date()}: center ({la_a:.2f}N,{lo_a:.2f}E), " + f"disp={dist:.0f}km, wn1_frac={d['wn1_frac']:.3f}, " + f"R2={d['R2_profile_wn1']:.3f}, error={err:+.1f} deg " + f"({'neg' if err < 0 else 'pos'})") + +valid = [r for r in rows if r.get("status") == "OK"] +excluded = [r for r in rows if r.get("status") != "OK"] +print(f"\n{len(valid)}/{len(rows)} storms valid; {len(excluded)} excluded: " + + ", ".join(f"{r['date'][:10]}={r['status']}" for r in excluded)) + + +def stratum_verdict(name, subset, min_n=6, bar=0.70): + if len(subset) < min_n: + print(f"\n{name}: n={len(subset)} < min_n={min_n} -> NO-VERDICT " + "(pre-registered minimum, stated before results)") + return {"n": len(subset), "verdict": "NO-VERDICT-INSUFFICIENT-N"} + errs = np.array([r["error_deg"] for r in subset]) + neg = float((errs < 0).mean()) + med_abs = float(np.median(np.abs(errs))) + passed = neg >= bar + print(f"\n{name}: n={len(subset)}, sign_neg_frac={neg:.2f} " + f"({int((errs<0).sum())}/{len(subset)}), median|error|={med_abs:.1f} " + f"deg -> {'PASS' if passed else 'FAIL'} (bar >= {bar})") + return {"n": len(subset), "sign_neg_frac": neg, + "median_abs_error_deg": med_abs, "pass": bool(passed)} + + +out = {"store": B, "sample_start": START.isoformat(), + "stride_days": STRIDE_DAYS, "n_candidates": N_CANDIDATES, + "rows": rows, "n_valid": len(valid), "n_excluded": len(excluded)} + +print("\n=== CT-F13 raw replication (no filter) ===") +out["CT_F13"] = stratum_verdict( + "CT-F13 (all valid, unfiltered)", valid, min_n=1, bar=0.70) +if valid: + print(" (compare against the first sample: 0.60 sign_neg_frac, " + "median|error|=40.2 deg)") + +print("\n=== CT-F10 displacement filter (>=250 km/6h) ===") +disp_subset = [r for r in valid if r["displacement_km"] >= 250.0] +out["CT_F10"] = stratum_verdict("CT-F10 (disp>=250km)", disp_subset) + +print("\n=== CT-F11 regime filter (wn1_frac >= 0.40) ===") +regime_subset = [r for r in valid if r["wn1_frac"] >= 0.40] +out["CT_F11"] = stratum_verdict("CT-F11 (wn1_frac>=0.40)", regime_subset) + +print("\n=== CT-F12 intersection (both filters, exploratory, no bar) ===") +both_subset = [r for r in valid + if r["displacement_km"] >= 250.0 and r["wn1_frac"] >= 0.40] +out["CT_F12"] = stratum_verdict("CT-F12 (both filters)", both_subset, + min_n=1) # report regardless; n likely small + +json.dump(out, open("comet_tail_f10_f11.json", "w"), indent=2) +print("\nwrote comet_tail_f10_f11.json") From 4f1a1b4f63c801b9b861e6108c3e718936f2c4fb Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 21:33:48 +0000 Subject: [PATCH 10/23] probe(weather): CT-F14 pre-registration -- displacement-filtered-only n>=20-30 sample, committed before running The correctly-scoped next step named in COMET_TAIL_REPORT.md sec 5.10: a SINGLE properly powered test of the displacement>=250km/6h subset, since the regime-contamination explanation was checked and failed against sample1's own data while the apparatus explanation (pooled disp>=250 across both prior samples, 6/7=0.857, p~=0.0625) is the one with surviving support. Fresh mechanically-generated dates (start 1996-01-15, stride=61d x 85 candidates, landing 1996-2014), zero overlap with sample1 (2015-2021, hand-picked) or sample2 (1980-1995, stride=411d). N_CANDIDATES sized from the two prior samples' combined empirical qualifying rate (7/25~=0.28) to target n>=20-30 -- an estimate, not a guarantee; actual attrition reported however it lands. Pre-committed interpretation of the COMBINED three-independent-sample p-value (decided now, before running, so the read cannot be tuned to the result): p<0.05 established/audit-gate-ready; 0.05<=p<0.10 suggestive, needs more n; p>=0.10 not established, apparatus explanation itself in question, retire directional claim to [S]. Committed before execution per the arc's pre-registration discipline. Result to follow in a separate commit. Co-Authored-By: Claude Sonnet 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/comet_tail_f14.py | 325 ++++++++++++++++++++++++++++ 1 file changed, 325 insertions(+) create mode 100644 probes/weather-p1/comet_tail_f14.py diff --git a/probes/weather-p1/comet_tail_f14.py b/probes/weather-p1/comet_tail_f14.py new file mode 100644 index 00000000..538eed39 --- /dev/null +++ b/probes/weather-p1/comet_tail_f14.py @@ -0,0 +1,325 @@ +"""EXPLORATORY — CT-F14, the correctly-scoped next step named in §5.10: a +SINGLE, properly powered, displacement-filtered-ONLY pre-registered sample. +Follow-up to comet_tail_f10_f11.py (e24ecf3d). NOT an EV; bars mine, +unaudited. + +WHY THIS IS THE RIGHT TEST NOW (not a third exploratory rerun). Two prior +independent 10-storm samples gave a combined 14/20 unfiltered (one-sided +p~=0.058, noise floor) but a combined 6/7 among storms with >=250 km/6h +displacement (one-sided p~=0.0625) -- suggestive but n=7 is too small to +call, and that pooling was NOT itself pre-registered before either sample +ran. The regime-contamination explanation was checked directly against +sample 1's own data and did NOT survive (dropped sample 1 to exactly +chance). The apparatus explanation (motion-bearing noise on slow-moving +storms) is the one with surviving support. CT-F14 is the single test that +moves that number from "suggestive, n=7" to a real verdict: ONE +pre-registered displacement-filtered-only sample, sized for n>=20-30 +qualifying storms. + +SAMPLE INDEPENDENCE (load-bearing, third time in this chain): dates are +MECHANICALLY generated (fixed start + fixed stride, chosen before this file +was written and never adjusted after seeing output) and land in 1996-2014 -- +zero overlap with sample 1 (2015-2021, hand-picked) or sample 2 +(1980-1995, stride=411d from 1980-02-10). Expected qualifying rate is +estimated from the TWO PRIOR samples' combined empirical rate (7 qualifying +storms out of 25 total candidates tried = 0.28/candidate) to size N_CANDIDATES +for the sample -- an estimate, not a guarantee; actual attrition is reported +however it lands, never resampled to hit a target. + +PRE-REGISTERED, single test: + +CT-F14 Among valid (CT-E2 trackable) storms in this fresh sample with + displacement >= 250 km/6h, sign-consistency (fraction with error<0, + the same direction as storms 1-2 and both prior samples' filtered + subsets) >= 0.70. Minimum n=20 for a real verdict (below that: + NO-VERDICT, stated in advance -- consistent with the n=6 floor used + for smaller strata in the prior probe, scaled up for a properly + powered single test). + +REPORTED ALONGSIDE (not additional bars, just always-computed descriptive +stats + the decision rule pre-committed for how this updates the arc's +overall verdict): + + - exact one-sided binomial p-value (H0: p=0.5) for CT-F14's own subset + - THE COMBINED three-independent-sample figure: CT-F14's qualifying + storms + the two prior samples' displacement>=250 subsets (n=4+3=7), + with its own one-sided binomial p-value + - median wn1_frac / R2_profile_wn1 among the qualifying subset, for + comparison against N3/N4's already-established generalization + - PRE-COMMITTED interpretation of the combined 3-sample p-value (decided + now, before running, so the read cannot be tuned to the result): + p < 0.05 -> established at this n, displacement-filtered + regime; ready for the CT-F10..F15 chain to close + and the finding to enter the audit-gate queue + as an [H]-graded, scope-limited claim + 0.05 <= p < 0.10 -> still suggestive; needs a further doubling of n + before promotion, not yet audit-gate-ready + p >= 0.10 -> NOT established; the apparatus explanation is + itself now in question and the offset's + directional claim should be retired to [S] + pending a fundamentally different design (e.g. + modeling motion-bearing uncertainty explicitly + rather than a hard displacement cutoff) + +NOT tested here: this remains a MSLP-only, NH-only, one-season-mix test; no +claim about SH, no claim about tropical cyclones, no claim beyond the +extratropical-low steering-flow framing §2 laid out from the start. +""" +import datetime +import json +import urllib.request +from math import comb + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +R_E = 6371.0 +R_DISK = 1200.0 +RING = 100.0 +EPOCH = datetime.datetime(1959, 1, 1) + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +def t_index(dt): + return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) + + +assert t_index(datetime.datetime(2021, 6, 15, 12)) == 91246, \ + "t-index formula does not reproduce the pinned arc anchor T0=91246" +print("t-index anchor guard: OK", flush=True) + +_MSLP_SHAPE = meta["mean_sea_level_pressure/.zarray"]["shape"] +_MAX_T = _MSLP_SHAPE[0] - 1 +print(f"store bounds guard: max valid t={_MAX_T}", flush=True) + +print("fetching static fields (latitude) ...", flush=True) +lat = fetch("latitude", "0").astype(np.float64).ravel() +NY = lat.size +NX = 1440 +phi = np.deg2rad(lat) +lon_deg = np.arange(NX) * 0.25 + +# ---- verbatim helpers (matches comet_tail_f10_f11.py / f5_n10.py) --------- + + +def geom_ll(latc, lonc): + phic = np.deg2rad(latc) + dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) + dphi = phi[:, None] - phic + dx = R_E * np.cos(phic) * dlon * np.ones((NY, 1)) + dy = R_E * dphi * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + fa = field - field.mean(axis=1, keepdims=True) + mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) + if near is not None: + _, _, r, _ = geom_ll(*near) + mask = mask & (r < radius_km) + ci, cj = np.unravel_index( + np.argmin(np.where(mask, fa, np.inf)), field.shape) + return int(ci), int(cj) + + +def decompose_ll(field, latc, lonc): + _, _, r, th = geom_ll(latc, lonc) + disk = r <= R_DISK + vals, rr, tt = field[disk], r[disk], th[disk] + rings = np.clip((rr / RING).astype(int), 0, int(R_DISK / RING) - 1) + nb = int(R_DISK / RING) + prof = np.zeros(nb) + a1 = np.zeros(nb) + b1 = np.zeros(nb) + for b in range(nb): + m = rings == b + if not m.any(): + continue + v, t = vals[m], tt[m] + prof[b] = v.mean() + a1[b] = 2 * ((v - prof[b]) * np.cos(t)).mean() + b1[b] = 2 * ((v - prof[b]) * np.sin(t)).mean() + resid0 = vals - prof[rings] + wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) + amp = np.hypot(a1, b1) + w = amp * np.arange(nb) + ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) + return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), + "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), + "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + + +def subgrid_min(field, ci, cj): + z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") + A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), + gx.ravel() ** 2, gy.ravel() ** 2, + (gx * gy).ravel()]) + c = np.linalg.lstsq(A, z, rcond=None)[0] + _, b, cc, d, e, g = c + H = np.array([[2 * d, g], [g, 2 * e]]) + try: + dj, di = np.linalg.solve(H, [-b, -cc]) + except np.linalg.LinAlgError: + dj = di = 0.0 + di, dj = float(np.clip(di, -1, 1)), float(np.clip(dj, -1, 1)) + lat_step = lat[ci + 1] - lat[ci] if ci + 1 < NY else lat[ci] - lat[ci - 1] + return lat[ci] + di * lat_step, lon_deg[cj] + dj * 0.25 + + +def wrap_deg(d): + return (d + 180.0) % 360.0 - 180.0 + + +def err_deg(low_pole_rad, motion_rad): + return float(wrap_deg(np.rad2deg( + low_pole_rad - (motion_rad + np.pi / 2)))) + + +def binom_sf_ge(k, n, p=0.5): + return sum(comb(n, i) * (p ** i) * ((1 - p) ** (n - i)) + for i in range(k, n + 1)) + + +# ---- fresh, mechanically-generated candidate dates ------------------------- +START = datetime.datetime(1996, 1, 15, 12) +STRIDE_DAYS = 61 # arbitrary, fixed before writing this loop, + # never adjusted after seeing output +N_CANDIDATES = 85 # sized from the two prior samples' combined + # empirical rate (7/25 candidates ~= 0.28) to + # target n>=20-30 qualifying storms +DATES = [START + datetime.timedelta(days=STRIDE_DAYS * i) + for i in range(N_CANDIDATES)] +print(f"\nCT-F14: {N_CANDIDATES} candidate dates, stride={STRIDE_DAYS}d, " + f"{DATES[0].date()} .. {DATES[-1].date()} " + "(1996-2014, zero overlap with prior samples)", flush=True) + +rows = [] +for idx, dt in enumerate(DATES): + t0 = t_index(dt) + if t0 < 0 or t0 + 1 > _MAX_T: + rows.append({"date": dt.isoformat(), "status": "OUT-OF-STORE-BOUNDS"}) + continue + try: + p0 = fetch("mean_sea_level_pressure", f"{t0}.0.0")[0].astype(np.float64) + p1 = fetch("mean_sea_level_pressure", f"{t0+1}.0.0")[0].astype(np.float64) + except Exception as e: + rows.append({"date": dt.isoformat(), "status": f"FETCH-ERROR: {e}"}) + print(f"[{idx+1}/{N_CANDIDATES}] {dt.date()}: fetch error {e}", + flush=True) + continue + + ci0, cj0 = find_center(p0) + la_a, lo_a = subgrid_min(p0, ci0, cj0) + ci1, cj1 = find_center(p1, near=(la_a, lo_a)) + dx, dy, _, _ = geom_ll(la_a, lo_a) + disp = (float(dx[ci1, cj1]), float(dy[ci1, cj1])) + dist = float(np.hypot(*disp)) + mth = float(np.arctan2(disp[1], disp[0])) + trackable = dist >= 100.0 + + row = {"date": dt.isoformat(), "t0": t0, + "center_lat": la_a, "center_lon": lo_a, + "displacement_km": dist, "CT_E2_trackable": bool(trackable)} + if not trackable: + row["status"] = "NOT-TRACKABLE" + rows.append(row) + if (idx + 1) % 10 == 0 or idx == 0: + print(f"[{idx+1}/{N_CANDIDATES}] {dt.date()}: " + f"disp={dist:.0f}km not trackable", flush=True) + continue + + d = decompose_ll(p0, la_a, lo_a) + err = err_deg(d["low_pole_rad"], mth) + row.update({"status": "OK", "wn1_frac": d["wn1_frac"], + "R2_profile_wn1": d["R2_profile_wn1"], "error_deg": err, + "sign_negative": bool(err < 0)}) + rows.append(row) + tag = " ***DISP>=250***" if dist >= 250 else "" + print(f"[{idx+1}/{N_CANDIDATES}] {dt.date()}: disp={dist:.0f}km " + f"err={err:+.1f}deg wn1={d['wn1_frac']:.2f}{tag}", flush=True) + +valid = [r for r in rows if r.get("status") == "OK"] +excluded = [r for r in rows if r.get("status") != "OK"] +qualifying = [r for r in valid if r["displacement_km"] >= 250.0] + +print(f"\n{len(valid)}/{len(rows)} valid (trackable); " + f"{len(qualifying)} qualify (disp>=250km); " + f"{len(excluded)} excluded", flush=True) +exclusion_counts = {} +for r in excluded: + exclusion_counts[r["status"]] = exclusion_counts.get(r["status"], 0) + 1 +print("exclusion breakdown:", exclusion_counts, flush=True) + +out = {"store": B, "sample_start": START.isoformat(), + "stride_days": STRIDE_DAYS, "n_candidates": N_CANDIDATES, + "rows": rows, "n_valid": len(valid), "n_qualifying": len(qualifying), + "n_excluded": len(excluded), "exclusion_breakdown": exclusion_counts} + +print("\n=== CT-F14 verdict ===", flush=True) +MIN_N = 20 +if len(qualifying) < MIN_N: + print(f"n={len(qualifying)} < MIN_N={MIN_N} -> NO-VERDICT " + "(pre-registered minimum, stated before results)") + out["CT_F14"] = {"n": len(qualifying), "verdict": "NO-VERDICT-INSUFFICIENT-N"} +else: + errs = np.array([r["error_deg"] for r in qualifying]) + neg = int((errs < 0).sum()) + frac = neg / len(qualifying) + p_own = binom_sf_ge(neg, len(qualifying)) + passed = frac >= 0.70 + med_wn1 = float(np.median([r["wn1_frac"] for r in qualifying])) + med_r2 = float(np.median([r["R2_profile_wn1"] for r in qualifying])) + print(f"CT-F14: n={len(qualifying)}, neg={neg}/{len(qualifying)} = " + f"{frac:.3f}, one-sided p={p_own:.4f} -> " + f"{'PASS' if passed else 'FAIL'} (bar >= 0.70)") + print(f" median wn1_frac={med_wn1:.3f}, median R2={med_r2:.3f} " + "(compare against N3/N4's 0.723/0.900)") + out["CT_F14"] = {"n": len(qualifying), "n_negative": neg, + "sign_neg_frac": frac, "one_sided_p": p_own, + "pass": bool(passed), "median_wn1_frac": med_wn1, + "median_R2_profile_wn1": med_r2} + +# combined with the two prior independent samples' disp>=250 subsets +PRIOR_S1 = {"n": 4, "neg": 3} # comet_tail_f5_n10.json CT_N disp>=250 +PRIOR_S2 = {"n": 3, "neg": 3} # comet_tail_f10_f11.json CT_F10 disp>=250 +n_qual = len(qualifying) +neg_qual = int(sum(1 for r in qualifying if r["error_deg"] < 0)) if n_qual else 0 +n_combined = PRIOR_S1["n"] + PRIOR_S2["n"] + n_qual +neg_combined = PRIOR_S1["neg"] + PRIOR_S2["neg"] + neg_qual +frac_combined = neg_combined / n_combined if n_combined else float("nan") +p_combined = binom_sf_ge(neg_combined, n_combined) if n_combined else float("nan") + +if p_combined < 0.05: + interp = "ESTABLISHED at this n, displacement-filtered regime -> ready for audit-gate queue as [H]-graded, scope-limited claim" +elif p_combined < 0.10: + interp = "SUGGESTIVE, needs further n before promotion, not audit-gate-ready" +else: + interp = "NOT ESTABLISHED -> apparatus explanation itself in question; retire directional claim to [S] pending a fundamentally different design" + +print(f"\n=== COMBINED across THREE independent samples " + f"(sample1 n=4 + sample2 n=3 + CT-F14 n={n_qual}) ===") +print(f"n={n_combined}, neg={neg_combined}/{n_combined} = {frac_combined:.3f}, " + f"one-sided p={p_combined:.4f}") +print(f"PRE-COMMITTED interpretation: {interp}") + +out["CT_F14_combined_3sample"] = { + "n": n_combined, "n_negative": neg_combined, + "sign_neg_frac": frac_combined, "one_sided_p": p_combined, + "interpretation": interp, + "components": {"sample1_disp250": PRIOR_S1, "sample2_disp250": PRIOR_S2, + "CT_F14": {"n": n_qual, "neg": neg_qual}}} + +json.dump(out, open("comet_tail_f14.json", "w"), indent=2) +print("\nwrote comet_tail_f14.json") From b86a81eb0349e6f03ff6a004f76e27685e407ef6 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 21:38:29 +0000 Subject: [PATCH 11/23] probe(weather): CT-F14 result -- the properly-powered test doesn't clear its own bar; pooled figure technically passes but is graded down on sensitivity scrutiny, and a gap in the pre-registration itself is named CT-F14 ran: 85 mechanically-generated candidates (1996-2010, zero overlap with either prior sample), 64/85 trackable, 19 qualified at disp>=250km/6h -- one short of the pre-registered n=20 floor. Correctly NO-VERDICT by the pre-registered rule. CT-F14's own qualifying subset, taken alone: 13/19=0.684, one-sided p=0.0835 -- BELOW the 0.70 bar and not significant, even ignoring the n<20 power floor. The single test this whole probe was built to produce does not independently support the directional claim. The pooled 3-sample figure (n=26, 19/26=0.731) crosses the pre-committed p<0.05 "established" threshold (p=0.0145) -- by the LETTER of the pre-registration this licenses reporting "established, audit-gate ready." Sensitivity check (same scrutiny applied to a result that HELPS as was applied to the reversal in the prior entry): all three pooled: n=26, 0.731, p=0.0145 excluding smallest/saturated (n=3): n=23, 0.696, p=0.0466 (still <0.05, barely -- not purely an artifact of one tiny fragment) CT-F14 alone (the properly-powered test): n=19, 0.684, p=0.0835 (NOT sig) two small prior fragments alone: n=7, 0.857, p=0.0625 GAP NAMED: the pre-registration specified thresholds for a POOLED figure without specifying what to do if the new properly-powered sample DISAGREES with the pooled verdict on its own terms -- a configuration I did not anticipate when writing the pre-registration. Reporting only the pooled "established" number, though technically licensed, would exploit that gap the same way cherry-picking data would -- just one level up, at which-valid-reading-to-report time instead of which-data-to-report time. Graded the verdict DOWN to "still suggestive" instead, and recorded the pre-registration's own gap as the finding. Second, smaller walk-back: sec5.10's striking monsoon-band exclusion pattern (4/5 in the small 1980-1995 sample) thinned to 2/21 at 4x the exclusion count here -- the small-n-looked-like-a-pattern theme recurring one level below the main directional claim. Report updated: sec5.11 new section with full sensitivity table, both executive-summary caveats layered again (third layer), sec6 marker updated (compression ready for audit queue; predictor "suggestive at best, not yet earned"), next-falsifiers/status closed out honestly: structural claim solid throughout every sample this arc ran; directional claim tested four separate ways across 41 total storms and still does not clear a real bar on its strongest test. Board: EPIPHANIES prepend E-MY-OWN-PRE-REGISTRATION-HAD-A-GAP-AND-I-NAMED-IT-1 -- pre-registration protects against post-hoc rationalization of DATA; it does not automatically protect against post-hoc selection among multiple valid readings of the rule itself when the rule admits more than one -- that needs the same discipline applied one level up, at read time not just design time. Co-Authored-By: Claude Sonnet 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 58 ++ probes/weather-p1/COMET_TAIL_REPORT.md | 151 +++- probes/weather-p1/comet_tail_f14.json | 1060 ++++++++++++++++++++++++ 3 files changed, 1243 insertions(+), 26 deletions(-) create mode 100644 probes/weather-p1/comet_tail_f14.json diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 37f73d5d..92021bd2 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,61 @@ +## 2026-08-11 — E-MY-OWN-PRE-REGISTRATION-HAD-A-GAP-AND-I-NAMED-IT-1 + +**Status:** FINDING `[G]` — CT-F14, `comet_tail_f14.py` / `.json`, report +§5.11. Direct follow-up to E-THE-RESCUE-THAT-WEAKENED-ITSELF... below; this +entry is about a defect in *my own pre-registration design*, caught and +corrected by the same discipline it should have applied from the start. + +**CT-F14 was pre-registered and committed to git BEFORE it ran** (`4f1a1b4f`) +— fixed dates, fixed bar (n≥20, ≥0.70), and a fixed interpretation table for +a "combined 3-sample" figure, all decided before any output existed. Run: 19 +of 85 mechanically-generated candidates qualified (one short of the n=20 +floor) → correctly **NO-VERDICT by the pre-registered rule.** The pooled +3-sample figure (n=26, 19/26=0.731) crossed the pre-committed p<0.05 +"established" threshold (p=0.0145). + +**By the letter of my own pre-registration, this should have been reported +as "established, ready for the audit-gate queue."** It was not, because a +sensitivity check — run precisely because the last entry demands applying +scrutiny to results that help as readily as to ones that hurt — found +something the pre-registration never anticipated: **CT-F14's own qualifying +subset, taken alone, sits at 0.684 (13/19), BELOW the 0.70 bar, p=0.0835 — +not significant.** The single test this whole probe existed to produce does +not independently support the claim it was built to test. The "established" +pooled figure is being carried by two small prior fragments (n=4 at 0.75, +n=3 at 1.00) blended with a properly-powered new sample that came in lower. +Dropping just the smallest fragment (n=3, fully saturated) still barely +clears p<0.05 (0.0466) — so the pooled crossing is not purely an artifact of +one tiny subsample, but the component that mattered most (the large, careful +new test) disagrees with the pooled verdict on its own terms. + +**The gap, named plainly: my pre-registration specified thresholds for a +pooled figure without specifying what to do if the new, properly-powered +sample and the pooled figure disagreed.** I did not write a rule for this +exact configuration because I did not anticipate it — I expected CT-F14 to +either clearly pass or clearly fail on its own, not to fall one storm short +of its power floor while *also* landing under the bar. Finding that gap +after the fact and exploiting it silently (reporting only the pooled +"established" number, which the letter of my pre-registration technically +licensed) would have been exactly the failure mode this arc's discipline +exists to prevent — just moved one level up, from cherry-picking a result to +cherry-picking which of two valid readings of a pre-committed rule to report. +**Named instead: graded the verdict down to "still suggestive," and +recorded the pre-registration gap itself as the finding**, alongside a +second, smaller walk-back (§5.10's striking monsoon-band exclusion pattern, +4/5 in the small sample, thinned to 2/21 at 4× the exclusion count in this +larger one — the small-n-looked-like-a-pattern theme recurring one level +down from the main directional claim). + +**The reusable lesson, sharper than the previous entry's:** *pre-registration +protects against post-hoc rationalization of the DATA. It does not +automatically protect against post-hoc selection among several VALID readings +of the rule itself, when the rule turns out to admit more than one — that +requires the same discipline applied one level up, at read time, not just at +design time.* After four probes, three independent samples, and 41 total +storms, the honest position is: structural claim solid throughout, directional +claim genuinely undetermined — not because no test was run, but because the +one test built to settle it came back below its own bar. + ## 2026-08-11 — E-THE-RESCUE-THAT-WEAKENED-ITSELF-UNDER-SCRUTINY-1 **Status:** FINDING `[G]` — CT-F10/F11/F13, `comet_tail_f10_f11.py` / `.json`, diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index c5501ff9..3253edfc 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -47,6 +47,20 @@ generalization beyond n=2. > chance, 0.500). Read §4/§5.9's "not established" verdict as still current, > now with a specific, falsifiable next step (CT-F14) rather than a closed > question. +> +> **⚠⚠⚠ UPDATE 2026-08-11 (§5.11, CT-F14, the properly-powered test).** The +> pre-registered decision rule technically fires "established" on a pooled +> 3-sample figure (p=0.0145) — but the single test this whole exercise was +> designed to produce, CT-F14 alone (n=19, the largest and most carefully +> powered sample in the chain), does **NOT** independently clear +> significance (p=0.0835) and its raw rate (0.684) sits below the 0.70 bar. +> Applying the same scrutiny used on §5.10's reversal: **graded down from +> "established" to "still suggestive"**, and the pre-registration's own +> pooling rule is flagged as having a real gap (no contingency for a large +> new sample disagreeing with small prior fragments). The directional claim +> remains **not established** after four probes and three independent +> samples (n=41 total storms). The structural claim (wn1 dominance, +> explanatory power) is untouched throughout. Product consequence `[S]`: a storm's pressure field compresses to **center position + ~12 ring means + one dipole vector** at 93–97 % variance @@ -561,6 +575,78 @@ correctly-scoped next step, not a third exploratory rerun. proxy at intake) and re-check whether CT-E2's own trackability gate keeps doing this filtering job for free, as the exclusion pattern above suggests. +### 5.11 CT-F14 — the properly-powered test — **fell one storm short of its own floor, and didn't clear the bar either** + +`comet_tail_f14.py` / `.json`. §5.10 named CT-F14 as the correctly-scoped +next step: a single, properly powered (n≥20), displacement-filtered-only, +pre-registered sample — committed to git (`4f1a1b4f`) *before execution*, +including the exact interpretation thresholds for a pooled three-sample +figure, so the read could not be tuned after seeing results. + +85 mechanically-generated candidates (fixed start 1996-01-15, fixed 61-day +stride, chosen before writing the loop), landing 1996–2010, zero overlap +with either prior sample. 64/85 trackable (75 % — closer to sample 1's 100 % +than sample 2's 67 %); **19 storms qualified** at displacement ≥ 250 km/6h — +one short of the pre-registered n=20 floor. + +| test | n | sign-neg fraction | one-sided p | verdict | +|---|---:|---:|---:|---| +| **CT-F14 alone** | 19 | 0.684 (13/19) | 0.0835 | **NO-VERDICT** (n<20, pre-registered floor) — and would have **FAILED** the 0.70 bar anyway if the floor were ignored | +| Pooled, all 3 independent samples | 26 | **0.731** (19/26) | **0.0145** | crosses the pre-committed <0.05 "established" threshold | + +**The pre-registered rule technically fires "established."** The honest +next step — applying the same scrutiny used on §5.10's favorable reversal — +is to check how much that crossing depends on which components are in the +pool, since two of the three components are very small: + +| pooled subset | n | fraction | one-sided p | +|---|---:|---:|---:| +| All three (pre-registered) | 26 | 0.731 | **0.0145** | +| Excluding the smallest/most saturated prior subsample (sample 2, n=3, 3/3) | 23 | 0.696 | 0.0466 | +| **CT-F14 alone (the properly-powered test)** | 19 | 0.684 | **0.0835** | +| The two small prior subsamples alone (n=7) | 7 | 0.857 | 0.0625 | + +**Two things are true at once, and both need to be said plainly.** (1) The +pooled figure is not purely an artifact of the smallest fragment — dropping +it still leaves p=0.0466, barely under 0.05. (2) **The single test this +whole probe was designed to produce — CT-F14 alone, n=19, the largest and +most carefully powered sample in the entire chain — does NOT independently +support the claim** (p=0.0835, "suggestive" by the arc's own pre-committed +scale, and its raw rate sits below the 0.70 bar). The "established" reading +depends on treating three heterogeneous samples (different eras, different +mechanical generators, sizes 4/3/19) as one undifferentiated pool of +Bernoulli trials — a legitimate but not the only reasonable pooling choice, +and one this probe's pre-registration did not anticipate needing to defend +against a properly-powered *new* sample landing lower than small prior +fragments. + +**Honest verdict, overriding the letter of the pre-committed rule where +scrutiny disagrees with it:** this is a **fragile pass, not an established +finding.** Graded down from "ready for the audit-gate queue" to +**suggestive, same tier as before CT-F14 ran** — because the properly-powered +component alone does not clear significance, and that component is the one +this whole exercise existed to produce. Recording this as a **gap in the +pre-registration itself**, not smoothed over: the interpretation thresholds +were written for the pooled figure without a contingency for "the new, +larger sample disagrees with the small prior fragments" — a real design +blind spot, named so a future pre-registration in this arc specifies it. + +**A second, unplanned walk-back, in the same direction.** §5.10 flagged a +striking pattern — 4 of 5 trackability exclusions in the 1980–1995 sample +clustered at 26–33°N/67–134°E in June–September. This sample's 21 +exclusions show the **same band catching only 2 of 21** — a much weaker +signal at 4× the exclusion count. The earlier 4/5 was very likely a small-n +inflation of a real-but-modest effect, not a strong reproducible pattern. +CT-F15 (the proposed geo-fence) is downgraded accordingly — worth a light +touch, not a structural fix. + +**What remains solid, restated:** the structural claim (wn1 dominance, +explanatory power) is untouched by any of this — CT-F14's own qualifying +subset has median wn1_frac 0.60, median R² 0.90, consistent with N3/N4. +Only the *directional/predictive* claim is affected, and it moves from +"borderline" (§5.10) to **"borderline, and the properly-powered test that +was supposed to settle it did not."** + ### 5.4 Where that leaves the three candidates > **Read this table as a within-storm-1/2 candidate ranking for the OFFSET @@ -637,11 +723,14 @@ this quantity. No stronger attribution is claimed at n = 2. ## 6. Product / encoding consequence `[S]` -> **⚠ Read with §5.9.** The ring-profile + dipole compression below is solid -> — CT-N3/N4 confirmed it generalizes at n=10. The *motion-encoding* half -> ("the dipole encodes the motion") is demonstrated on storms 1–2 only; CT-N1 -> found the sign relationship it depends on is not yet shown to generalize. -> Treat the compression as ready, the predictor as gated on CT-F10/F11. +> **⚠ Read with §5.9–5.11.** The ring-profile + dipole compression below is +> solid — it generalized cleanly across every sample this arc ran (N3/N4, +> and CT-F14's own qualifying subset: median wn1_frac 0.60, R² 0.90). The +> *motion-encoding* half ("the dipole encodes the motion") depends on the +> signed relationship CT-F14 — the properly-powered test built to settle it — +> did NOT independently establish (§5.11). Treat the compression as ready +> for the audit-gate queue; treat the predictor as **suggestive at best**, +> not gated-and-pending but **not yet earned**. If CT-F1..F3 hold up, the compact representation of a surface low is: @@ -704,25 +793,35 @@ storm ≈ CENTER (place) — 1 address (drops it to exactly chance). What *does* strengthen is the displacement-filtered pooled subset (6/7, p≈0.0625) — apparatus, not regime, is now the better-supported explanation for the gap. -7. **CT-F14 (pre-registered, NOT run) is the correctly-scoped next step:** - a single, properly powered (n≥25–30) displacement-filtered-only sample. +7. ~~**CT-F14**~~ — **RUN, §5.11. The single properly-powered test does NOT + independently support the claim** (n=19, one short of its own n≥20 floor; + 0.684, p=0.0835). The pooled 3-sample figure technically crosses the + pre-committed <0.05 threshold (p=0.0145), but the honest, scrutinized + verdict grades this down to **still suggestive** — the pre-registration's + own pooling rule had a gap, named rather than exploited. 8. Adversarial audit gate (plan §8) before any of it is promoted to EV / - product claim. - -**Net effect of the full follow-up chain on the headline claim.** The §1 -summary needs a real caveat now, not just a footnote: wn-1 dominance and the -R² lift **generalize** cleanly across two independent decades — the signed, -motion-locked orientation that made storms 1–2 exciting is **borderline**: -not established (combined 14/20 sits at the noise floor), not dead either -(the displacement-filtered pooled subset, 6/7, is the most consistent signal -in the entire chain). The offset went **dead (F3) → alive with a ±3–7° error -bar (F4) → apparently general at n=10 → reversed on n=10 more → converging -on "real for fast-moving storms, undetermined for slow ones" (n=10+10, CT-N + -CT-F10/13)**. Each step was a genuine gate, not a rescue of the previous -one — including the moment (§5.10) a result that *helped* the headline claim -got the same scrutiny as one that hurt it (the regime-filter check on -sample 1 was run and reported even though it weakened the more flattering -story). That symmetry is what makes the current position — "solid structural -finding, borderline-and-narrowing directional finding, one clean next -falsifier (CT-F14)" — trustworthy. §6's encoding and single-frame-predictor -work is gated behind CT-F14, not general n ≥ 10. + product claim — moot for the directional claim until it clears its own + properly-powered test; live for the structural (compression) claim now. + +**Net effect of the full follow-up chain on the headline claim, stated +plainly after four probes and 41 total storms across three independent +samples.** The structural claim — wn-1 dominance, the ring-profile + dipole +compression, R² lift — **generalizes cleanly and has not been shaken once** +across every sample this arc ran. The directional/predictive claim — the +signed left-of-motion orientation that made storms 1–2 exciting — has now +been tested four separate ways (apparatus §5.6, land-fraction mechanism +§5.7, blind n=10 §5.9, a reversal on independent n=10 §5.10, a +properly-powered n=19 §5.11) and **still does not clear a real bar on its +own strongest test.** The offset's status across the chain: **dead (F3) → +alive with a ±3–7° error bar (F4) → apparently general (misread of an n=10 +in isolation) → reversed on a second n=10 → pooled-and-technically-passing +but not independently supported by the one test built to settle it (F14).** +That is not noise in the writing — it is the honest trajectory of a +borderline effect being measured with increasing rigor, and every step was a +genuine gate: a result that *helped* the claim (§5.10's reversal, CT-F14's +pooled crossing) got exactly the scrutiny a result that hurt it would have +gotten, and in both cases the scrutiny found reasons for caution that a less +careful pass would have missed. **Current position: the compression is +ready for the audit-gate queue now; the predictor is not, and should not be +represented as more than "suggestive" until a properly-powered test clears +its own bar without pooling assistance.** §6 is marked accordingly. diff --git a/probes/weather-p1/comet_tail_f14.json b/probes/weather-p1/comet_tail_f14.json new file mode 100644 index 00000000..e9aa30c8 --- /dev/null +++ b/probes/weather-p1/comet_tail_f14.json @@ -0,0 +1,1060 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "sample_start": "1996-01-15T12:00:00", + "stride_days": 61, + "n_candidates": 85, + "rows": [ + { + "date": "1996-01-15T12:00:00", + "t0": 54114, + "center_lat": 63.25982565609447, + "center_lon": 319.72181248801405, + "displacement_km": 143.29189743733238, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.7034459124320716, + "R2_profile_wn1": 0.7677471033064385, + 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"NOT-TRACKABLE" + }, + { + "date": "2005-11-22T12:00:00", + "t0": 68510, + "center_lat": 46.252952173269, + "center_lon": 208.49998623458347, + "displacement_km": 225.92029658999758, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.27525031465262145, + "R2_profile_wn1": 0.7784413891426507, + "error_deg": 33.1314799989201, + "sign_negative": false + }, + { + "date": "2006-01-22T12:00:00", + "t0": 68754, + "center_lat": 41.63454690695439, + "center_lon": 158.0240357377511, + "displacement_km": 291.43078891941735, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.5287657645924341, + "R2_profile_wn1": 0.8637769409048849, + "error_deg": 46.520425963822305, + "sign_negative": false + }, + { + "date": "2006-03-24T12:00:00", + "t0": 68998, + "center_lat": 40.081768429120515, + "center_lon": 316.97489409322253, + "displacement_km": 282.49422464834026, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.7667653963904881, + "R2_profile_wn1": 0.9007108619579532, + 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41.990250690400565, + "center_lon": 134.35349734825567, + "displacement_km": 244.24100553586638, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.613718755258605, + "R2_profile_wn1": 0.7516332404696452, + "error_deg": 53.71512031682133, + "sign_negative": false + }, + { + "date": "2007-07-25T12:00:00", + "t0": 70950, + "center_lat": 36.76618471235129, + "center_lon": 96.17249268645661, + "displacement_km": 37.677201014839085, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "2007-09-24T12:00:00", + "t0": 71194, + "center_lat": 59.69631488814643, + "center_lon": 183.509118148923, + "displacement_km": 105.24774845288307, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.5871365986298074, + "R2_profile_wn1": 0.8849299246694261, + "error_deg": -2.212465859228871, + "sign_negative": true + }, + { + "date": "2007-11-24T12:00:00", + "t0": 71438, + "center_lat": 55.229505601945235, + "center_lon": 169.92053231351656, + "displacement_km": 74.2624359832062, + "CT_E2_trackable": false, + "status": "NOT-TRACKABLE" + }, + { + "date": "2008-01-24T12:00:00", + "t0": 71682, + "center_lat": 56.127472346306945, + "center_lon": 173.16491994869907, + "displacement_km": 127.51075300415441, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.28851992440842855, + "R2_profile_wn1": 0.8221809306100194, + "error_deg": 158.2208369158227, + "sign_negative": false + }, + { + "date": "2008-03-25T12:00:00", + "t0": 71926, + "center_lat": 52.95302344219514, + "center_lon": 30.90502116898233, + "displacement_km": 144.35750607678403, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.5384726774578575, + "R2_profile_wn1": 0.8935969065345197, + "error_deg": 57.47145916488944, + "sign_negative": false + }, + { + "date": "2008-05-25T12:00:00", + "t0": 72170, + "center_lat": 57.097293225844446, + "center_lon": 308.38436394044123, + "displacement_km": 164.86759953878362, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.7290751808591619, + "R2_profile_wn1": 0.8989490296494737, + "error_deg": -48.04552730472784, + "sign_negative": true + }, + { + "date": "2008-07-25T12:00:00", + "t0": 72414, + "center_lat": 31.900038974581634, + "center_lon": 63.05409675230088, + "displacement_km": 114.12091612889061, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.6141772212888934, + "R2_profile_wn1": 0.642327611825033, + "error_deg": -156.31609111456203, + "sign_negative": true + }, + { + "date": "2008-09-24T12:00:00", + "t0": 72658, + "center_lat": 50.29407906924802, + "center_lon": 161.66609864388215, + "displacement_km": 134.7592934657519, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.6842262474882242, + "R2_profile_wn1": 0.8536437505832669, + "error_deg": -24.841364650294935, + "sign_negative": true + }, + { + "date": "2008-11-24T12:00:00", + "t0": 72902, + "center_lat": 55.63059655563931, + "center_lon": 200.15611004795264, + "displacement_km": 213.88546971278637, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.6902624708340415, + "R2_profile_wn1": 0.8551239580601979, + "error_deg": -18.96265701386858, + "sign_negative": true + }, + { + "date": "2009-01-24T12:00:00", + "t0": 73146, + "center_lat": 63.76633709633874, + "center_lon": 338.9549162858299, + "displacement_km": 243.54993083197846, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.7984554191055045, + "R2_profile_wn1": 0.8724220061337502, + "error_deg": 9.025548666417762, + "sign_negative": false + }, + { + "date": "2009-03-26T12:00:00", + "t0": 73390, + "center_lat": 59.458189862483295, + "center_lon": 354.749746897519, + "displacement_km": 141.35475016145975, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.6769636234075413, + "R2_profile_wn1": 0.9143044100189791, + "error_deg": -49.68728539987143, + "sign_negative": true + }, + { + "date": "2009-05-26T12:00:00", + "t0": 73634, + "center_lat": 32.081890067957204, + "center_lon": 85.29824266895127, + "displacement_km": 585.0246566127836, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.4499584064719253, + "R2_profile_wn1": 0.4952763158604363, + "error_deg": -53.08536899757155, + "sign_negative": true + }, + { + "date": "2009-07-26T12:00:00", + "t0": 73878, + "center_lat": 34.14437390359832, + "center_lon": 71.81849475304371, + "displacement_km": 363.2025023438297, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.4313284079650069, + "R2_profile_wn1": 0.44840644133553687, + "error_deg": -93.58088940292669, + "sign_negative": true + }, + { + "date": "2009-09-25T12:00:00", + "t0": 74122, + "center_lat": 69.01585282912403, + "center_lon": 353.4343585345505, + "displacement_km": 562.8503596905939, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.8679881135275417, + "R2_profile_wn1": 0.9189089794677993, + "error_deg": 0.13717943106883013, + "sign_negative": false + }, + { + "date": "2009-11-25T12:00:00", + "t0": 74366, + "center_lat": 59.52619677699913, + "center_lon": 352.03934730709716, + "displacement_km": 270.81659311746546, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.5955321552576727, + "R2_profile_wn1": 0.8724852150614523, + "error_deg": -30.594845686320213, + "sign_negative": true + }, + { + "date": "2010-01-25T12:00:00", + "t0": 74610, + "center_lat": 66.49068385848419, + "center_lon": 328.610334955992, + "displacement_km": 141.0921969569801, + "CT_E2_trackable": true, + "status": "OK", + "wn1_frac": 0.3735504876612974, + "R2_profile_wn1": 0.8651860376528754, + "error_deg": 3.7479716406901105, + "sign_negative": false + } + ], + "n_valid": 64, + "n_qualifying": 19, + "n_excluded": 21, + "exclusion_breakdown": { + "NOT-TRACKABLE": 21 + }, + "CT_F14": { + "n": 19, + "verdict": "NO-VERDICT-INSUFFICIENT-N" + }, + "CT_F14_combined_3sample": { + "n": 26, + "n_negative": 19, + "sign_neg_frac": 0.7307692307692307, + "one_sided_p": 0.014479637145996094, + "interpretation": "ESTABLISHED at this n, displacement-filtered regime -> ready for audit-gate queue as [H]-graded, scope-limited claim", + "components": { + "sample1_disp250": { + "n": 4, + "neg": 3 + }, + "sample2_disp250": { + "n": 3, + "neg": 3 + }, + "CT_F14": { + "n": 19, + "neg": 13 + } + } + } +} \ No newline at end of file From feec4b77c481be2ec16f6daf5a0750f3bdd93c14 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 22:03:02 +0000 Subject: [PATCH 12/23] =?UTF-8?q?docs(weather):=20=C2=A79=20reframe=20?= =?UTF-8?q?=E2=80=94=20the=20spine=20is=20found,=20the=20moderators=20are?= =?UTF-8?q?=20missing=20(operator=20ruling),=20incl.=20the=20unmodeled=20m?= =?UTF-8?q?oist/entropy=20sector?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Operator framing, recorded because it is the statistically correct reading of the chain, not spin: a 0.68-0.73 directional main effect whose residual runs MONOTONICALLY with a measured variable (the 92-102 deg height ladder, 3-5x apparatus noise) is the signature of a MISSING MODERATOR, not a null — a null does not produce a ladder. §9.1 the spine [G]: center + ~12 ring means + 1 wn-1 dipole = 93-97% of in-disk variance, unshaken across 3 independent samples / 41+ storms / 1980-2021 — an explicit ~14-byte skeleton for the center and the first asymmetry mode of the dynamics. §9.2 dry moderators [H], measured but unwired: steering level (CT-F16 = score the dipole against steering-level motion, not 6h surface displacement), displacement/label noise, friction/surface type, latitude/regime. §9.3 moist sector [S], not modeled at all (operator: "Feuchtigkeit und Abregnen im Aufwind an der Kollision zwischen den Gebieten — eine Art Entropie"): rain-out in the collision-zone updraft as irreversible moist entropy production, theta-e the state variable, precipitation the sink (Emanuel/Pauluis frame). Tractable now — the store carries specific_humidity/temperature/TCWV/total_precipitation_6hr/vertical_velocity, and theta-e/TCWV are scalar fields so the SAME ring/wn-1 decomposition applies verbatim. CT-M1..M3 named. The July failures (wn1 0.19-0.36) are plausibly the diabatically-dominated storms -> diabatic dominance as a computable intake gate. §9.4 the brutal step [S] (operator-directed): moderators as W in domino.rs' symbiont C=A·W tile-GEMM (stencil-as-GEMM already byte-proven on real WB2 in ndarray geostrophic_stencil.rs) + LSTM-shaped recurrence over 6h spine states (byte-parity int8 LSTM exists, E-OCR-LSTM-1). Explicit physics as spine, learned weights as moderators — gated by disjoint-decade train/test + the plan-§8 audit. Board: EPIPHANIES prepend E-SPINE-FOUND-MODERATORS-MISSING-1. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 46 +++++++++++ probes/weather-p1/COMET_TAIL_REPORT.md | 104 +++++++++++++++++++++++++ 2 files changed, 150 insertions(+) diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 92021bd2..7fc82efb 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,49 @@ +## 2026-08-11 — E-SPINE-FOUND-MODERATORS-MISSING-1 + +**Status:** OPERATOR RULING (framing) + FINDING `[H]` — report §9, commit +paired with the PR opening. Reframes the whole comet-tail chain's verdict. + +**Operator:** *"Wir haben ein Spine gefunden — die Stellschrauben müssen noch +mit den Variablen der bekannten Modelle moduliert werden. Uns fehlen die +Moderatoren; aber wir haben bereits das Gerüst, um das Zentrum und die +Dynamik zu modellieren. Außerdem haben wir Feuchtigkeit und Abregnen im +Aufwind an der Kollision zwischen den Gebieten nicht modelliert — das ist +eine Art Entropie bei Verdunstung und Abregnen."* + +**Why this is the statistically correct reading, not spin:** a 0.68–0.73 +directional main effect whose residual were random would be a dying claim — +but this chain's residual runs MONOTONICALLY with a measured variable (the +92–102° height ladder, 3–5× apparatus noise). *Main effect + structured +residual + identified covariate* is the signature of a **missing moderator**, +not of a null. A null does not produce a ladder. + +**The three-part decomposition now on record (report §9):** +1. **Spine `[G]`** — center + ~12 ring means + 1 wn-1 dipole = 93–97 % of + in-disk variance, unshaken across 3 independent samples / 41+ storms / + 1980–2021. ~14 bytes + an address. +2. **Dry moderators `[H]`** — measured in this chain, not yet wired: + steering level (THE ladder; CT-F16 = score the dipole against + steering-level motion instead of 6h surface displacement), displacement/ + label noise, friction/surface type, latitude/regime. +3. **Moist sector `[S]`** — not modeled at all, and "entropy" is technically + the right word: rain-out in the collision-zone updraft is irreversible + moist entropy production (θe the state variable, precipitation the sink — + Emanuel/Pauluis frame). Tractable NOW: the WB2 store carries + specific_humidity/temperature (θe), TCWV, total_precipitation_6hr, + vertical_velocity — and θe/TCWV are scalar fields, so the SAME ring/wn-1 + decomposition applies verbatim. CT-M1..M3 named as falsifiers; the July + failures (wn1_frac 0.19–0.36) are plausibly the diabatically-dominated + storms, making diabatic dominance itself a computable intake gate. + +**The brutal step (operator-directed, `[S]`):** learn the moderator matrix on +the substrate's own proven machinery — the spine as board state, moderators +as `W` in domino.rs' symbiont `C = A·W` tile-GEMM (stencil-as-GEMM already +byte-proven on real WB2 in ndarray `geostrophic_stencil.rs`), recurrence over +6h spine states via the workspace's byte-parity int8 LSTM (E-OCR-LSTM-1). +Explicit physics as spine, learned weights as moderators — the NeuralGCM- +shaped hybrid at 512 B/storm, gated by disjoint-decade train/test + the +plan-§8 audit. + ## 2026-08-11 — E-MY-OWN-PRE-REGISTRATION-HAD-A-GAP-AND-I-NAMED-IT-1 **Status:** FINDING `[G]` — CT-F14, `comet_tail_f14.py` / `.json`, report diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 3253edfc..964ac987 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -825,3 +825,107 @@ careful pass would have missed. **Current position: the compression is ready for the audit-gate queue now; the predictor is not, and should not be represented as more than "suggestive" until a properly-powered test clears its own bar without pooling assistance.** §6 is marked accordingly. + +## 9. Reframe — the spine is found; the moderators are missing (operator, 2026-08-11) + +Operator ruling on how to read the whole chain, and it is quantitatively +better than my "borderline" framing: + +> *"Wir haben ein Spine gefunden — die Stellschrauben müssen noch mit den +> Variablen der bekannten Modelle moduliert werden. Uns fehlen die +> Moderatoren; aber wir haben bereits das Gerüst, um das Zentrum und die +> Dynamik zu modellieren."* + +**Why this framing is not spin — it is the statistically correct reading of +the residual.** A directional main effect at 0.68–0.73 sign consistency +whose residual were *random* would be a dying claim. This chain's residual +is not random: it runs **monotonically with a measured variable** — the +height ladder (§5.2/5.8), ≈ −40° at 1000 hPa climbing smoothly through zero +in the mid-troposphere, spread 92–102°, 3–5× the measured apparatus noise, +on both storms it was measured on. *Main effect + structured residual + +identified covariate* is the signature of a **missing moderator**, not of a +null. A null does not produce a ladder. `[H]` at the ladder's n=2; `[G]` +that the framing follows if the ladder replicates. + +### 9.1 What is established (the spine) `[G]` + +**Center (place) + ring profile (~12 values) + one wn-1 dipole (2 values) += 93–97 % of in-disk MSLP variance** — replicated across three independent +samples spanning 1980–2021, 41+ storms, four seasons, never shaken once +(N3/N4; §5.11's own subset: median wn1_frac 0.60, R² 0.90). This is a +skeleton that models the **center and the first asymmetry mode of the +dynamics** in ~14 bytes plus an address — which is, as the operator notes, +already more explicit structure than a learned model exposes. + +### 9.2 The DRY moderators — measured in this chain, not yet wired `[H]` + +| moderator | measured evidence | wiring | +|---|---|---| +| **Steering level** (baroclinic tilt) | the 92–102° monotone height ladder, zero-crossing 400–650 hPa (§5.2/5.8) | score the dipole against the *steering-level* motion (500–700 hPa flow) instead of the 6h surface displacement — the single most promising fix, **CT-F16** | +| **Displacement magnitude** (label noise) | 6/7 pooled at ≥250 km vs 14/20 unfiltered; CT-F14 0.684 | model the motion-bearing *uncertainty* explicitly instead of a hard cutoff | +| **Surface type / friction** | +14° ocean vs +34° land inflow, paired within one disk (§5.7) | a wind-level correction; second-order on the pressure dipole | +| **Latitude / f, regime** | the low-wn1 July cases; the 75°N outlier | intake covariates, already computed per storm | + +### 9.3 The MOIST sector — not modeled at all (operator, same ruling) `[S]` + +> *"Außerdem haben wir Feuchtigkeit und Abregnen im Aufwind an der Kollision +> zwischen den Gebieten nicht modelliert — das ist eine Art Entropie bei +> Verdunstung und Abregnen."* + +Correct, and the "entropy" word is the *technically* right one, not a +metaphor. Everything in this chain is **dry, adiabatic, balanced dynamics**. +The missing half is diabatic: moisture converges into the collision zone +between air masses (the front), rises, condenses — releasing latent heat +that deepens the low — and **rains out irreversibly**: the water leaves the +column, the heat stays. That one-way flow is moist **entropy production**, +and treating the storm as a heat engine bounded by it is established +literature (Emanuel's potential-intensity Carnot frame; Pauluis' moist +entropy budgets). The state variable is equivalent potential temperature θe; +the sink is precipitation. + +Three things make this *tractable on this substrate, now*, rather than +aspirational: + +1. **The store has the variables** (verified in the `.zmetadata` earlier + this arc): `specific_humidity` (13 levels), `temperature` (13 levels) — + together θe; `total_column_water_vapour`; `total_precipitation_6hr`; + `vertical_velocity` (13 levels — the updraft itself). +2. **θe and TCWV are scalar fields** — the *same* ring/wn-1 decomposition + applies verbatim. The moisture spine costs nothing new. +3. **A diabatic-dominance moderator falls out for free:** the storms where + the dry spine's prediction failed worst (the July cases, wn1_frac + 0.19–0.36) are plausibly the diabatically-driven ones. Precip-per-disk / + TCWV-dipole-strength is a computable gate variable at intake. + +Named falsifiers, NOT run, `[S]` until probed: **CT-M1** — the TCWV/θe wn-1 +dipole leads the pressure dipole in bearing (moisture converges *ahead* of +the low, ≈90° from the left-of-motion low pole); **CT-M2** — 6h disk +precipitation is predicted by TCWV × mid-level ascent (`vertical_velocity` +at 700/500 hPa) — the rain-out entropy sink as a budget check; **CT-M3** — +adding the diabatic-dominance gate as a moderator cleans the directional +claim's residual where the displacement filter alone did not. + +### 9.4 The brutal step — learn the moderator matrix on the substrate's own machinery `[S]` + +Operator: *"du könntest sogar brutal sein und domino.rs / LSTM modellieren."* +The shapes already exist and are proven: + +- **The spine is a board state.** Per storm and timestep: ~16 spine values + (center, profile, dipole) + the moderator covariates (steering vector, f, + surface fraction, diabatic gate). A moderator set IS a weight matrix `W`; + `domino.rs`'s symbiont step (`C = A·W`, 16-board AMX/int8 tile-GEMM with + requantize feedback) executes exactly this — and the stencil-as-GEMM + path is already **byte-proven on real WB2 data** in ndarray's + `examples/geostrophic_stencil.rs` (4/4 pre-registered bars, corr 0.9985). +- **The recurrence is an LSTM-shaped problem.** Successive 6h spine states + are a short sequence; the workspace already carries byte-parity-proven + int8 LSTM machinery (`tesseract-recognizer`, `E-OCR-LSTM-1`) consuming + the same `ndarray` tile-GEMM. +- **The hybrid is the honest architecture:** explicit physics as the spine + (this report), learned weights as the moderators — the NeuralGCM-shaped + split, on a 512-byte-per-storm substrate encoding, with the training + discipline this arc has already built (pre-registration, held-out decades, + the audit gate). + +Gate, unchanged: train/test on disjoint decades, pre-registered bars, +adversarial audit (plan §8) before any of it is called more than a probe. From 5302828ee249e33a388ea9ade5a5700eb83a8064 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 22:58:19 +0000 Subject: [PATCH 13/23] =?UTF-8?q?probes:=20address=20the=2016=20PR=20#926?= =?UTF-8?q?=20review=20findings=20=E2=80=94=20one=20corrects=20the=20headl?= =?UTF-8?q?ine?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit External review of PR #926 (14 CodeRabbit + 2 Codex). Four findings changed measured numbers; two of those IMPROVED results, one worsened the headline. CHANGED NUMBERS 1. THE COMPRESSION HEADLINE MEASURED A MODEL NOBODY CLAIMED (codex P1). decompose() fits a1[b], b1[b] PER RING -- 12 rings x 2 = 24 free dipole parameters -- so the published R2 0.972/0.926 belongs to a 36-PARAMETER model, while the storage claim describes 14 values ("~12 ring means + ONE dipole = 2 values"). Measured properly, the constrained 2-parameter dipole (one amplitude slope + one bearing -- the linear-background form the report's own §2 derives) gives 0.943/0.909. Corrected headline: 90.9-94.3%, NOT 93-97%. The finding survives (14 values still lift 29-63% -> 91-94%), but claim and measurement had drifted ~2.5x in parameter count across six probes and several report rewrites. comet_tail_probe.py now measures BOTH and emits the parameter counts so the two cannot drift again. 2. SUNFLOWER E2 WAS NOT A CONTROLLED COMPARISON (codex P2 + coderabbit). grid_pts(n) returned every in-disk lattice point: 80 grid samples against 64 spiral (293 vs 256, 1085 vs 1024), and NN reconstruction improves with samples. With exactly n enforced the verdict improves IN THE SPIRAL'S FAVOUR -- 234.5 vs 269.0 Pa (n=64), 119.1 vs 123.3 (256), 58.9 vs 59.9 (1024): the spiral now wins at every budget where the arc had recorded "parity". The original result was PESSIMISTIC, not optimistic. 3. THE VOXEL-CHESS PALETTE ARM WAS A HYBRID (coderabbit). geo_corr took the palette geostrophic winds but closed over module-level RAW u/v, so "u8 max dev 0.0047" compared palette geostrophy against raw observations. Observed fields are now explicit parameters; the palette arm passes u8/v8. 4. go_territory's explained variance re-centred the residual after every atom (res.var()), excluding the residual mean from the error. Fixed to a fixed centered-field denominator over the residual mean-square: K=10 matched 0.530 -> 0.523, and NO verdict flips (A-E1/A-E2 still fail their bars). VACUOUS ASSERTION (the arc's house style, 4th instance) E6's rises_then_decays required only an interior maximum plus a lower final value, so it accepted a profile that DECREASED before rising -- which the committed run literally did (12.190 -> 12.163 m/s before the 525 km peak) while reporting true. Now asserts monotone rise-to-peak and decay-after, with a stated 0.05 m/s tolerance. Still passes, but now because the profile is Rankine-shaped rather than because the test could not fail. LATENT BUGS (no committed run hit them, so no numbers move) - find_center returned grid cell (0,0) when a `near`-limited mask was fully masked, instead of None. Guarded in all six probes. - subgrid_min's 3x3 slice did not wrap in longitude and would have raised on any centre at the 0 deg seam (centres come from a global scan, so it is reachable). Columns now wrap; rows clamp -- the poles are not periodic. LABELLING / PROVENANCE CT-F12 can no longer emit pass=true below its evaluable minimum; F7d's test now uses the 40 deg its own key and docstring pre-register (it tested 35); comet_tail_followup.json persists per-storm centre/bearing/displacement instead of "storms": {}; go_territory_probe.json is written beside the probe. Dead code dropped (unused dland decomposition, unused ci/cj). BOARD - EPIPHANIES: "the signature of a missing moderator, NOT of a null" softened to "consistent with a missing moderator and requires independent validation" -- a monotonic residual does not by itself exclude misspecification, centre/label error, selection, or chance (coderabbit). - New entry E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1 with the extracted rule: a number in a headline must be produced by code whose parameter count matches the headline's own description of the object. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/COMET_TAIL_REPORT.md | 87 ++++++++++++++++++- probes/weather-p1/comet_tail_f10_f11.py | 27 ++++-- probes/weather-p1/comet_tail_f14.py | 18 +++- probes/weather-p1/comet_tail_f4_f7.py | 23 ++++- probes/weather-p1/comet_tail_f5_n10.py | 22 +++-- probes/weather-p1/comet_tail_followup.py | 26 +++++- probes/weather-p1/comet_tail_probe.json | 12 +++ probes/weather-p1/comet_tail_probe.py | 33 ++++++- probes/weather-p1/go_territory_probe.json | 44 +++++----- probes/weather-p1/go_territory_probe.py | 9 +- .../weather-p1/sunflower_cyclone_probe.json | 12 +-- probes/weather-p1/sunflower_cyclone_probe.py | 29 +++++-- probes/weather-p1/voxel_chess_probe.json | 11 ++- probes/weather-p1/voxel_chess_probe.py | 42 ++++++--- 14 files changed, 316 insertions(+), 79 deletions(-) diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 964ac987..b6816518 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -48,6 +48,29 @@ generalization beyond n=2. > now with a specific, falsifiable next step (CT-F14) rather than a closed > question. > +> **⚠⚠⚠⚠ CORRECTION 2026-08-11 (external review of PR #926) — THE +> COMPRESSION NUMBERS IN THIS REPORT WERE FROM THE WRONG MODEL.** Codex +> flagged, and re-measurement confirms, that `decompose()` fits `a1[b]`, +> `b1[b]` **per ring** — 12 rings × 2 = **24** free dipole parameters, not the +> **2** ("amplitude slope + bearing") the storage claim describes. So the +> published R² 0.972 / 0.926 belongs to a **36-parameter** model, while the +> claimed 14-value representation is a different, more constrained one. Both +> are now measured (`comet_tail_probe.py`, `R2_profile_wn1_constrained_2param`): +> +> | model | params | storm 1 | storm 2 | +> |---|---:|---:|---:| +> | ring profile only | 12 | 0.635 | 0.294 | +> | + per-ring dipole (what was published) | 36 | 0.972 | 0.926 | +> | **+ constrained 2-param dipole (what was CLAIMED)** | **14** | **0.943** | **0.909** | +> +> **The corrected headline is 90.9–94.3 %, not 93–97 %.** The structural +> finding survives — 14 values still lift a storm from 29–63 % to 91–94 % — +> but every "93–97 %" in this document is an overstatement of ~2.5× in +> parameter count, and is superseded by the table above. The constrained model +> is the physically motivated one (a linear background gives exactly one +> amplitude slope and one bearing, §2), so this is a correction of the +> MEASUREMENT to the claim, not a retreat from the claim. +> > **⚠⚠⚠ UPDATE 2026-08-11 (§5.11, CT-F14, the properly-powered test).** The > pre-registered decision rule technically fires "established" on a pooled > 3-sample figure (p=0.0145) — but the single test this whole exercise was @@ -723,9 +746,11 @@ this quantity. No stronger attribution is claimed at n = 2. ## 6. Product / encoding consequence `[S]` -> **⚠ Read with §5.9–5.11.** The ring-profile + dipole compression below is -> solid — it generalized cleanly across every sample this arc ran (N3/N4, -> and CT-F14's own qualifying subset: median wn1_frac 0.60, R² 0.90). The +> **⚠ Read with §5.9–5.11 AND the compression correction in §1.** The figures +> below say 93–97 %; the honest number for the 14-value model they describe is +> **90.9–94.3 %** (the 93–97 % belongs to a 36-parameter per-ring fit). The +> compression is real and generalized cleanly across every sample this arc ran +> (N3/N4, and CT-F14's own qualifying subset: median wn1_frac 0.60, R² 0.90). The > *motion-encoding* half ("the dipole encodes the motion") depends on the > signed relationship CT-F14 — the properly-powered test built to settle it — > did NOT independently establish (§5.11). Treat the compression as ready @@ -738,7 +763,9 @@ If CT-F1..F3 hold up, the compact representation of a surface low is: storm ≈ CENTER (place) — 1 address + p̄(r) ring-profile means — ~12 bytes (12 × 100 km rings, u8-quantizable per voxel-chess probe: u8 max dev 0.0047) + (a₁,b₁) ONE dipole vector — 2 values (amplitude slope + bearing) - = 93–97 % of in-disk MSLP variance + = 90.9–94.3 % of in-disk MSLP variance [corrected 2026-08-11; the + 93–97 % previously printed here was a 36-parameter per-ring fit, not + this 14-value model — see the §1 correction] ``` - Maps directly onto `highheelbgz`'s 3-integer spiral **address** form @@ -826,6 +853,58 @@ ready for the audit-gate queue now; the predictor is not, and should not be represented as more than "suggestive" until a properly-powered test clears its own bar without pooling assistance.** §6 is marked accordingly. +## 8b. External review of PR #926 — what it changed (2026-08-11) + +16 findings (14 CodeRabbit + 2 Codex). Four changed measured numbers; the rest +were latent bugs or labelling. Recorded because two of them make the arc's own +results BETTER and one makes the headline WORSE — the review is not a formality. + +**Changed published numbers:** + +1. **The compression claim was measured on the wrong model** (Codex P1) — see + the §1 correction. 93–97 % → **90.9–94.3 %** for the 14-value model actually + claimed. The most consequential finding in the review. +2. **Sunflower E2 was not a controlled comparison** (Codex P2 + CodeRabbit): + `grid_pts(n)` returned every in-disk lattice point, so the grid arm ran on + 80 samples against the spiral's 64 (293 vs 256, 1085 vs 1024) — and + nearest-neighbour reconstruction improves with samples, so the arm being + compared was systematically advantaged. With EXACTLY n enforced the verdict + **improves in the spiral's favour**: 234.5 vs 269.0 Pa (n=64), 119.1 vs + 123.3 (n=256), 58.9 vs 59.9 (n=1024) — the spiral now wins at every budget, + where the earlier write-up recorded "parity". The original result was + PESSIMISTIC, not optimistic. +3. **The voxel-chess palette arm was a hybrid** (CodeRabbit): `geo_corr` + received the palette-derived geostrophic winds but closed over the + module-level RAW `u`/`v`, so "u8 max dev 0.0047" compared palette + geostrophy against raw observations — not the pre-registered palette + result. The observed fields are now explicit parameters and the palette arm + passes `u8`/`v8`. +4. **go_territory's explained variance re-centred the residual** (CodeRabbit): + `res.var()` subtracts the residual mean after every atom, excluding it from + the error. Fixed to a fixed centered-field denominator over the residual + mean-square; K=10 matched moves 0.530 → **0.523**, and **no verdict flips** + (A-E1 and A-E2 still fail their bars). + +**Vacuous assertion caught** (CodeRabbit): E6's `rises_then_decays` required +only an interior maximum plus a lower final value — it accepted a profile that +DECREASED before rising to the peak, which the committed run literally did +(12.190 → 12.163 m/s before the 525 km peak) while reporting `true`. Now +asserts monotone rise to the peak and decay after it, with a stated 0.05 m/s +tolerance; the run still passes, but now because the profile is Rankine-shaped +rather than because the test could not fail. + +**Latent bugs fixed (no committed run hit them, so no numbers move):** +`find_center` returned grid cell (0,0) when a `near`-limited mask was fully +masked, instead of `None`; `subgrid_min`'s 3×3 slice did not wrap in longitude +and would have raised on any centre at the 0° seam. Both are now guarded in +all six probes. + +**Labelling / provenance:** CT-F12 can no longer emit `pass: true` below its +evaluable minimum; F7d's threshold now matches the 40 deg its own key and +docstring pre-register (it tested 35); `comet_tail_followup.json` persists the +per-storm centre / bearing / displacement instead of `"storms": {}`; +`go_territory_probe.json` is written beside the probe rather than the cwd. + ## 9. Reframe — the spine is found; the moderators are missing (operator, 2026-08-11) Operator ruling on how to read the whole chain, and it is quantitatively diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py index d4e5067a..d3e21d8b 100644 --- a/probes/weather-p1/comet_tail_f10_f11.py +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -111,8 +111,14 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): if near is not None: _, _, r, _ = geom_ll(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): + return None return int(ci), int(cj) @@ -144,7 +150,13 @@ def decompose_ll(field, latc, lonc): def subgrid_min(field, ci, cj): - z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + # Longitude WRAPS: a centre at cj == 0 or NX-1 would otherwise slice a 3x2 + # neighbourhood, `A` would have 9 rows against 6 values and lstsq would + # raise. Centres come from a global scan, so the seam at 0 deg is reachable + # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the + # poles are not periodic. + ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) + z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), gx.ravel() ** 2, gy.ravel() ** 2, @@ -229,6 +241,10 @@ def err_deg(low_pole_rad, motion_rad): def stratum_verdict(name, subset, min_n=6, bar=0.70): + """Report a stratum. Below `min_n` this emits an explicit NO-VERDICT and + NEVER a `pass` field — an earlier version let CT-F12 (n=3) carry + `pass: true`, which a consumer could lift out of context as a valid + passing result (coderabbit on PR #926, 2026-08-11).""" if len(subset) < min_n: print(f"\n{name}: n={len(subset)} < min_n={min_n} -> NO-VERDICT " "(pre-registered minimum, stated before results)") @@ -266,8 +282,9 @@ def stratum_verdict(name, subset, min_n=6, bar=0.70): print("\n=== CT-F12 intersection (both filters, exploratory, no bar) ===") both_subset = [r for r in valid if r["displacement_km"] >= 250.0 and r["wn1_frac"] >= 0.40] -out["CT_F12"] = stratum_verdict("CT-F12 (both filters)", both_subset, - min_n=1) # report regardless; n likely small +# min_n stays at the real evaluable floor: CT-F12 is REPORTED for +# completeness, but it must not be able to emit a pass/fail verdict on n=3. +out["CT_F12"] = stratum_verdict("CT-F12 (both filters)", both_subset) json.dump(out, open("comet_tail_f10_f11.json", "w"), indent=2) print("\nwrote comet_tail_f10_f11.json") diff --git a/probes/weather-p1/comet_tail_f14.py b/probes/weather-p1/comet_tail_f14.py index 538eed39..28e325e5 100644 --- a/probes/weather-p1/comet_tail_f14.py +++ b/probes/weather-p1/comet_tail_f14.py @@ -128,8 +128,14 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): if near is not None: _, _, r, _ = geom_ll(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): + return None return int(ci), int(cj) @@ -161,7 +167,13 @@ def decompose_ll(field, latc, lonc): def subgrid_min(field, ci, cj): - z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + # Longitude WRAPS: a centre at cj == 0 or NX-1 would otherwise slice a 3x2 + # neighbourhood, `A` would have 9 rows against 6 values and lstsq would + # raise. Centres come from a global scan, so the seam at 0 deg is reachable + # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the + # poles are not periodic. + ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) + z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), gx.ravel() ** 2, gy.ravel() ** 2, diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py index 43a1f916..1a882564 100644 --- a/probes/weather-p1/comet_tail_f4_f7.py +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -136,8 +136,14 @@ def find_center(field, near=None, radius_km=600.0): if near is not None: _, _, r, _ = geom_ll(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): + return None return int(ci), int(cj) @@ -180,7 +186,13 @@ def err_deg(low_pole_rad, motion_rad): def subgrid_min(field, ci, cj): """2D quadratic LS fit on the 3x3; stationary point -> (lat, lon).""" - z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + # Longitude WRAPS: a centre at cj == 0 or NX-1 would otherwise slice a 3x2 + # neighbourhood, `A` would have 9 rows against 6 values and lstsq would + # raise. Centres come from a global scan, so the seam at 0 deg is reachable + # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the + # poles are not periodic. + ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) + z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), gx.ravel() ** 2, gy.ravel() ** 2, @@ -396,8 +408,11 @@ def inflow(mask): "F7a_across_storm_pass": f7a, "F7b_within_storm_paired_pass": f7b, "F7b_evaluable": bool(paired_ok), + # The key names 40 deg and the docstring pre-registers 40 deg, so the + # test uses 40 deg. It read >= 35.0, which would have reported true for + # a 36 deg median under a key claiming 40 (coderabbit on PR #926). "F7d_friction_alone_could_own_40deg_over_land": - bool(ml is not None and ml >= 35.0), + bool(ml is not None and ml >= 40.0), } print(f" storm chosen: ({lat[ci]:.2f}N, {lon_deg[cj]:.2f}E), " f"ring land fraction {lf:.2f}, anomaly {fa[ci, cj]:.0f} Pa") diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py index 2ef7b8e6..705e1eb5 100644 --- a/probes/weather-p1/comet_tail_f5_n10.py +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -150,9 +150,13 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): if near is not None: _, _, r, _ = geom_ll(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) - if not np.isfinite(fa[ci, cj]) if near is None else False: + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): return None return int(ci), int(cj) @@ -186,7 +190,13 @@ def decompose_ll(field, latc, lonc): def subgrid_min(field, ci, cj): - z = field[ci - 1:ci + 2, cj - 1:cj + 2].ravel() + # Longitude WRAPS: a centre at cj == 0 or NX-1 would otherwise slice a 3x2 + # neighbourhood, `A` would have 9 rows against 6 values and lstsq would + # raise. Centres come from a global scan, so the seam at 0 deg is reachable + # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the + # poles are not periodic. + ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) + z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") A = np.column_stack([np.ones(9), gx.ravel(), gy.ravel(), gx.ravel() ** 2, gy.ravel() ** 2, @@ -278,8 +288,6 @@ def sep_km(a, b): max_step = 0.0 for li in order: lev = int(levels[li]) - ci = int(np.argmin(np.abs(lat - cur[0]))) - cj = int(np.argmin(np.abs(((lon_deg - cur[1] + 180) % 360 - 180)))) found = find_center(z0[li], near=cur, radius_km=250.0) if found is None: walk.append({"level_hPa": lev, "found": False}) @@ -389,7 +397,6 @@ def sep_km(a, b): dA = decompose_ll(p0, la_a, lo_a) la_c, lo_c = centroid_ll(zeta10, ci0, cj0) dC = decompose_ll(p0, la_c, lo_c) - dland = decompose_ll(lsm.astype(np.float64), la_a, lo_a) errA = err_deg(dA["low_pole_rad"], mth) errC = err_deg(dC["low_pole_rad"], mth) @@ -401,7 +408,6 @@ def sep_km(a, b): "F8_error_C_vort_deg": errC, "F8_shrinks_at_vort_center": bool(abs(errC) < abs(errA)), }) - del dland # decompose_ll's own amp_by_ring omitted b1; land dipole recomputed below _, _, r_, th_ = geom_ll(la_a, lo_a) disk = r_ <= R_DISK lv, rr, tt = lsm[disk].astype(np.float64), r_[disk], th_[disk] diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py index fd8bd31e..21f47307 100644 --- a/probes/weather-p1/comet_tail_followup.py +++ b/probes/weather-p1/comet_tail_followup.py @@ -118,8 +118,14 @@ def find_center(field, near=None, radius_km=600.0): if near is not None: _, _, r, _ = geom(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): + return None return int(ci), int(cj) @@ -232,7 +238,21 @@ def static_key(var): "motion_rad": float(np.arctan2(mv[1], mv[0])), "disp_km": float(np.hypot(*mv))}) -out = {"store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK, "storms": {}} +# STORMS carries the centre, motion bearing and displacement each per-storm +# CT-F1/F2/F3 record is computed from; writing `{}` dropped that provenance +# (coderabbit on PR #926, 2026-08-11). +out = { + "store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK, + "storms": { + st["name"]: { + "center_t0": {"lat": float(lat[st["c0"][0]]), + "lon": float(lon_deg[st["c0"][1]])}, + "motion_bearing_deg": float(np.rad2deg(st["motion_rad"])), + "displacement_km": st["disp_km"], + } + for st in STORMS + }, +} # ============================ CT-F3 : APPARATUS ============================= print("\n=== CT-F3 APPARATUS (center sensitivity) — runs first, gates F1/F2 ===") diff --git a/probes/weather-p1/comet_tail_probe.json b/probes/weather-p1/comet_tail_probe.json index e9edd3a0..fb58d3cf 100644 --- a/probes/weather-p1/comet_tail_probe.json +++ b/probes/weather-p1/comet_tail_probe.json @@ -19,6 +19,12 @@ "alignment_error_deg": -41.96709521646496, "R2_profile": 0.6348125006744301, "R2_profile_wn1": 0.9722877428704574, + "R2_profile_wn1_constrained_2param": 0.9434382813469203, + "n_params_profile": 12, + "n_params_profile_wn1_perring": 36, + "n_params_profile_wn1_constrained": 14, + "constrained_slope_pa_per_km": 0.9408726353949788, + "constrained_bearing_deg": -116.09993446761843, "wn1_frac_of_resid": 0.9243451823302548, "low_pole_bearing_rad": 1.142178523788281, "amp_vs_r_corr": 0.8002995480605919, @@ -43,6 +49,12 @@ "alignment_error_deg": -40.209454299946835, "R2_profile": 0.29427821878043336, "R2_profile_wn1": 0.9259330249471591, + "R2_profile_wn1_constrained_2param": 0.909049958041129, + "n_params_profile": 12, + "n_params_profile_wn1_perring": 36, + "n_params_profile_wn1_constrained": 14, + "constrained_slope_pa_per_km": 1.0442717052955404, + "constrained_bearing_deg": -99.32825625591818, "wn1_frac_of_resid": 0.8948035500704673, "low_pole_bearing_rad": 1.3983140509546386, "amp_vs_r_corr": 0.9977355074833578, diff --git a/probes/weather-p1/comet_tail_probe.py b/probes/weather-p1/comet_tail_probe.py index 7df845b1..d70adcb3 100644 --- a/probes/weather-p1/comet_tail_probe.py +++ b/probes/weather-p1/comet_tail_probe.py @@ -74,8 +74,14 @@ def find_center(field, near=None, radius_km=600.0): if near is not None: _, _, r, _ = geom(*near) mask = mask & (r < radius_km) - ci, cj = np.unravel_index( - np.argmin(np.where(mask, fa, np.inf)), field.shape) + masked = np.where(mask, fa, np.inf) + ci, cj = np.unravel_index(np.argmin(masked), field.shape) + # An empty mask makes `masked` all-inf and argmin returns index 0, i.e. the + # function would report grid cell (0,0) as a storm centre. A `near`-limited + # search CAN be fully masked, so this must be checked on every path + # (coderabbit on PR #926, 2026-08-11). + if not np.isfinite(masked[ci, cj]): + return None return int(ci), int(cj) @@ -105,9 +111,20 @@ def decompose(field, ci, cj): wn1 = a1[rings] * np.cos(tt) + b1[rings] * np.sin(tt) resid1 = resid0 - wn1 # after profile + wn-1 + # CONSTRAINED dipole — the model the report's storage claim actually + # describes: ONE amplitude slope + ONE bearing, i.e. a1(r) = s*r*cos(t0), + # b1(r) = s*r*sin(t0), which is the linear-background signature from §2. + # The per-ring fit above has 2*nb = 24 free parameters and is NOT a + # 2-value representation; conflating the two overstated the compression + # (codex P1 on PR #926, 2026-08-11). Both are now reported. + X = np.column_stack([rr * np.cos(tt), rr * np.sin(tt)]) + coef, *_ = np.linalg.lstsq(X, resid0, rcond=None) + wn1_con = X @ coef + var_t = vals.var() e1 = 1.0 - resid0.var() / var_t # profile-only R2 - e2 = 1.0 - resid1.var() / var_t # profile + wn-1 R2 + e2 = 1.0 - resid1.var() / var_t # profile + per-ring wn-1 + e2c = 1.0 - (resid0 - wn1_con).var() / var_t # profile + 2-param dipole wn1_frac = wn1.var() / resid0.var() # amplitude-weighted dipole phase: bearing of the LOW pole @@ -118,6 +135,12 @@ def decompose(field, ci, cj): r_mid = (np.arange(nb) + 0.5) * RING a_corr = float(np.corrcoef(amp[1:], r_mid[1:])[0, 1]) return {"R2_profile": float(e1), "R2_profile_wn1": float(e2), + "R2_profile_wn1_constrained_2param": float(e2c), + "n_params_profile": int(nb), + "n_params_profile_wn1_perring": int(nb + 2 * nb), + "n_params_profile_wn1_constrained": int(nb + 2), + "constrained_slope_pa_per_km": float(np.hypot(*coef)), + "constrained_bearing_deg": float(np.rad2deg(np.arctan2(coef[1], coef[0]))), "wn1_frac_of_resid": float(wn1_frac), "low_pole_bearing_rad": float(low_pole), "amp_vs_r_corr": a_corr} @@ -161,6 +184,10 @@ def track(name, c0_hint=None): f"bar +/-45)") print(f" CT-E4 R2 profile-only {d['R2_profile']:.3f} -> " f"profile+wn1 {d['R2_profile_wn1']:.3f} (bar >= 0.80)") + print(f" [param count: {d['n_params_profile_wn1_perring']} per-ring " + f"vs {d['n_params_profile_wn1_constrained']} constrained]") + print(f" CONSTRAINED 2-param dipole (the storage claim's actual " + f"model): R2 {d['R2_profile_wn1_constrained_2param']:.3f}") print(f" CT-E5 corr(a1(r), r) = {d['amp_vs_r_corr']:.3f} " f"(linear-background signature; observation, no bar)") return res diff --git a/probes/weather-p1/go_territory_probe.json b/probes/weather-p1/go_territory_probe.json index 0ac58b86..8bb0982f 100644 --- a/probes/weather-p1/go_territory_probe.json +++ b/probes/weather-p1/go_territory_probe.json @@ -7,16 +7,16 @@ ], "A": { "explained_by_k": [ - 0.12049389150478484, - 0.21273185676323636, - 0.2676820303186759, - 0.3001417574768168, - 0.32981359089194373, - 0.38994206995613034, - 0.3957312192737913, - 0.4214904781858111, - 0.4586807878253665, - 0.529569066040689 + 0.1168690839787806, + 0.2127309490271857, + 0.26574796827848, + 0.2939871339283845, + 0.32918200372029927, + 0.3822168549947018, + 0.3903509061233552, + 0.4108893433743397, + 0.4380645265327874, + 0.523308291374023 ], "centers": [ { @@ -60,20 +60,20 @@ "lon": 320.5 } ], - "E1": 0.529569066040689, - "E2_random": 0.41734777491677777, + "E1": 0.523308291374023, + "E2_random": 0.40571381344120383, "E3_decay_ratios": [ - 0.765499097975342, - 0.5957435574545545, - 0.5907120043104531, - 0.9141122249909357, - 2.0264497384759745, - 0.09627965662462677, - 4.449575835509386, - 1.4437647358791685, - 1.9060954023336814 + 0.8202499907144846, + 0.553056413251751, + 0.5326434048669204, + 1.2463140812389346, + 1.5068915324297096, + 0.1533718099173651, + 2.5249948551017583, + 1.3231378233095652, + 3.136823930282756 ], - "E3_mean": 1.420914694839347 + "E3_mean": 1.3108315379014717 }, "B": { "n_centers": 20, diff --git a/probes/weather-p1/go_territory_probe.py b/probes/weather-p1/go_territory_probe.py index ef1b0812..9458d93b 100644 --- a/probes/weather-p1/go_territory_probe.py +++ b/probes/weather-p1/go_territory_probe.py @@ -31,6 +31,7 @@ Hoehenprofil (geopotential stacking) deliberately NOT here — next probe. """ import json +import os import urllib.request import numcodecs @@ -93,7 +94,11 @@ def fit_atom(res, ci, cj): def matching_pursuit(field, k_max, centers=None): """Greedy K radial atoms; centers picked from residual argmax (or given).""" res = field.copy() - v0 = field.var() + # Fixed denominator = the CENTERED field's variance; the numerator is the + # residual's MEAN SQUARE, not its variance. `res.var()` re-centres after + # every atom, so a non-zero residual mean was excluded from the error and + # explained variance came out inflated (coderabbit on PR #926). + v0 = float(np.mean((field - field.mean()) ** 2)) used, explained = [], [] for k in range(k_max): if centers is not None: @@ -106,7 +111,7 @@ def matching_pursuit(field, k_max, centers=None): atom, _ = fit_atom(res, ci, cj) res -= atom used.append((int(ci), int(cj))) - explained.append(1.0 - res.var() / v0) + explained.append(1.0 - float(np.mean(res ** 2)) / v0) return used, explained diff --git a/probes/weather-p1/sunflower_cyclone_probe.json b/probes/weather-p1/sunflower_cyclone_probe.json index 48662b3a..62709504 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.json +++ b/probes/weather-p1/sunflower_cyclone_probe.json @@ -16,8 +16,8 @@ "rmse_Pa": 234.48255636818175 }, "grid": { - "n": 80, - "rmse_Pa": 215.37203997558382 + "n": 64, + "rmse_Pa": 269.01833911376923 }, "random": { "n": 64, @@ -30,8 +30,8 @@ "rmse_Pa": 119.10369656260444 }, "grid": { - "n": 293, - "rmse_Pa": 111.55372624817461 + "n": 256, + "rmse_Pa": 123.32309279184084 }, "random": { "n": 256, @@ -44,8 +44,8 @@ "rmse_Pa": 58.854942160393385 }, "grid": { - "n": 1085, - "rmse_Pa": 57.7735059109564 + "n": 1024, + "rmse_Pa": 59.86267107651939 }, "random": { "n": 1024, diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py index bb5a62a7..aa6b16dd 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.py +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -113,12 +113,31 @@ def spiral_pts(n): def grid_pts(n): - """~n points on a uniform grid clipped to the disk.""" + """EXACTLY n points on a uniform grid clipped to the disk. + + E2 is an EQUAL-BUDGET comparison, so this must return n, not "about n". + An earlier version returned every in-disk lattice point, which handed the + grid arm 80 samples against the spiral's 64 (and 293 vs 256, 1085 vs 1024) + — nearest-neighbour reconstruction improves with samples, so the arm being + compared was systematically advantaged and the verdict was not a controlled + comparison (codex P2 + coderabbit on PR #926, 2026-08-11). + + The lattice is grown until it holds at least n in-disk points, then the n + CLOSEST to the disk centre are kept — a deterministic, spatially even + subset with no RNG and no dependence on iteration order. + """ side = int(np.ceil(np.sqrt(n * 4 / np.pi))) - g = (np.arange(side) + 0.5) / side * 2 * R_DISK_KM - R_DISK_KM - gx, gy = np.meshgrid(g, g) - m = np.hypot(gx, gy) <= R_DISK_KM - return gx[m], gy[m] + while True: + g = (np.arange(side) + 0.5) / side * 2 * R_DISK_KM - R_DISK_KM + gx, gy = np.meshgrid(g, g) + rr = np.hypot(gx, gy) + m = rr <= R_DISK_KM + if m.sum() >= n: + break + side += 1 + gx, gy, rr = gx[m], gy[m], rr[m] + keep = np.argsort(rr, kind="stable")[:n] + return gx[keep], gy[keep] def rand_pts(n, seed=7): diff --git a/probes/weather-p1/voxel_chess_probe.json b/probes/weather-p1/voxel_chess_probe.json index fe0f3d13..ea59a4f5 100644 --- a/probes/weather-p1/voxel_chess_probe.json +++ b/probes/weather-p1/voxel_chess_probe.json @@ -25,10 +25,10 @@ "n_nh_highs": 36622, "E3_sh_highs_ccw": 0.7194711726511773, "n_sh_highs": 76698, - "nh_corr_u": 0.38443625370572837, - "nh_corr_v": 0.42178482993212973, - "sh_corr_u": 0.8466889689137697, - "sh_corr_v": 0.710729073456687 + "nh_corr_u": 0.3837072803428654, + "nh_corr_v": 0.42140908709596736, + "sh_corr_u": 0.8456117083342138, + "sh_corr_v": 0.7089358037738284 }, "E4_max_popfrac_dev": 0.004749107357875787, "E6_rankine": { @@ -88,6 +88,9 @@ ], "peak_ring": 3, "cyclonic_at_peak": true, + "monotone_tol_ms": 0.05, + "rises_to_peak": true, + "decays_after_peak": true, "rises_then_decays": true } } \ No newline at end of file diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py index 60b4b564..00ba3e1f 100644 --- a/probes/weather-p1/voxel_chess_probe.py +++ b/probes/weather-p1/voxel_chess_probe.py @@ -112,13 +112,20 @@ def board_eval(zeta, p_anom, tag): return r -def geo_corr(ug, vg, tag): +def geo_corr(ug, vg, u_obs, v_obs, tag): + """Correlate the geostrophic estimate against the OBSERVED winds passed in. + + The observed fields are explicit parameters: an earlier version closed over + the module-level raw `u`/`v`, so the palette arm compared palette-derived + geostrophic winds against RAW observations — a hybrid, not the + pre-registered palette result (codex/coderabbit on PR #926, 2026-08-11). + """ r = {} for name, lo_b, hi_b in [("nh", 20, 70), ("sh", -70, -20)]: m = (lat[:, None] >= lo_b) & (lat[:, None] <= hi_b) \ - & np.ones_like(u, bool) - cu = np.corrcoef(ug[m], u[m])[0, 1] - cv = np.corrcoef(vg[m], v[m])[0, 1] + & np.ones_like(u_obs, bool) + cu = np.corrcoef(ug[m], u_obs[m])[0, 1] + cv = np.corrcoef(vg[m], v_obs[m])[0, 1] r[f"{name}_corr_u"], r[f"{name}_corr_v"] = float(cu), float(cv) print(f" [{tag}] geostrophic {name.upper()}: corr(u_g,u)={cu:.3f} " f"corr(v_g,v)={cv:.3f}") @@ -131,13 +138,13 @@ def geo_corr(ug, vg, tag): print("\n== RAW arm ==") zeta, p_anom, ug, vg = physics(p, u, v) -out["raw"] = {**board_eval(zeta, p_anom, "raw"), **geo_corr(ug, vg, "raw")} +out["raw"] = {**board_eval(zeta, p_anom, "raw"), **geo_corr(ug, vg, u, v, "raw")} print("\n== u8-PALETTE arm (p, u, v each quantized to 256 buckets) ==") p8, u8, v8 = quant_u8(p), quant_u8(u), quant_u8(v) zeta8, p_anom8, ug8, vg8 = physics(p8, u8, v8) out["palette_u8"] = {**board_eval(zeta8, p_anom8, "u8"), - **geo_corr(ug8, vg8, "u8")} + **geo_corr(ug8, vg8, u8, v8, "u8")} # E4: substrate fidelity of the popcount verdicts keys = ["E1_nh_lows_ccw", "E2_sh_lows_ccw", "E3_nh_highs_ccw", @@ -150,8 +157,11 @@ def geo_corr(ug, vg, tag): # E6: Rankine profile around the deepest NH low print("\n== E6 Rankine ('Gluecksrad') around the deepest NH low ==") nh_rows = lat > 15 -pa_nh = np.where(nh_rows[:, None], p_anom, np.inf) -ci, cj = np.unravel_index(np.argmin(pa_nh), pa_nh.shape) +# E6 says "the deepest NH low", so select on MSLP itself. Selecting on the +# zonal ANOMALY picks a different point and silently changes the wind profile +# and the serialized center (coderabbit on PR #926, 2026-08-11). +p_nh = np.where(nh_rows[:, None], p, np.inf) +ci, cj = np.unravel_index(np.argmin(p_nh), p_nh.shape) print(f" center: lat={lat[ci]:.2f} lon={cj * 0.25:.2f} " f"p'={p_anom[ci, cj]:.0f} Pa") lon = np.arange(p.shape[1]) * 0.25 @@ -171,11 +181,23 @@ def geo_corr(ug, vg, tag): f"(n={prof[-1]['n']})") vts = [q["vt_mean"] for q in prof] pk = int(np.argmax(vts)) +# A Rankine profile must actually RISE to the peak and DECAY after it. The +# earlier test only required an interior maximum plus a lower final value, +# which accepted a profile that DECREASED then rose to the peak — the +# committed run did exactly that (12.190 -> 12.163 m/s before the 525 km peak) +# and still reported true (coderabbit on PR #926, 2026-08-11). TOL absorbs +# ring-to-ring sampling noise; the monotonicity is what is being asserted. +TOL = 0.05 # m/s, ~0.4% of the observed peak +rise_ok = all(vts[i + 1] >= vts[i] - TOL for i in range(pk)) +decay_ok = all(vts[i + 1] <= vts[i] + TOL for i in range(pk, len(vts) - 1)) out["E6_rankine"] = {"center_lat": float(lat[ci]), "center_lon": float(lon[cj]), "profile": prof, "peak_ring": pk, "cyclonic_at_peak": vts[pk] > 0, - "rises_then_decays": 0 < pk < len(vts) - 1 - and vts[-1] < vts[pk]} + "monotone_tol_ms": TOL, + "rises_to_peak": bool(rise_ok), + "decays_after_peak": bool(decay_ok), + "rises_then_decays": bool(0 < pk < len(vts) - 1 + and rise_ok and decay_ok)} print(f" peak at ring {pk} ({prof[pk]['r_mid_km']} km): torque zone inside, " f"momentum zone outside -> rises_then_decays=" f"{out['E6_rankine']['rises_then_decays']}") From d03978bd94dbecea8887075d7116ded20624106a Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 22:59:14 +0000 Subject: [PATCH 14/23] board: record the PR #926 review findings + correct an overstatement The board half of commit 5302828e, which failed silently: the edit script asserted on ASCII hyphens where the file uses en-dashes, so the probe and report fixes committed while the board changes did not. Caught by re-reading the file rather than trusting the commit. Two changes: 1. CORRECTION in E-SPINE-FOUND-MODERATORS-MISSING-1. It read "is the signature of a missing moderator, NOT of a null. A null does not produce a ladder." That overstates what a monotonic residual establishes -- it SUPPORTS the hypothesis but does not discriminate it from model misspecification, centre/label error, selection effects, or chance. Now reads "consistent with a missing moderator and requires independent validation", with the original wording and its reason preserved in a correction note (CodeRabbit on PR #926). 2. NEW ENTRY E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1 -- the compression headline (93-97%) was computed from a 36-parameter per-ring fit while the storage claim describes 14 values; the constrained 2-parameter model actually gives 90.9-94.3%. Also records that two review findings IMPROVED results (sunflower's spiral now wins at every N once the sampling budget is equal; the voxel-chess palette arm was a hybrid), and that E6 carried a fourth vacuous assertion found by a reviewer rather than the author. Extracted rule: a number in a headline must be produced by code whose parameter count matches the headline's own description of the object. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 67 +++++++++++++++++++++++++++++++++---- 1 file changed, 61 insertions(+), 6 deletions(-) diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 7fc82efb..3b74fe22 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,48 @@ +## 2026-08-11 — E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1 + +**Status:** FINDING `[G]` — external review of PR #926 (14 CodeRabbit + 2 Codex +findings); re-measured and corrected in `comet_tail_probe.py` and +`COMET_TAIL_REPORT.md` §1 / §8b (commit 5302828e). + +**The arc's most-repeated number was measured on a model the arc never +claimed.** The compression headline — "center + ~12 ring means + ONE dipole +(2 values) = 93–97 % of in-disk variance" — came from a `decompose()` that +fits `a1[b]`, `b1[b]` **per ring**: 12 rings × 2 = **24** free dipole +parameters, so the published 0.972/0.926 belongs to a **36-parameter** model, +not the **14-value** one the storage claim describes. Measured properly, the +constrained 2-parameter dipole (one amplitude slope + one bearing — the +linear-background form the report's own §2 derives) gives **0.943 / 0.909**. +Corrected headline: **90.9–94.3 %, not 93–97 %.** + +The finding SURVIVES — 14 values still lift a storm from 29–63 % to 91–94 % — +but claim and measurement had drifted apart by ~2.5× in parameter count across +six probes and several report rewrites, and nobody in-session noticed. **The +tell was available the whole time:** the report described the representation +in one place ("2 values") and the code produced another ("per-ring"), and no +test tied the two together. + +**Two review findings IMPROVED results rather than damaging them**, which is +the argument for external review as more than ceremony: (a) sunflower E2 was +handing the grid arm up to 25 % more samples than the spiral (every in-disk +lattice point instead of exactly n) — with equal budgets enforced the **spiral +now wins at every N**, where the arc had recorded "parity"; the original result +was PESSIMISTIC. (b) The voxel-chess palette arm compared palette-derived +geostrophic winds against RAW observations, because `geo_corr` closed over +module-level `u`/`v` — a hybrid, not the pre-registered palette result. + +**A fourth vacuous assertion, in this arc's own documented house style:** E6's +`rises_then_decays` required only an interior maximum plus a lower final +value, so it accepted a profile that DECREASED before rising to the peak — and +the committed run did exactly that (12.190 → 12.163 m/s before the 525 km +peak) while reporting `true`. Found by a reviewer, not by the author, which is +the same asymmetry `E-ZERO-FOR-ELEVEN-...` already recorded. + +**Rule:** *a number that appears in a headline must be produced by code whose +parameter count matches the headline's own description of the object.* Where +prose says "N values", the probe should EMIT N and the report should print it +— which `comet_tail_probe.py` now does +(`n_params_profile_wn1_constrained`), so the two cannot silently drift again. + ## 2026-08-11 — E-SPINE-FOUND-MODERATORS-MISSING-1 **Status:** OPERATOR RULING (framing) + FINDING `[H]` — report §9, commit @@ -10,12 +55,22 @@ Dynamik zu modellieren. Außerdem haben wir Feuchtigkeit und Abregnen im Aufwind an der Kollision zwischen den Gebieten nicht modelliert — das ist eine Art Entropie bei Verdunstung und Abregnen."* -**Why this is the statistically correct reading, not spin:** a 0.68–0.73 -directional main effect whose residual were random would be a dying claim — -but this chain's residual runs MONOTONICALLY with a measured variable (the -92–102° height ladder, 3–5× apparatus noise). *Main effect + structured -residual + identified covariate* is the signature of a **missing moderator**, -not of a null. A null does not produce a ladder. +**Why this reading is defensible — stated at the strength the evidence +actually supports:** a 0.68–0.73 directional main effect whose residual were +random would be a dying claim — but this chain's residual runs MONOTONICALLY +with a measured variable (the 92–102° height ladder, 3–5× apparatus noise). +*Main effect + structured residual + identified covariate* is **consistent +with a missing moderator and requires independent validation**. It does NOT by +itself exclude model misspecification, centre/label error, selection effects, +or chance. + +> **Correction (CodeRabbit on PR #926, 2026-08-11).** This paragraph +> originally read "is the signature of a missing moderator, NOT of a null. A +> null does not produce a ladder." That overstated what a monotonic residual +> can establish — it supports the hypothesis, it does not discriminate it from +> the alternatives above. Corrected in place per the append-only rule's +> allowance for regrading; the directional predictor stays SUGGESTIVE and +> unpromoted either way, which is what the PR objective already said. **The three-part decomposition now on record (report §9):** 1. **Spine `[G]`** — center + ~12 ring means + 1 wn-1 dipole = 93–97 % of From 2357de19d1a5558d595bc5a9cf609794c9e7360b Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:01:01 +0000 Subject: [PATCH 15/23] probes: document the shared helpers (34% -> 91% docstring coverage) The last open CodeRabbit pre-merge check on PR #926. The probes carry extensive MODULE docstrings (the pre-registration blocks), but the helper functions they share -- geom_ll, find_center, decompose_ll, subgrid_min, wrap_deg, err_deg, sep_km, the binomial tails, the derivative kernels -- were undocumented in every copy. Since those helpers are duplicated across six files, a one-line contract on each is worth more than the line count suggests: it is the only place a reader learns, for instance, that d_dy flips sign because the row index grows southward, that find_center can return None, or that subgrid_min wraps in longitude but clamps in latitude. Docstrings state the CONTRACT (units, sign conventions, None-returns, what the metric is computed over), not a restatement of the function name. Coverage 34.25% -> 91.5% (107/117 functions). Verified after the pass: all 22 probe/support files compile, and comet_tail_probe.py re-ran end-to-end reproducing its committed numbers (wn1 0.924/0.895, R2 0.972/0.926 per-ring and 0.943/0.909 constrained, CT-E3 2/2) -- so the insertion touched no behaviour. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/comet_tail_f10_f11.py | 8 ++++++++ probes/weather-p1/comet_tail_f14.py | 9 +++++++++ probes/weather-p1/comet_tail_f4_f7.py | 10 ++++++++++ probes/weather-p1/comet_tail_f5_n10.py | 13 +++++++++++++ probes/weather-p1/comet_tail_followup.py | 3 +++ probes/weather-p1/comet_tail_probe.py | 2 ++ probes/weather-p1/go_territory_probe.py | 1 + probes/weather-p1/sunflower_cyclone_probe.py | 3 +++ probes/weather-p1/voxel_chess_probe.py | 1 + 9 files changed, 50 insertions(+) diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py index d3e21d8b..fc51f964 100644 --- a/probes/weather-p1/comet_tail_f10_f11.py +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -68,6 +68,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=600).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -75,6 +76,7 @@ def fetch(var, key): def t_index(dt): + """WB2 time index for a datetime: 6-hourly steps since 1959-01-01.""" return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) @@ -97,6 +99,7 @@ def t_index(dt): def geom_ll(latc, lonc): + """dx, dy, r (km) and azimuth theta (rad, CCW from east) relative to a CONTINUOUS (lat, lon) centre.""" phic = np.deg2rad(latc) dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) dphi = phi[:, None] - phic @@ -106,6 +109,7 @@ def geom_ll(latc, lonc): def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + """Deepest zonal-anomaly low; returns None when the (optionally `near`-limited) mask admits no finite candidate.""" fa = field - field.mean(axis=1, keepdims=True) mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) if near is not None: @@ -123,6 +127,7 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): def decompose_ll(field, latc, lonc): + """Ring-mean profile + per-ring wavenumber-1 fit about a continuous centre.""" _, _, r, th = geom_ll(latc, lonc) disk = r <= R_DISK vals, rr, tt = field[disk], r[disk], th[disk] @@ -155,6 +160,7 @@ def subgrid_min(field, ci, cj): # raise. Centres come from a global scan, so the seam at 0 deg is reachable # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the # poles are not periodic. + """Sub-grid minimum by 2-D quadratic fit on the 3x3 neighbourhood; longitude wraps, latitude clamps.""" ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") @@ -174,10 +180,12 @@ def subgrid_min(field, ci, cj): def wrap_deg(d): + """Wrap degrees into (-180, 180].""" return (d + 180.0) % 360.0 - 180.0 def err_deg(low_pole_rad, motion_rad): + """Signed alignment error, in degrees, of a low-pole bearing against the left-of-motion prediction.""" return float(wrap_deg(np.rad2deg( low_pole_rad - (motion_rad + np.pi / 2)))) diff --git a/probes/weather-p1/comet_tail_f14.py b/probes/weather-p1/comet_tail_f14.py index 28e325e5..9f87769e 100644 --- a/probes/weather-p1/comet_tail_f14.py +++ b/probes/weather-p1/comet_tail_f14.py @@ -85,6 +85,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=600).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -92,6 +93,7 @@ def fetch(var, key): def t_index(dt): + """WB2 time index for a datetime: 6-hourly steps since 1959-01-01.""" return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) @@ -114,6 +116,7 @@ def t_index(dt): def geom_ll(latc, lonc): + """dx, dy, r (km) and azimuth theta (rad, CCW from east) relative to a CONTINUOUS (lat, lon) centre.""" phic = np.deg2rad(latc) dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) dphi = phi[:, None] - phic @@ -123,6 +126,7 @@ def geom_ll(latc, lonc): def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + """Deepest zonal-anomaly low; returns None when the (optionally `near`-limited) mask admits no finite candidate.""" fa = field - field.mean(axis=1, keepdims=True) mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) if near is not None: @@ -140,6 +144,7 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): def decompose_ll(field, latc, lonc): + """Ring-mean profile + per-ring wavenumber-1 fit about a continuous centre.""" _, _, r, th = geom_ll(latc, lonc) disk = r <= R_DISK vals, rr, tt = field[disk], r[disk], th[disk] @@ -172,6 +177,7 @@ def subgrid_min(field, ci, cj): # raise. Centres come from a global scan, so the seam at 0 deg is reachable # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the # poles are not periodic. + """Sub-grid minimum by 2-D quadratic fit on the 3x3 neighbourhood; longitude wraps, latitude clamps.""" ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") @@ -191,15 +197,18 @@ def subgrid_min(field, ci, cj): def wrap_deg(d): + """Wrap degrees into (-180, 180].""" return (d + 180.0) % 360.0 - 180.0 def err_deg(low_pole_rad, motion_rad): + """Signed alignment error, in degrees, of a low-pole bearing against the left-of-motion prediction.""" return float(wrap_deg(np.rad2deg( low_pole_rad - (motion_rad + np.pi / 2)))) def binom_sf_ge(k, n, p=0.5): + """Exact one-sided binomial tail P(X >= k) for n trials at probability p.""" return sum(comb(n, i) * (p ** i) * ((1 - p) ** (n - i)) for i in range(k, n + 1)) diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py index 1a882564..326ed6bf 100644 --- a/probes/weather-p1/comet_tail_f4_f7.py +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -87,6 +87,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=600).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -94,6 +95,7 @@ def fetch(var, key): def static_key(var): + """Chunk key for a static (time-independent) variable: all-zero index of the right arity.""" return ".".join("0" * len(meta[f"{var}/.zarray"]["chunks"])) @@ -127,10 +129,12 @@ def geom_ll(latc, lonc): def geom(ci, cj): + """dx, dy, r (km) and azimuth theta (rad, CCW from east) relative to a grid-point centre.""" return geom_ll(lat[ci], lon_deg[cj]) def find_center(field, near=None, radius_km=600.0): + """Deepest zonal-anomaly low; returns None when the (optionally `near`-limited) mask admits no finite candidate.""" fa = field - field.mean(axis=1, keepdims=True) mask = lat[:, None] > 15 if near is not None: @@ -176,10 +180,12 @@ def decompose_ll(field, latc, lonc): def wrap_deg(d): + """Wrap degrees into (-180, 180].""" return (d + 180.0) % 360.0 - 180.0 def err_deg(low_pole_rad, motion_rad): + """Signed alignment error, in degrees, of a low-pole bearing against the left-of-motion prediction.""" return float(wrap_deg(np.rad2deg( low_pole_rad - (motion_rad + np.pi / 2)))) @@ -226,6 +232,7 @@ def centroid_ll(weight, ci, cj, radius_km=300.0): def d_dx(f): + """Zonal derivative in per-km units (centred differences, cos(lat) metric).""" dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) o = np.zeros_like(f) o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) @@ -233,6 +240,7 @@ def d_dx(f): def d_dy(f): + """Meridional derivative in per-km units; the row index grows southward, so the sign is flipped.""" dyk = R_E * np.deg2rad(0.25) o = np.zeros_like(f) o[1:-1, :] = -(f[2:, :] - f[:-2, :]) / (2 * dyk) # index grows southward @@ -240,6 +248,7 @@ def d_dy(f): def sep_km(a, b): + """Great-circle-ish separation between two (lat, lon) points, in km.""" la, lo = a lb, lob = b dlon = np.deg2rad((lo - lob + 180) % 360 - 180) @@ -349,6 +358,7 @@ def sep_km(a, b): def inflow(mask): + """Signed 10m cross-isobar inflow angle in degrees; positive = turned toward the low (NH friction sign).""" bg = np.arctan2(gx[mask], -gy[mask]) ba = np.arctan2(v10[mask], u10[mask]) return wrap_deg(np.rad2deg(ba - bg)) diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py index 705e1eb5..f4ba64db 100644 --- a/probes/weather-p1/comet_tail_f5_n10.py +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -98,6 +98,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=600).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -105,10 +106,12 @@ def fetch(var, key): def static_key(var): + """Chunk key for a static (time-independent) variable: all-zero index of the right arity.""" return ".".join("0" * len(meta[f"{var}/.zarray"]["chunks"])) def t_index(dt): + """WB2 time index for a datetime: 6-hourly steps since 1959-01-01.""" return int(round((dt - EPOCH).total_seconds() / 3600 / 6)) @@ -136,6 +139,7 @@ def t_index(dt): def geom_ll(latc, lonc): + """dx, dy, r (km) and azimuth theta (rad, CCW from east) relative to a CONTINUOUS (lat, lon) centre.""" phic = np.deg2rad(latc) dlon = np.deg2rad((lon_deg[None, :] - lonc + 180) % 360 - 180) dphi = phi[:, None] - phic @@ -145,6 +149,7 @@ def geom_ll(latc, lonc): def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): + """Deepest zonal-anomaly low; returns None when the (optionally `near`-limited) mask admits no finite candidate.""" fa = field - field.mean(axis=1, keepdims=True) mask = (lat[:, None] > lat_lo) & (lat[:, None] < lat_hi) if near is not None: @@ -162,6 +167,7 @@ def find_center(field, near=None, radius_km=600.0, lat_lo=25.0, lat_hi=75.0): def decompose_ll(field, latc, lonc): + """Ring-mean profile + per-ring wavenumber-1 fit about a continuous centre.""" _, _, r, th = geom_ll(latc, lonc) disk = r <= R_DISK vals, rr, tt = field[disk], r[disk], th[disk] @@ -195,6 +201,7 @@ def subgrid_min(field, ci, cj): # raise. Centres come from a global scan, so the seam at 0 deg is reachable # (coderabbit on PR #926, 2026-08-11). Rows are clamped, not wrapped — the # poles are not periodic. + """Sub-grid minimum by 2-D quadratic fit on the 3x3 neighbourhood; longitude wraps, latitude clamps.""" ri = np.clip(np.array([ci - 1, ci, ci + 1]), 0, field.shape[0] - 1) z = np.take(field[ri, :], [cj - 1, cj, cj + 1], axis=1, mode="wrap").ravel() gy, gx = np.meshgrid([-1., 0., 1.], [-1., 0., 1.], indexing="ij") @@ -214,6 +221,7 @@ def subgrid_min(field, ci, cj): def centroid_ll(weight, ci, cj, radius_km=300.0): + """Half-max-weighted centroid of `weight` near a centre, as continuous (lat, lon).""" dx, dy, r, _ = geom_ll(lat[ci], lon_deg[cj]) m = (r <= radius_km) & (weight > 0) if not m.any(): @@ -229,6 +237,7 @@ def centroid_ll(weight, ci, cj, radius_km=300.0): def d_dx(f): + """Zonal derivative in per-km units (centred differences, cos(lat) metric).""" dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) o = np.zeros_like(f) o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) @@ -236,6 +245,7 @@ def d_dx(f): def d_dy(f): + """Meridional derivative in per-km units; the row index grows southward, so the sign is flipped.""" dyk = R_E * np.deg2rad(0.25) o = np.zeros_like(f) o[1:-1, :] = -(f[2:, :] - f[:-2, :]) / (2 * dyk) @@ -243,15 +253,18 @@ def d_dy(f): def wrap_deg(d): + """Wrap degrees into (-180, 180].""" return (d + 180.0) % 360.0 - 180.0 def err_deg(low_pole_rad, motion_rad): + """Signed alignment error, in degrees, of a low-pole bearing against the left-of-motion prediction.""" return float(wrap_deg(np.rad2deg( low_pole_rad - (motion_rad + np.pi / 2)))) def sep_km(a, b): + """Great-circle-ish separation between two (lat, lon) points, in km.""" la, lo = a lb, lob = b dlon = np.deg2rad((lo - lob + 180) % 360 - 180) diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py index 21f47307..43909193 100644 --- a/probes/weather-p1/comet_tail_followup.py +++ b/probes/weather-p1/comet_tail_followup.py @@ -85,6 +85,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=600).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -174,10 +175,12 @@ def decompose(field, ci, cj): def wrap_deg(d): + """Wrap degrees into (-180, 180].""" return (d + 180.0) % 360.0 - 180.0 def err_deg(low_pole_rad, motion_rad): + """Signed alignment error, in degrees, of a low-pole bearing against the left-of-motion prediction.""" pred = (motion_rad + np.pi / 2) % (2 * np.pi) return float(wrap_deg(np.rad2deg(low_pole_rad - pred))) diff --git a/probes/weather-p1/comet_tail_probe.py b/probes/weather-p1/comet_tail_probe.py index d70adcb3..6c740792 100644 --- a/probes/weather-p1/comet_tail_probe.py +++ b/probes/weather-p1/comet_tail_probe.py @@ -45,6 +45,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=180).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -147,6 +148,7 @@ def decompose(field, ci, cj): def track(name, c0_hint=None): + """Track one storm t -> t+6h and score its wn-1 dipole against the motion bearing.""" ci0, cj0 = find_center(p0, near=c0_hint) ci1, cj1 = find_center(p1, near=(ci0, cj0)) dx, dy, _, _ = geom(ci0, cj0) diff --git a/probes/weather-p1/go_territory_probe.py b/probes/weather-p1/go_territory_probe.py index 9458d93b..88796142 100644 --- a/probes/weather-p1/go_territory_probe.py +++ b/probes/weather-p1/go_territory_probe.py @@ -49,6 +49,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=180).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py index aa6b16dd..68956313 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.py +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -48,6 +48,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=180).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) @@ -106,6 +107,7 @@ def axisym_index(ci_, cj_): def spiral_pts(n): + """n sunflower/golden-angle points on the disk (equal-area: r = sqrt((k+0.5)/n)*R).""" k = np.arange(n) r = np.sqrt((k + 0.5) / n) * R_DISK_KM th = k * GOLDEN_ANGLE @@ -141,6 +143,7 @@ def grid_pts(n): def rand_pts(n, seed=7): + """n uniform random points on the disk, seeded for reproducibility.""" rng = np.random.default_rng(seed) r = np.sqrt(rng.uniform(0, 1, n)) * R_DISK_KM th = rng.uniform(0, 2 * np.pi, n) diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py index 00ba3e1f..19b9d8f1 100644 --- a/probes/weather-p1/voxel_chess_probe.py +++ b/probes/weather-p1/voxel_chess_probe.py @@ -43,6 +43,7 @@ def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" za = meta[f"{var}/.zarray"] raw = op.open(f"{B}/{var}/{key}", timeout=180).read() dec = numcodecs.get_codec(za["compressor"]).decode(raw) From ec1b079eab19f8344058ce2440ddaa88e5bd457a Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:37:24 +0000 Subject: [PATCH 16/23] =?UTF-8?q?probe(weather):=20the=20three-register=20?= =?UTF-8?q?picture,=20measured=20=E2=80=94=20there=20is=20no=20absolute=20?= =?UTF-8?q?anchor,=20and=20rank-normalised=20palette256=20is=20the=20only?= =?UTF-8?q?=20frame=20where=20cross-variable=20distance=20is=20defined=20a?= =?UTF-8?q?t=20all?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Operator correction (2026-08-11): "du lebst noch in der Vorstellung dass alles absolut ist -- die Relativitaetstheorie widerlegt sogar das" + "in der Statistik ist es gold wert alles auf Palette256 normalized zu haben". I had encoded pressure against a FIXED 1000 hPa reference and called it "a stable convention". It is an ABSOLUTE anchor and there is none: 994 hPa is unremarkable over Iceland and a record in the subtropics. Measured on the real global ERA5 field (t=91246, n=1,038,240), three 1-byte readings of one value: band A affine B rank C fisher-rank storm tail (bottom 1%) 10.71 204.54 24.74 lower shoulder (1-10%) 10.76 51.13 45.38 bulk (40-60%) 10.79 2.34 16.50 high tail (top 1%) 10.71 88.22 6.86 OVERALL 10.77 27.58 24.55 [Pa RMSE] R1 A flat across bands (1.01, bar <=1.5) PASS -- the band decomposition is meaningful, not an artefact. R2 B beats A in the bulk (2.34 vs 10.79, 4.6x) PASS -- equal-probability buckets are denser in Pa exactly where the mass is. R3 B WORSE than A in the storm tail (204.54 vs 10.71) PASS -- the cost of the rank register, measured rather than assumed. R4 C beats B in the storm tail (24.74 vs 204.54, 8.3x) PASS -- the rim stretch does what helix's fisher_z.rs says it does. NOT pre-registered, and it does NOT hold: C does not beat A in the storm tail (24.74 vs 10.71). The MSLP distribution is left-skewed -- the low tail runs 70 hPa below the median, the high tail only 25 hPa above -- and the rim stretch does not fully compensate that asymmetry, while the uniform register covers it by construction. C DOES beat A in the high tail (6.86 vs 10.71). Recorded because it is the one expectation this probe disappointed. R5, the actual payoff -- after rank normalisation the SAME u8 denotes the same rarity in MSLP, 2m temperature and 10m wind: max spread 0.00007 against a one-bucket tolerance of 0.00391 (56x inside). For the ABSOLUTE register the same question is not worse-answered but UNDEFINED: Pa, K and m/s share no unit, so byte 128 of each denotes no common quantity. CONSEQUENCE for my own earlier objection. "Fisher-Z breaks the stencil" was right about affinity but judged C by the WRONG metric -- Pa reconstruction is not what an extremity register is for; R5 is. The three are not competing candidates for one job but three readings of one value: A carries quantitative differences (affine, the stencil/GEMM lane), B carries probability mass (bulk resolution), C carries cross-variable comparability (the only frame in which a multi-variable storm state has a defined distance). Scope limit stated in the probe: the reference distribution is the global field at ONE timestep, the population a rolling floor would have observed. A multi-year climatology is the honest reference for operational extremity. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/three_register_probe.json | 80 +++++++ probes/weather-p1/three_register_probe.py | 223 ++++++++++++++++++++ 2 files changed, 303 insertions(+) create mode 100644 probes/weather-p1/three_register_probe.json create mode 100644 probes/weather-p1/three_register_probe.py diff --git a/probes/weather-p1/three_register_probe.json b/probes/weather-p1/three_register_probe.json new file mode 100644 index 00000000..a61e6e6d --- /dev/null +++ b/probes/weather-p1/three_register_probe.json @@ -0,0 +1,80 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "t0": 91246, + "palette": 256, + "global_range_pa": [ + 94166.734375, + 103717.75 + ], + "uniform_step_pa": 37.30865478515625, + "bands": { + "storm tail (bottom 1%)": { + "n": 10383, + "A_affine": 10.710753687683953, + "B_rank": 204.54202672789424, + "C_fisher_rank": 24.740686546773482 + }, + "lower shoulder (1-10%)": { + "n": 93450, + "A_affine": 10.762425611574091, + "B_rank": 51.12867755561899, + "C_fisher_rank": 45.37628239859642 + }, + "bulk (40-60%)": { + "n": 207737, + "A_affine": 10.78562651417146, + "B_rank": 2.3376101713669164, + "C_fisher_rank": 16.49641288115609 + }, + "high tail (top 1%)": { + "n": 10387, + "A_affine": 10.71190579425477, + "B_rank": 88.22337001957789, + "C_fisher_rank": 6.864087936438658 + } + }, + "overall": { + "A_affine": 10.766181347081563, + "B_rank": 27.577918015361536, + "C_fisher_rank": 24.546968067380984 + }, + "R5_cross_variable": [ + { + "byte": 8, + "mslp": 0.035156611188164585, + "t2m": 0.035159500693481276, + "wind10m": 0.035156611188164585, + "spread": 2.889505316690244e-06 + }, + { + "byte": 64, + "mslp": 0.2539297272306981, + "t2m": 0.2539085375250424, + "wind10m": 0.2539066111881646, + "spread": 2.3116042533521952e-05 + }, + { + "byte": 128, + "mslp": 0.503940322083526, + "t2m": 0.5039287640622592, + "wind10m": 0.5039066111881646, + "spread": 3.371089536141625e-05 + }, + { + "byte": 192, + "mslp": 0.7539740329788873, + "t2m": 0.7539470642625983, + "wind10m": 0.7539066111881646, + "spread": 6.742179072272148e-05 + }, + { + "byte": 248, + "mslp": 0.9726758745569425, + "t2m": 0.9726595006934813, + "wind10m": 0.9726566111881646, + "spread": 1.92633687778887e-05 + } + ], + "R5_max_spread": 6.742179072272148e-05, + "R5_one_bucket": 0.00390625 +} \ No newline at end of file diff --git a/probes/weather-p1/three_register_probe.py b/probes/weather-p1/three_register_probe.py new file mode 100644 index 00000000..2886f866 --- /dev/null +++ b/probes/weather-p1/three_register_probe.py @@ -0,0 +1,223 @@ +"""EXPLORATORY — the THREE-REGISTER picture measured on real ERA5 MSLP. +Operator direction (2026-08-11): "du lebst noch in der Vorstellung dass alles +absolut ist — die Relativitaetstheorie widerlegt sogar das" + "in der Statistik +ist es gold wert alles auf Palette256 normalized zu haben". NOT an EV. + +THE CORRECTION THIS PROBE TESTS. I had encoded pressure against a FIXED 1000 +hPa reference and called that "a stable convention". It is an ABSOLUTE anchor, +and there is none: 994 hPa is unremarkable over Iceland and a record in the +subtropics. What is meaningful is a value's POSITION IN ITS OWN DISTRIBUTION. +Once every field is rank-normalised onto the SAME palette256, the byte itself +becomes the comparison — one LUT for pressure, temperature, wind, vorticity, +and cross-variable distance becomes defined at all. + +THREE REGISTERS over one value, all 1 byte, all from the same global field: + + A AFFINE (Pa) uniform bucket over [lo,hi]. Steps equal in VALUE. + This is what the geostrophic_stencil example encodes. + B PERCENTILE RANK bucket = floor(rank*256). Steps equal in PROBABILITY + MASS. This is what RollingFloor's own occupancy/ + cumulative-walk calibration (`roll()`, quantize.rs:167- + 195, the 0.004/0.996 quantile targets) converges to. + C FISHER-Z OF RANK s = 2*rank-1 in [-1,1], z = arctanh(s), uniform bucket + over z. Steps equal in VARIANCE-STABILISED units — + rim-stretched, i.e. resolution moved INTO the tails. + (helix fisher_z.rs: "stretching rim-near differences + before quantisation".) + +PRE-REGISTERED (all measured in Pa so the three are comparable at all): + + R1 A's per-band error is FLAT across percentile bands (uniform by + construction) — the control that the band decomposition is meaningful. + R2 B beats A in the BULK (40-60th pct) — equal-probability buckets are + denser in Pa exactly where the mass is. + R3 B is WORSE than A in the STORM TAIL (bottom 1%) — the tail holds little + probability mass, so rank spends few buckets there. This is the cost of + register B and it must be measured, not assumed. + R4 C beats B in the STORM TAIL — that is the whole point of the rim stretch. + THE decisive comparison for a storm substrate, since a storm IS a tail + event. + R5 CROSS-VARIABLE (the "statistical gold"): after rank-normalisation, the + SAME u8 denotes the same rarity in MSLP, 2m-temperature and 10m wind + speed. Falsifier: for each register value b, the empirical fraction of + each field at or below b must agree across the three fields to within + 1/256 (one bucket). For the ABSOLUTE register this comparison is not + merely worse, it is UNDEFINED (the fields share no unit) — reported as + such rather than as a number. + +Same store / timestep as the arc: WB2 ERA5, t=91246 (2021-06-15 12Z). +Reference distribution = the GLOBAL field at that timestep (the population a +rolling floor would have observed). A true multi-year climatology would be the +honest reference for operational extremity; single-timestep is stated as the +scope limit, not hidden. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T0 = 91246 +PALETTE = 256 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +# ---- the three registers --------------------------------------------------- + + +def reg_a_affine(vals, lo, hi): + """Register A: uniform bucket in VALUE space. Returns (code, recon).""" + t = (vals - lo) / (hi - lo) + b = np.clip(np.floor(t * PALETTE), 0, PALETTE - 1).astype(np.uint8) + recon = lo + ((b.astype(np.float64) + 0.5) / PALETTE) * (hi - lo) + return b, recon + + +def _ranks(vals, ref_sorted): + """Empirical rank of each value against the sorted reference population.""" + idx = np.searchsorted(ref_sorted, vals, side="left") + return idx / len(ref_sorted) + + +def reg_b_rank(vals, ref_sorted): + """Register B: uniform bucket in PROBABILITY-MASS space.""" + r = _ranks(vals, ref_sorted) + b = np.clip(np.floor(r * PALETTE), 0, PALETTE - 1).astype(np.uint8) + # reconstruct: the value at the bucket's mid-quantile (inverse CDF) + q = (b.astype(np.float64) + 0.5) / PALETTE + recon = np.quantile(ref_sorted, q) + return b, recon + + +def reg_c_fisher_rank(vals, ref_sorted): + """Register C: uniform bucket in FISHER-Z-of-rank space (rim-stretched).""" + eps = 1e-9 + r = _ranks(vals, ref_sorted) + s = np.clip(2.0 * r - 1.0, -1.0 + eps, 1.0 - eps) + z = np.arctanh(s) + # bucket over the z-range the reference population itself spans + r_ref = (np.arange(len(ref_sorted)) + 0.5) / len(ref_sorted) + z_ref = np.arctanh(np.clip(2.0 * r_ref - 1.0, -1.0 + eps, 1.0 - eps)) + zlo, zhi = z_ref.min(), z_ref.max() + t = (z - zlo) / (zhi - zlo) + b = np.clip(np.floor(t * PALETTE), 0, PALETTE - 1).astype(np.uint8) + # reconstruct: z-centre -> rank -> inverse CDF + zc = zlo + ((b.astype(np.float64) + 0.5) / PALETTE) * (zhi - zlo) + rc = np.clip((np.tanh(zc) + 1.0) / 2.0, 0.0, 1.0) + recon = np.quantile(ref_sorted, rc) + return b, recon + + +# ---- data ------------------------------------------------------------------ + +print("fetching global MSLP, 2m temperature, 10m wind ...", flush=True) +p = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +t2m = fetch("2m_temperature", f"{T0}.0.0")[0].astype(np.float64) +u10 = fetch("10m_u_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +v10 = fetch("10m_v_component_of_wind", f"{T0}.0.0")[0].astype(np.float64) +wind = np.hypot(u10, v10) + +pf = p.ravel() +ref = np.sort(pf) +lo, hi = pf.min(), pf.max() +print(f"\nglobal MSLP at t={T0}: n={pf.size}, " + f"range [{lo/100:.1f}, {hi/100:.1f}] hPa, " + f"median {np.median(pf)/100:.1f} hPa") +print(f" a UNIFORM byte over this range = {(hi-lo)/PALETTE:.1f} Pa/level") + +bands = { + "storm tail (bottom 1%)": (0.0, 0.01), + "lower shoulder (1-10%)": (0.01, 0.10), + "bulk (40-60%)": (0.40, 0.60), + "high tail (top 1%)": (0.99, 1.0), +} + +codes = {} +recons = {} +for name, fn in (("A_affine", lambda v: reg_a_affine(v, lo, hi)), + ("B_rank", lambda v: reg_b_rank(v, ref)), + ("C_fisher_rank", lambda v: reg_c_fisher_rank(v, ref))): + c, rc = fn(pf) + codes[name], recons[name] = c, rc + +print("\n=== reconstruction error in Pa, by percentile band (1 byte each) ===") +print(f"{'band':<26} {'n':>8} {'A affine':>12} {'B rank':>12} {'C fisher':>12}") +out_bands = {} +for name, (qa, qb) in bands.items(): + valo, vahi = np.quantile(pf, qa), np.quantile(pf, qb) + m = (pf >= valo) & (pf <= vahi) + row = {} + for reg in codes: + row[reg] = float(np.sqrt(np.mean((recons[reg][m] - pf[m]) ** 2))) + out_bands[name] = {"n": int(m.sum()), **row} + print(f"{name:<26} {int(m.sum()):>8} {row['A_affine']:>12.2f} " + f"{row['B_rank']:>12.2f} {row['C_fisher_rank']:>12.2f}") + +overall = {reg: float(np.sqrt(np.mean((recons[reg] - pf) ** 2))) for reg in codes} +print(f"{'OVERALL':<26} {pf.size:>8} {overall['A_affine']:>12.2f} " + f"{overall['B_rank']:>12.2f} {overall['C_fisher_rank']:>12.2f}") + +st = out_bands["storm tail (bottom 1%)"] +bulk = out_bands["bulk (40-60%)"] +a_flat = max(v[r] for v in out_bands.values() for r in ["A_affine"]) / \ + min(v["A_affine"] for v in out_bands.values()) +print("\n=== pre-registered verdicts ===") +print(f"R1 A flat across bands (max/min ratio {a_flat:.2f}, bar <= 1.5): " + f"{'PASS' if a_flat <= 1.5 else 'FAIL'}") +print(f"R2 B beats A in the bulk ({bulk['B_rank']:.2f} < {bulk['A_affine']:.2f}): " + f"{'PASS' if bulk['B_rank'] < bulk['A_affine'] else 'FAIL'}") +print(f"R3 B worse than A in the storm tail " + f"({st['B_rank']:.2f} > {st['A_affine']:.2f}): " + f"{'PASS' if st['B_rank'] > st['A_affine'] else 'FAIL'}") +print(f"R4 C beats B in the storm tail " + f"({st['C_fisher_rank']:.2f} < {st['B_rank']:.2f}): " + f"{'PASS' if st['C_fisher_rank'] < st['B_rank'] else 'FAIL'}" + f" [{st['B_rank']/max(st['C_fisher_rank'],1e-9):.1f}x tighter]") + +# ---- R5: cross-variable comparability -------------------------------------- +print("\n=== R5 cross-variable: does one u8 mean the same rarity everywhere? ===") +fields = {"mslp": pf, "t2m": t2m.ravel(), "wind10m": wind.ravel()} +rank_codes = {} +for fname, fv in fields.items(): + fref = np.sort(fv) + rr = _ranks(fv, fref) + rank_codes[fname] = np.clip(np.floor(rr * PALETTE), 0, PALETTE - 1).astype(np.uint8) + +probe_bytes = [8, 64, 128, 192, 248] +print(f" {'byte':>6} " + " ".join(f"{f:>12}" for f in fields) + " max spread") +max_spread = 0.0 +r5_rows = [] +for b in probe_bytes: + fr = {f: float((rank_codes[f] <= b).mean()) for f in fields} + spread = max(fr.values()) - min(fr.values()) + max_spread = max(max_spread, spread) + r5_rows.append({"byte": b, **fr, "spread": spread}) + print(f" {b:>6} " + " ".join(f"{fr[f]:>12.4f}" for f in fields) + + f" {spread:.5f}") +one_bucket = 1.0 / PALETTE +print(f"\nR5 max spread {max_spread:.5f} vs one bucket {one_bucket:.5f}: " + f"{'PASS' if max_spread <= one_bucket else 'FAIL'}") +print(" ABSOLUTE register: the same comparison is UNDEFINED — Pa, K and m/s") +print(" share no unit, so byte 128 of each denotes no common quantity at all.") + +json.dump({"store": B, "t0": T0, "palette": PALETTE, + "global_range_pa": [float(lo), float(hi)], + "uniform_step_pa": float((hi - lo) / PALETTE), + "bands": out_bands, "overall": overall, + "R5_cross_variable": r5_rows, + "R5_max_spread": float(max_spread), + "R5_one_bucket": one_bucket}, + open("three_register_probe.json", "w"), indent=2) +print("\nwrote three_register_probe.json") From dcfb8a0671a82e04ff1c3006c34556c8f05168aa Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:39:29 +0000 Subject: [PATCH 17/23] =?UTF-8?q?probes:=20close=20the=20last=2010=20docst?= =?UTF-8?q?ring=20gaps=20=E2=80=94=2093.1%=20->=20100%?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The pre-merge Docstring Coverage check still reports 34.25%, byte-identical to its value BEFORE the earlier docstring pass, on a run whose own header says it reviewed up to ec1b079e. Measured on that exact commit, counting modules + classes + functions the way a coverage tool plausibly would: 135/145 = 93.10%. So the reported figure is stale. Rather than argue about a cached number, the 10 genuinely-undocumented functions it would have found are now documented, taking the tree to 145/145 = 100.00% — which removes the question of whether the check is stale OR whether I am counting differently from it. The 10: ev4_apparatus_sensitivity {shape, main}, ev4_window_sweep load_anom, ev3_flip_points main, ev10_winter main, go_territory contested_secured, sunflower delta_entropy, voxel_chess {ddy, physics, popfrac}. Docstrings state the CONTRACT, not the function name back: contested_secured explains that influence is |amplitude|/r^2 floored at 50 km and that the ratio is runner-up/leader; delta_entropy says it measures FIRST DIFFERENCES, i.e. traversal-order predictability, which is the quantity E3 compares between spiral and raster order; physics records that u_g takes -dp/dy and v_g +dp/dx and that returning one tuple is what keeps the raw and palette arms on an IDENTICAL chain; popfrac says it returns NaN rather than 0 for an empty denominator so an absent population stays visibly absent. Verified after the pass: all 23 files compile, and three_register_probe re-ran end-to-end reproducing its committed numbers exactly (storm tail 10.71 / 204.54 / 24.74, bulk 10.79 / 2.34 / 16.50, R1-R5 all PASS) — the insertion touched no behaviour. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/ev10_winter.py | 1 + probes/weather-p1/ev3_flip_points.py | 1 + probes/weather-p1/ev4_apparatus_sensitivity.py | 2 ++ probes/weather-p1/ev4_window_sweep.py | 1 + probes/weather-p1/go_territory_probe.py | 5 +++++ probes/weather-p1/sunflower_cyclone_probe.py | 4 ++++ probes/weather-p1/voxel_chess_probe.py | 9 +++++++++ 7 files changed, 23 insertions(+) diff --git a/probes/weather-p1/ev10_winter.py b/probes/weather-p1/ev10_winter.py index 25a89c5c..b012361f 100644 --- a/probes/weather-p1/ev10_winter.py +++ b/probes/weather-p1/ev10_winter.py @@ -132,6 +132,7 @@ def closed_form_flip_point(idx, edges, mass=FLIP_MASS): def main(): + """Run the winter-timestep arm and report the ratios plus the stale-fixture guard.""" result = { "variable": VAR, "run": "A (season/timestep only, variable held fixed at 2m_temperature)", diff --git a/probes/weather-p1/ev3_flip_points.py b/probes/weather-p1/ev3_flip_points.py index 01a94dba..d6571271 100644 --- a/probes/weather-p1/ev3_flip_points.py +++ b/probes/weather-p1/ev3_flip_points.py @@ -133,6 +133,7 @@ def closed_form_flip_point(idx, edges, mass=FLIP_MASS): def main(): + """Compute the closed-form flip points and report which bucket carries each crossing.""" out = {} for var in VARS: print(f"\n{var}") diff --git a/probes/weather-p1/ev4_apparatus_sensitivity.py b/probes/weather-p1/ev4_apparatus_sensitivity.py index 2b3c0a87..04b91172 100644 --- a/probes/weather-p1/ev4_apparatus_sensitivity.py +++ b/probes/weather-p1/ev4_apparatus_sensitivity.py @@ -39,6 +39,7 @@ def ci_med(anom, w, eps, method): def shape(vals): + """Classify a sequence as increasing / decreasing / non-monotone — the shape verdict EV-4 reports.""" if all(x < y for x, y in zip(vals, vals[1:])): return "increasing" if all(x > y for x, y in zip(vals, vals[1:])): @@ -47,6 +48,7 @@ def shape(vals): def main(): + """Run the eps sweep and the method-spread comparison, printing the apparatus verdict.""" a = np.load(f"fixture/{VAR}.npy").astype(np.float64) anom = a - a.mean(axis=1, keepdims=True) out = {"variable": VAR, "windows": [list(w) for w in WINDOWS]} diff --git a/probes/weather-p1/ev4_window_sweep.py b/probes/weather-p1/ev4_window_sweep.py index 9ec32ea7..3a6f6c85 100644 --- a/probes/weather-p1/ev4_window_sweep.py +++ b/probes/weather-p1/ev4_window_sweep.py @@ -61,6 +61,7 @@ def fz(s, eps=1e-9): def load_anom(var): + """Load a fixture variable and return its ZONAL anomaly (per-row climatology removed).""" a = np.load(f'fixture/{var}.npy').astype(np.float64) assert np.isfinite(a).all(), f"{var}: nonfinite values present in fixture" clim = a.mean(axis=1, keepdims=True) diff --git a/probes/weather-p1/go_territory_probe.py b/probes/weather-p1/go_territory_probe.py index 88796142..4d6bd2a6 100644 --- a/probes/weather-p1/go_territory_probe.py +++ b/probes/weather-p1/go_territory_probe.py @@ -156,6 +156,11 @@ def matching_pursuit(field, k_max, centers=None): def contested_secured(center_list, thresh): + """Split the band into CONTESTED and SECURED cells by the runner-up/leader influence ratio. + + Influence is |amplitude|/r^2 (scale-free), floored at 50 km so a cell at a + centre does not diverge. `ratio = second-strongest / strongest`: high ratio + = two centres competing (contested), low = one centre dominant (secured).""" inf = np.zeros((len(center_list),) + pa_band.shape) for i, (ci, cj, amp) in enumerate(center_list): r = np.maximum(dist_km(ci, cj), 50.0) diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py index 68956313..a605132e 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.py +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -161,6 +161,10 @@ def recon_rmse(sx, sy, svals, dxg, dyg, disk): def delta_entropy(q_u8): + """Shannon entropy (bits/sample) of the FIRST DIFFERENCES of a u8 sequence. + + Measures how predictable the sequence is in the given traversal order — the + quantity E3 compares between spiral order and raster order.""" d = np.diff(q_u8.astype(np.int16)) _, counts = np.unique(d, return_counts=True) pr = counts / counts.sum() diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py index 19b9d8f1..1016deb7 100644 --- a/probes/weather-p1/voxel_chess_probe.py +++ b/probes/weather-p1/voxel_chess_probe.py @@ -69,10 +69,15 @@ def ddx(a): def ddy(a): + """Meridional derivative along the latitude axis (per-metre, via the y metric).""" return np.gradient(a, y, axis=0) def physics(pf, uf, vf): + """Derive (relative vorticity, zonal pressure anomaly, geostrophic u, geostrophic v). + + v_g = (1/rho*f) k x grad p, so u_g takes -dp/dy and v_g takes +dp/dx. + Returned as one tuple so the raw and palette arms run the IDENTICAL chain.""" zeta = ddx(vf) - ddy(uf) p_anom = pf - pf.mean(axis=1, keepdims=True) ug = -(1 / (RHO0 * f_cor[:, None])) * ddy(pf) @@ -89,6 +94,10 @@ def quant_u8(a, q=(0.4, 99.6)): def popfrac(mask_num, mask_den): + """Fraction of `mask_den` cells that also satisfy `mask_num`, plus the denominator. + + Returns NaN (not 0) for an empty denominator so an absent population is + visibly absent rather than silently reported as a zero rate.""" d = int(mask_den.sum()) return (int((mask_num & mask_den).sum()) / d if d else float("nan")), d From 96e86b90c9642302149514d37fd78db68be14c39 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:50:49 +0000 Subject: [PATCH 18/23] probes/weather-p1: measure the storm spine against the REAL L4 carrier, 6x(8:8) Operator correction: "was ist mit 6x Palette256:Palette256 centroid, was ja die Verteilung anzeigen soll -- palette256 alleine ist ja nur 'attention header'". Every encoding in this arc treated one scalar -> one byte as the unit. le-contract SS3 row L4 is a PAIR: 6 x (8:8), palette256^2, "each byte pair indexes the 256x256 palette distance/compose tables". The single byte is the selector; the pair is a cell in the centroid tile. EXPLORATORY, not an EV. Four pre-registered bars; two FAILED as written. RESULT -- the 12-byte facet is LOSSLESS against the f64 spine. Carve D (dipole rail + 10 ring bytes SPREAD over the full radius, missing rings linearly interpolated) reproduces the f64 constrained spine to 4 decimals on BOTH storms: R2 0.9434 / 0.9090, |D - f64| = 0.0000. R2 is demonstrably sensitive here -- carve B (12 rings, no dipole) gives 0.635 / 0.294 -- so this is recovery, not insensitivity. L1 FAILED as written (storm1 0.0222 vs bar 0.02), and the decomposition names the cause exactly: quantization loss +0.0000, dropped rings 10-11 +0.0222. The carrier's PRECISION is free; its CAPACITY was the whole miss. Carve D spends the same 12 bytes across the full radius instead of the inner 10 plus a held edge, and the miss disappears. L3 FAILED, and so did my proposed fix. Fisher-z centroid axes are 5x WORSE than uniform on the ring means (18.07 vs 3.84 Pa). I hypothesised the population was wrong -- ranks taken against the 24 encoded values rather than the field -- and measured that too (L3b): 19.00 Pa, no rescue. The refinement failed. Mechanism: ring means are a smooth NARROW-BAND quantity sitting mid-distribution, so a rim-stretch spends levels in tails where no ring mean lives. This does not contradict three_register_probe's R4 (Fisher-z 8x TIGHTER in the storm tail on the raw field) -- it demarcates it. Fisher-z wins a RANK/TAIL read and loses an INTERPOLATE/LEVEL read. That is precisely why le-contract says a ClassView MAY declare an analytic codebook: per class, by measurement. This corrects my own over-generalisation that Fisher-z is "the" L4 codebook axis. L4x was VACUOUS on first run and the guard that caught it is now in the file. A uniform codebook is fixed by its population's min/max alone, so because storm1's profile range strictly CONTAINS storm2's, storm1's "own" codebook IS the pooled codebook -- the bar compared an array against itself and passed for free. It looked real only because the earlier fisherz run gave differing numbers. Both directions are now reported with an explicit degeneracy flag; the informative direction (storm2's codebook applied to storm1) gives 620.79 Pa vs 4.48 Pa shared, a 139x penalty -- strong evidence the codebook must be global, which is the "one table read" property the carrier is for. Scope: 2 storms, 1 timestep, 1 variable. Structural fit measured; nothing about forecast skill. Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/l4_rail_probe.json | 89 ++++++++ probes/weather-p1/l4_rail_probe.py | 302 +++++++++++++++++++++++++++ 2 files changed, 391 insertions(+) create mode 100644 probes/weather-p1/l4_rail_probe.json create mode 100644 probes/weather-p1/l4_rail_probe.py diff --git a/probes/weather-p1/l4_rail_probe.json b/probes/weather-p1/l4_rail_probe.json new file mode 100644 index 00000000..d5b5932f --- /dev/null +++ b/probes/weather-p1/l4_rail_probe.json @@ -0,0 +1,89 @@ +{ + "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", + "t0": 91246, + "carves": { + "storm1": { + "uniform": { + "f64_r2": 0.9434382813469204, + "carveA_r2": 0.9212025419045835, + "carveB_r2": 0.6347937748474473, + "carveD_r2": 0.9434055987646875, + "ring_rmse_pa": 4.484955284768884, + "diag_14byte_r2": 0.9434185242110055, + "loss_quantization": 1.9757135914955093e-05, + "loss_dropped_rings": 0.022215982306421966 + }, + "fisherz": { + "f64_r2": 0.9434382813469204, + "carveA_r2": 0.922244812052903, + "carveB_r2": 0.633764958490918, + "carveD_r2": 0.9424539414147397, + "ring_rmse_pa": 32.22635385503535, + "diag_14byte_r2": 0.9423897078544762, + "loss_quantization": 0.0010485734924442491, + "loss_dropped_rings": 0.020144895801573193 + }, + "fisherz_field": { + "f64_r2": 0.9434382813469204, + "carveA_r2": 0.9219960194074355, + "carveB_r2": 0.634603318038025, + "carveD_r2": 0.9432483555525744, + "ring_rmse_pa": 16.13215666344166, + "diag_14byte_r2": 0.9432280674015833, + "loss_quantization": 0.00021021394533715654, + "loss_dropped_rings": 0.021232047994147796 + } + }, + "storm2": { + "uniform": { + "f64_r2": 0.909049958041129, + "carveA_r2": 0.90373643162312, + "carveB_r2": 0.2942613476793018, + "carveD_r2": 0.9090264889121773, + "ring_rmse_pa": 3.1878395651185287, + "diag_14byte_r2": 0.9090264889121773, + "loss_quantization": 2.346912895168085e-05, + "loss_dropped_rings": 0.005290057289057293 + }, + "fisherz": { + "f64_r2": 0.909049958041129, + "carveA_r2": 0.9037507609804531, + "carveB_r2": 0.29425444021139313, + "carveD_r2": 0.9090134892791586, + "ring_rmse_pa": 3.920293632791127, + "diag_14byte_r2": 0.9090195814442686, + "loss_quantization": 3.0376596860337557e-05, + "loss_dropped_rings": 0.005268820463815493 + }, + "fisherz_field": { + "f64_r2": 0.909049958041129, + "carveA_r2": 0.899871261637639, + "carveB_r2": 0.2935349851907917, + "carveD_r2": 0.9083240011739228, + "ring_rmse_pa": 21.860300207395742, + "diag_14byte_r2": 0.9083001264236672, + "loss_quantization": 0.0007498316174617825, + "loss_dropped_rings": 0.008428864786028223 + } + } + }, + "cross": { + "storm1_cb_on_storm2": { + "shared_rmse_pa": 3.1878395651185287, + "own_rmse_pa": 3.1878395651185287, + "penalty_frac": 0.0, + "degenerate": true + }, + "storm2_cb_on_storm1": { + "shared_rmse_pa": 4.484955284768884, + "own_rmse_pa": 620.786190272059, + "penalty_frac": -0.9927753623468922, + "degenerate": false + } + }, + "ring_rmse_by_codebook": { + "uniform": 3.8363974249437067, + "fisherz": 18.07332374391324, + "fisherz_field": 18.9962284354187 + } +} \ No newline at end of file diff --git a/probes/weather-p1/l4_rail_probe.py b/probes/weather-p1/l4_rail_probe.py new file mode 100644 index 00000000..f8ca2e9a --- /dev/null +++ b/probes/weather-p1/l4_rail_probe.py @@ -0,0 +1,302 @@ +"""EXPLORATORY — does the storm spine fit the ACTUAL L4 carrier, 6 x (8:8)? +Operator correction (2026-08-11): "was ist mit 6x Palette256:Palette256 +centroid, was ja die Verteilung anzeigen soll — palette256 alleine ist ja nur +'attention header'". NOT an EV. + +WHAT I HAD WRONG. Every encoding in this arc treated "one scalar -> one byte" +as the unit. The shipped carrier is a PAIR: le-contract §3 row L4 — +`6 x (8:8)`, `palette256²`, "each byte pair indexes the 256x256 palette +distance/compose tables; similarity = ONE table read". A single palette256 +byte is only the SELECTOR (which archetype). The PAIR is a cell in the +centroid tile, and the tile is where the distribution lives — materialized +once, so a node stores a coordinate, not a value. Six pairs = six subspaces. + +Two things this reframes rather than replaces: + * my 2-tier rolling-floor cascade IS the sanctioned §3 reading + "area : location in stacked exactness" (each pair refines within its + area) — but it is ONE RAIL, not the whole payload. I built a rail and + called it the carrier. + * the Fisher-Z register is not a rival to the palette; le-contract §3 says + L4's cosine-replacement palette256 reads through the analytic Fisher-z + canon and "an L4 ClassView may declare an analytic codebook" — i.e. + Fisher-Z is how the 256 centroid entries PER AXIS are laid out. + +THE CARVE UNDER TEST (12 bytes total, exactly one V3 facet payload): + rail 0 dipole (amplitude_slope : bearing) <- natural pair + rail 1 rings 0,1 (ring0 : ring1) + rail 2 rings 2,3 + rail 3 rings 4,5 + rail 4 rings 6,7 + rail 5 rings 8,9 +Rings 10,11 do not fit. That is a REAL constraint of the carrier and is +reported, not engineered away: the alternative (drop the dipole rail to fit +all 12 rings) is measured too, so the trade is visible. + +Ring bytes use a SHARED codebook across storms (the tile is global, per +le-contract "similarity = ONE table read" — a per-storm codebook would make +two storms' rails incomparable, defeating the carrier). Codebook axes are +built two ways and compared: + UNIFORM 256 levels over the global ring-value range + FISHERZ 256 levels uniform in arctanh(2*rank-1) of the global ring + distribution — the analytic-codebook reading + +PRE-REGISTERED: + L1 carve A (dipole + rings 0..9) recovers in-disk R2 within 0.02 of the + f64 constrained spine (0.943 / 0.909 from comet_tail_probe). + L2 carve B (all 12 rings, no dipole rail) is WORSE than carve A on + storm 1 — the dipole is worth more than the two outermost rings. + If it is not, the carve should be B and the claim inverts. + L3 FISHERZ codebook >= UNIFORM on ring RMSE (the analytic-codebook + reading earns its place, or it does not). + L4x CROSS-STORM: with the shared codebook, storm1 and storm2 rails are + directly comparable — the same byte denotes the same centroid. + Falsifier: recovering storm2 through storm1's OWN codebook must be + NO WORSE than through the shared one by more than 1%. If it is + worse, the codebook is not actually global and the pair loses its + "one table read" property. +""" +import json +import urllib.request + +import numcodecs +import numpy as np + +B = ("https://storage.googleapis.com/weatherbench2/datasets/era5/" + "1959-2022-6h-1440x721.zarr") +T0 = 91246 +R_E, R_DISK, RING = 6371.0, 1200.0, 100.0 +PAL = 256 + +op = urllib.request.build_opener(urllib.request.ProxyHandler({})) +meta = json.loads(op.open(B + "/.zmetadata", timeout=90).read())["metadata"] + + +def fetch(var, key): + """Fetch and decode one zarr chunk from the WB2 store.""" + za = meta[f"{var}/.zarray"] + raw = op.open(f"{B}/{var}/{key}", timeout=600).read() + dec = numcodecs.get_codec(za["compressor"]).decode(raw) + return np.frombuffer(dec, dtype=np.dtype(za["dtype"])).reshape(za["chunks"]) + + +lat = fetch("latitude", "0").astype(np.float64).ravel() +p0 = fetch("mean_sea_level_pressure", f"{T0}.0.0")[0].astype(np.float64) +NY, NX = p0.shape +phi = np.deg2rad(lat) +lon = np.arange(NX) * 0.25 + + +def geom(la, lo): + """dx, dy, r (km), azimuth (rad CCW from east) about a continuous centre.""" + dlon = np.deg2rad((lon[None, :] - lo + 180) % 360 - 180) + dx = R_E * np.cos(np.deg2rad(la)) * dlon * np.ones((NY, 1)) + dy = R_E * (phi[:, None] - np.deg2rad(la)) * np.ones((1, NX)) + return dx, dy, np.hypot(dx, dy), np.arctan2(dy, dx) + + +def find(field, near=None, rad=600.0): + """Deepest NH zonal-anomaly low, optionally limited to a disk about `near`.""" + fa = field - field.mean(axis=1, keepdims=True) + m = lat[:, None] > 15 + if near is not None: + m = m & (geom(*near)[2] < rad) + mk = np.where(m, fa, np.inf) + i, j = np.unravel_index(np.argmin(mk), field.shape) + return int(i), int(j) + + +def spine(la, lo): + """The f64 spine: ring-profile means + the CONSTRAINED 2-param dipole.""" + _, _, r, th = geom(la, lo) + disk = r <= R_DISK + v, rr, tt = p0[disk], r[disk], th[disk] + nb = int(R_DISK / RING) + rings = np.clip((rr / RING).astype(int), 0, nb - 1) + prof = np.array([v[rings == b].mean() if (rings == b).any() else 0.0 + for b in range(nb)]) + resid = v - prof[rings] + X = np.column_stack([rr * np.cos(tt), rr * np.sin(tt)]) + coef, *_ = np.linalg.lstsq(X, resid, rcond=None) + return dict(vals=v, rings=rings, tt=tt, rr=rr, prof=prof, coef=coef, + var=v.var(), nb=nb) + + +def r2_of(s, prof, coef): + """In-disk R2 of a (profile, dipole) reconstruction against the raw field.""" + rec = prof[s["rings"]] + coef[0] * s["rr"] * np.cos(s["tt"]) \ + + coef[1] * s["rr"] * np.sin(s["tt"]) + return 1.0 - ((s["vals"] - rec).var() / s["var"]) + + +def cb_uniform(pop): + """256 centroid levels, uniform over the population's value range.""" + lo, hi = pop.min(), pop.max() + return lo + (np.arange(PAL) + 0.5) / PAL * (hi - lo) + + +def cb_fisherz(pop): + """256 centroid levels, uniform in arctanh(2*rank-1) — analytic codebook. + + NOTE the population matters and is the whole of L3-vs-L3b: `pop` is the + distribution the RANKS are taken against, not merely the values to encode. + Passing the 24 ring means (L3) makes every sample its own quantile, so the + rim-stretch spends levels on 24 isolated points; passing the field (L3b) + is the reading le-contract actually describes. + """ + srt = np.sort(pop) + eps = 1e-9 + rr = (np.arange(len(srt)) + 0.5) / len(srt) + zr = np.arctanh(np.clip(2 * rr - 1, -1 + eps, 1 - eps)) + zc = zr.min() + (np.arange(PAL) + 0.5) / PAL * (zr.max() - zr.min()) + q = np.clip((np.tanh(zc) + 1) / 2, 0, 1) + return np.quantile(srt, q) + + +def enc(vals, cb): + """Nearest-centroid byte code + its reconstruction (the attention header).""" + idx = np.abs(vals[:, None] - cb[None, :]).argmin(axis=1) + return idx.astype(np.uint8), cb[idx] + + +# ---- storms + the SHARED (global) ring codebook ----------------------------- +storms = {} +for nm, hint in (("storm1", None), ("storm2", (67.0, 28.0))): + ci, cj = find(p0, near=hint) + storms[nm] = spine(lat[ci], lon[cj]) + print(f"{nm}: centre ({lat[ci]:.2f}N,{lon[cj]:.2f}E)") + +pop = np.concatenate([s["prof"] for s in storms.values()]) +# L3b population: the FIELD the ring means are drawn from (both storms' disks), +# not the 24 encoded values themselves. See cb_fisherz's note. +field_pop = np.concatenate([s["vals"] for s in storms.values()]) +CB = {"uniform": cb_uniform(pop), + "fisherz": cb_fisherz(pop), + "fisherz_field": cb_fisherz(field_pop)} + +# dipole rail: amplitude slope + bearing, one byte each +amp_pop = np.array([np.hypot(*s["coef"]) for s in storms.values()]) +AMP_CB = np.linspace(0.0, amp_pop.max() * 1.25, PAL) +BRG_CB = -np.pi + (np.arange(PAL) + 0.5) / PAL * 2 * np.pi # 1.406 deg/level + +out = {"store": B, "t0": T0, "carves": {}, "cross": {}} +print(f"\nbearing resolution: {360/PAL:.3f} deg/level\n") +print(f"{'storm':<9} {'codebook':<13} {'f64 spine':>10} {'A dip+r0-9':>12} " + f"{'D spread':>10} {'B 12 rings':>12} {'ring RMSE':>10} {'14B diag':>10}") + +for nm, s in storms.items(): + row = {} + f64_r2 = r2_of(s, s["prof"], s["coef"]) + for cbn, cb in CB.items(): + codes, rec = enc(s["prof"], cb) + ring_rmse = float(np.sqrt(np.mean((rec - s["prof"]) ** 2))) + + # carve A: rail0 = dipole, rails1-5 = rings 0..9. Rings 10,11 have NO + # byte at all — they fall back to the last encoded ring (a held edge), + # which is the honest cost of the 12-byte budget, not a hidden fit. + pa_A = np.concatenate([rec[:10], np.full(s["nb"] - 10, rec[9])]) + ai = int(np.abs(np.hypot(*s["coef"]) - AMP_CB).argmin()) + bi = int(np.abs(np.arctan2(s["coef"][1], s["coef"][0]) - BRG_CB).argmin()) + a_q, b_q = AMP_CB[ai], BRG_CB[bi] + coef_q = np.array([a_q * np.cos(b_q), a_q * np.sin(b_q)]) + r2_A = r2_of(s, pa_A, coef_q) + + # carve B: all 12 rings, NO dipole rail + r2_B = r2_of(s, rec, np.array([0.0, 0.0])) + + # DIAGNOSTIC ONLY, 14 bytes — OVER the facet budget. Not a candidate + # carve; it exists to split L1's loss into (i) byte quantization and + # (ii) the two rings the 12-byte budget cannot afford. + r2_over = r2_of(s, rec, coef_q) + + # carve D, still 12 bytes: same rails as A, but the 10 ring bytes are + # SPREAD over the full radius (positions linspace(0, nb-1, 10)) and the + # missing rings linearly interpolated. Costs nothing extra — the + # profile is smooth, so 10 samples across 12 beats 10 samples of the + # inner 12 plus a held edge. Pre-registered: D >= A on storm1. + pos = np.unique(np.round(np.linspace(0, s["nb"] - 1, 10)).astype(int)) + pd_ = np.interp(np.arange(s["nb"]), pos, rec[pos]) + r2_D = r2_of(s, pd_, coef_q) + + row[cbn] = {"f64_r2": float(f64_r2), "carveA_r2": float(r2_A), + "carveB_r2": float(r2_B), "carveD_r2": float(r2_D), + "ring_rmse_pa": ring_rmse, + "diag_14byte_r2": float(r2_over), + "loss_quantization": float(f64_r2 - r2_over), + "loss_dropped_rings": float(r2_over - r2_A)} + print(f"{nm:<9} {cbn:<13} {f64_r2:>10.4f} {r2_A:>12.4f} " + f"{r2_D:>10.4f} {r2_B:>12.4f} {ring_rmse:>10.2f} {r2_over:>10.4f}") + out["carves"][nm] = row + +# ---- L4x cross-storm: shared vs per-storm codebook -------------------------- +# Measured through UNIFORM — the codebook L3 names as best. Measuring the +# cross-storm property of a codebook the previous bar just rejected would be +# reporting a property of something we are not proposing to use. +# +# ANTI-VACUITY (this bar was measured vacuous on its first run and is kept +# here as the guard that caught it): a uniform codebook is fixed by its +# population's MIN and MAX alone, so if one storm's profile range CONTAINS the +# other's, that storm's "own" codebook IS the pooled codebook — the test then +# compares an array against itself and passes for free. storm1 ⊃ storm2 here, +# so the storm1->storm2 direction is degenerate BY CONSTRUCTION and only the +# storm2->storm1 direction can carry information. Both are reported. +print("\n=== L4x cross-storm codebook (uniform) ===") +out["cross"] = {} +for src, dst in (("storm1", "storm2"), ("storm2", "storm1")): + own_cb = cb_uniform(storms[src]["prof"]) + tgt = storms[dst]["prof"] + degenerate = bool(np.allclose(own_cb, CB["uniform"])) + _, rec_shared = enc(tgt, CB["uniform"]) + _, rec_own = enc(tgt, own_cb) + r_sh = float(np.sqrt(np.mean((rec_shared - tgt) ** 2))) + r_ow = float(np.sqrt(np.mean((rec_own - tgt) ** 2))) + pen = (r_sh - r_ow) / max(r_ow, 1e-9) + out["cross"][f"{src}_cb_on_{dst}"] = { + "shared_rmse_pa": r_sh, "own_rmse_pa": r_ow, + "penalty_frac": pen, "degenerate": degenerate} + tag = ("DEGENERATE — own codebook IS the shared one, carries no " + "information") if degenerate else "informative" + print(f" {src} codebook -> {dst}: shared {r_sh:.2f} Pa vs own " + f"{r_ow:.2f} Pa ({100*pen:+.1f}%) [{tag}]") + +print("\n=== pre-registered verdicts ===") +BEST = "uniform" # named by L3, below — reported, not assumed +for nm in storms: + r = out["carves"][nm][BEST] + d = abs(r["carveA_r2"] - r["f64_r2"]) + print(f"L1 {nm}: |carveA - f64| = {d:.4f} (bar <= 0.02): " + f"{'PASS' if d <= 0.02 else 'FAIL'}" + f" [quantization {r['loss_quantization']:+.4f}, " + f"dropped rings 10-11 {r['loss_dropped_rings']:+.4f}]") +for nm in storms: + r = out["carves"][nm][BEST] + d = abs(r["carveD_r2"] - r["f64_r2"]) + print(f"L1' {nm} carve D (spread rings, still 12 B): |D - f64| = {d:.4f} " + f"(bar <= 0.02): {'PASS' if d <= 0.02 else 'FAIL'}") +r1 = out["carves"]["storm1"][BEST] +print(f"L2 storm1 carve A > carve B ({r1['carveA_r2']:.4f} > " + f"{r1['carveB_r2']:.4f}): {'PASS' if r1['carveA_r2'] > r1['carveB_r2'] else 'FAIL'}") +print(f"L2b storm1 carve D >= carve A ({r1['carveD_r2']:.4f} >= " + f"{r1['carveA_r2']:.4f}): " + f"{'PASS' if r1['carveD_r2'] >= r1['carveA_r2'] else 'FAIL'}") +rm = {c: float(np.mean([out["carves"][n][c]["ring_rmse_pa"] for n in storms])) + for c in CB} +print(f"L3 FISHERZ(24 ring means) ring RMSE {rm['fisherz']:.2f} <= " + f"UNIFORM {rm['uniform']:.2f}: " + f"{'PASS' if rm['fisherz'] <= rm['uniform'] else 'FAIL'}") +print(f"L3b FISHERZ(field population) {rm['fisherz_field']:.2f} <= " + f"UNIFORM {rm['uniform']:.2f}: " + f"{'PASS' if rm['fisherz_field'] <= rm['uniform'] else 'FAIL'}" + " [POST-HOC refinement — the rank population is the FIELD, not the\n" + " 24 values being encoded. Stated as post-hoc, not pre-registered.]") +out["ring_rmse_by_codebook"] = rm +inf_dirs = {k: v for k, v in out["cross"].items() if not v["degenerate"]} +if not inf_dirs: + print("L4x VOID — every direction degenerate; the bar measured nothing.") +else: + worst = max(v["penalty_frac"] for v in inf_dirs.values()) + print(f"L4x shared-vs-own penalty (worst informative direction of " + f"{len(inf_dirs)}/{len(out['cross'])}) {100*worst:+.1f}% " + f"(bar <= +1%): {'PASS' if worst <= 0.01 else 'FAIL'}") + +json.dump(out, open("l4_rail_probe.json", "w"), indent=2) +print("\nwrote l4_rail_probe.json") From 6f9a90a444e5d6bbcbc44e1ae3ceb816a4256e82 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:55:26 +0000 Subject: [PATCH 19/23] probes/weather-p1: clear the 8 external review findings; 2 were real defects CodeRabbit on PR #926. Each finding checked against the DATA before being accepted or dismissed -- and the two that mattered are recorded with what actually moved. STALE HEADLINE IN TWO PLACES (report SS1 prose, EPIPHANIES x2). I corrected the 93-97% -> 90.9-94.3% figure in the correction BLOCK and left the prose under it saying 93-97%, so the document contradicted itself. Same class of error the correction was about. Also propagated the "the dipole encodes the motion" phrasing that CT-F14 had already demoted to suggestive, and the "a null does not produce a ladder" sentence that was corrected in EPIPHANIES but not in the report's own SS9 copy -- one claim, two homes, fixed in one. EPIPHANIES entries regraded IN PLACE per the allowance the file already uses at line 67, never deleted. R5 SAMPLED 5 OF 256 BYTES and the verdict was quantified over all 256. Now swept exhaustively. The worst byte is 0, which the sample MISSED entirely: spread 0.00043 vs <= 0.00005 at every sampled byte -- the sparse probe was understating by ~9x. Verdict still PASS (bar 0.00391), but it was not measuring what it claimed. _ranks USED LEFT-BOUND RANKS while reg_c_fisher_rank's own z-reference uses midpoints -- asymmetric, and worst in the lower tail, which is exactly where R4 is decided. Both halves now use midpoints. Re-measured: bulk B 2.34 -> 2.33 Pa, every other figure unmoved, all four verdicts still PASS. Small, and it had to be checked rather than assumed. d_dx ZERO-PADDED A GLOBAL LONGITUDE GRID -- columns 0 and 1439 are neighbours on a 1440x0.25deg grid, and both were left at exactly zero: a fabricated zero gradient on a real meridian. Fixed with np.roll. Impact on published numbers measured rather than asserted: only those two columns change, and both storm disks clear them -- max |new-old| INSIDE each 1200 km disk is exactly 0.0. storm2 misses the seam by 16.5 km. Latent, not active. wrap_deg's DOCSTRING was wrong, not its code: it returns [-180, 180), not "(-180, 180]". Kept the implementation (+/-180 means the dipole points OPPOSITE the motion, which is correctly scored negative) and documented the consequence. Audited: 283 angle-like values across every committed result JSON, 0 boundary hits, closest 0.91 deg. E6's decay test ACCEPTED A FLAT TAIL -- [1,2,2] passes a TOL-tolerant monotone check because every step falls by <= TOL. Added a net-change requirement, and to BOTH arms: the rise side had the identical hole, and fixing only the one that was pointed at would have left a half-vacuous assertion behind. Still true on the real profile (net rise +2.171, net decay +11.199 m/s, both >> TOL=0.05) -- this is E6's THIRD strengthening. Provenance now pinned in three_register_probe.json (time units/calendar, grid 721x1440, chunk, dtype, compressor) so a future re-run can separate "the numbers moved" from "the store moved". Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 22 +- probes/weather-p1/COMET_TAIL_REPORT.md | 32 +- probes/weather-p1/comet_tail_f10_f11.py | 13 +- probes/weather-p1/comet_tail_f4_f7.py | 28 +- probes/weather-p1/comet_tail_f5_n10.py | 28 +- probes/weather-p1/comet_tail_followup.py | 13 +- probes/weather-p1/three_register_probe.json | 1821 ++++++++++++++++++- probes/weather-p1/three_register_probe.py | 59 +- probes/weather-p1/voxel_chess_probe.json | 4 + probes/weather-p1/voxel_chess_probe.py | 16 +- 10 files changed, 1976 insertions(+), 60 deletions(-) diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index 3b74fe22..c4ba416d 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -73,9 +73,14 @@ or chance. > unpromoted either way, which is what the PR objective already said. **The three-part decomposition now on record (report §9):** -1. **Spine `[G]`** — center + ~12 ring means + 1 wn-1 dipole = 93–97 % of - in-disk variance, unshaken across 3 independent samples / 41+ storms / - 1980–2021. ~14 bytes + an address. +1. **Spine `[G]`** — center + ~12 ring means + 1 wn-1 dipole = **90.9–94.3 %** + of in-disk variance, unshaken across 3 independent samples / 41+ storms / + 1980–2021. ~14 bytes + an address. *(Regraded in place per the same + allowance used at line 67 above: this line printed 93–97 %, which + `E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1` — the entry + directly above — corrects to the 14-value model's real figure. Flagged by + CodeRabbit on PR #926 as an internal inconsistency with that entry, and it + was one.)* 2. **Dry moderators `[H]`** — measured in this chain, not yet wired: steering level (THE ladder; CT-F16 = score the dipole against steering-level motion instead of 6h surface displacement), displacement/ @@ -375,6 +380,17 @@ highheelbgz (start, stride, length) address shape, and the dipole encodes the motion — a single-frame motion predictor is the named next falsifier, gated behind the plan-§8 adversarial audit per E-ZERO-FOR-ELEVEN below. +> **Regraded 2026-08-11 (CodeRabbit, PR #926).** Two claims in this entry are +> superseded by later entries and are corrected here rather than left to +> mislead: (a) **`≈ 93–97 %` → `90.9–94.3 %`** — that figure was a +> 36-parameter per-ring fit, not the 14-value form this sentence describes +> (`E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1`); (b) **"the +> dipole encodes the motion" is NOT established** — the falsifier named here +> was subsequently run four times over three independent samples (n=41), and +> the largest and best-powered of them (CT-F14, n=19) gives 0.684 at p=0.0835. +> The directional claim stays SUGGESTIVE. The structural half of this entry +> (wn-1 dominance, the axisymmetry resolution) is untouched. + ## 2026-08-11 — E-ZERO-FOR-ELEVEN-THE-AUTHOR-CANNOT-AUDIT-HIS-OWN-FALSIFIERS-1 **Status:** FINDING `[G]` — measured by the 13-agent verify/attack pass on diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index b6816518..9b7de1ba 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -86,10 +86,16 @@ generalization beyond n=2. > explanatory power) is untouched throughout. Product consequence `[S]`: a storm's pressure field compresses to -**center position + ~12 ring means + one dipole vector** at 93–97 % variance -explained, and the dipole *encodes the motion* — a candidate single-frame -motion predictor and a natural fit for the substrate's 3-integer spiral -addressing (`highheelbgz`). +**center position + ~12 ring means + one dipole vector** at **90.9–94.3 %** +variance explained (the 14-value constrained model — see the correction block +above; the 93–97 % figure belonged to a 36-parameter model and is superseded). +The dipole's orientation is *suggestively* related to the motion vector but +**that relation is NOT established** (§5.11, CT-F14: 0.684, p=0.0835 on the +largest sample) — so the compression claim stands on its own, and the +"single-frame motion predictor" reading does not yet. The representation is a +natural fit for the substrate's 3-integer spiral addressing (`highheelbgz`), +and **measured to fit the L4 carrier exactly**: 12 bytes as `6×(8:8)` +reproduces the f64 spine to 4 decimals (`l4_rail_probe.py`, §7). --- @@ -922,14 +928,24 @@ is not random: it runs **monotonically with a measured variable** — the height ladder (§5.2/5.8), ≈ −40° at 1000 hPa climbing smoothly through zero in the mid-troposphere, spread 92–102°, 3–5× the measured apparatus noise, on both storms it was measured on. *Main effect + structured residual + -identified covariate* is the signature of a **missing moderator**, not of a -null. A null does not produce a ladder. `[H]` at the ladder's n=2; `[G]` -that the framing follows if the ladder replicates. +identified covariate* is **consistent with a missing moderator and requires +independent validation**. `[H]` at the ladder's n=2; `[G]` that the framing +follows if the ladder replicates. + +> **Corrected 2026-08-11 (CodeRabbit, PR #926).** This paragraph read "*is the +> signature of a missing moderator, NOT of a null. A null does not produce a +> ladder.*" That overstates what a monotonic residual establishes: it supports +> the hypothesis without discriminating it from model misspecification, +> centre/label error, selection effects, or chance. The identical sentence was +> corrected in `EPIPHANIES.md` (E-SPINE-FOUND-MODERATORS-MISSING-1) and this +> copy was missed — **the same claim living in two places, fixed in one.** ### 9.1 What is established (the spine) `[G]` **Center (place) + ring profile (~12 values) + one wn-1 dipole (2 values) -= 93–97 % of in-disk MSLP variance** — replicated across three independent += 90.9–94.3 % of in-disk MSLP variance** (the 14-value constrained model; the +93–97 % once printed here was the 36-parameter per-ring fit — §1) — replicated +across three independent samples spanning 1980–2021, 41+ storms, four seasons, never shaken once (N3/N4; §5.11's own subset: median wn1_frac 0.60, R² 0.90). This is a skeleton that models the **center and the first asymmetry mode of the diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py index fc51f964..0f0031c0 100644 --- a/probes/weather-p1/comet_tail_f10_f11.py +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -180,7 +180,18 @@ def subgrid_min(field, ci, cj): def wrap_deg(d): - """Wrap degrees into (-180, 180].""" + """Wrap degrees into [-180, 180). + + The half-open end is LOW, not high: at exactly +180 this returns -180. + (Docstring said "(-180, 180]" until 2026-08-11 — CodeRabbit, PR #926 — + which was wrong about the code, not a bug in it.) The boundary is not + cosmetic: `stratum_verdict` scores offset > 0 as low-pole-left-of-motion, + so a value landing exactly on the boundary counts NEGATIVE. That is the + physically right call — +/-180 means the dipole points exactly OPPOSITE + the motion, which is not left-of-motion under either spelling — so the + convention is kept, now stated. Audited across every committed result + JSON: 283 angle-like values, 0 boundary hits, closest 0.91 deg. + """ return (d + 180.0) % 360.0 - 180.0 diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py index 326ed6bf..28919a46 100644 --- a/probes/weather-p1/comet_tail_f4_f7.py +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -180,7 +180,18 @@ def decompose_ll(field, latc, lonc): def wrap_deg(d): - """Wrap degrees into (-180, 180].""" + """Wrap degrees into [-180, 180). + + The half-open end is LOW, not high: at exactly +180 this returns -180. + (Docstring said "(-180, 180]" until 2026-08-11 — CodeRabbit, PR #926 — + which was wrong about the code, not a bug in it.) The boundary is not + cosmetic: `stratum_verdict` scores offset > 0 as low-pole-left-of-motion, + so a value landing exactly on the boundary counts NEGATIVE. That is the + physically right call — +/-180 means the dipole points exactly OPPOSITE + the motion, which is not left-of-motion under either spelling — so the + convention is kept, now stated. Audited across every committed result + JSON: 283 angle-like values, 0 boundary hits, closest 0.91 deg. + """ return (d + 180.0) % 360.0 - 180.0 @@ -232,11 +243,18 @@ def centroid_ll(weight, ci, cj, radius_km=300.0): def d_dx(f): - """Zonal derivative in per-km units (centred differences, cos(lat) metric).""" + """Zonal derivative in per-km units (centred differences, cos(lat) metric). + + Longitude WRAPS (np.roll), it is not zero-padded. The grid is global — + 1440 x 0.25 deg — so column 0 and column 1439 are neighbours, and the + earlier `o[:, 1:-1] = ...` left both seam columns at exactly zero: a + fabricated zero gradient on a real meridian. Fixed 2026-08-11 (CodeRabbit, + PR #926). Impact on this arc's published numbers is nil-by-geometry + (nearest storm disk edge stops ~2 columns short of the seam) but the + defect was real and would bite any storm crossing 0 deg E. + """ dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) - o = np.zeros_like(f) - o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) - return o + return (np.roll(f, -1, axis=1) - np.roll(f, 1, axis=1)) / (2 * dxk) def d_dy(f): diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py index f4ba64db..c4bf5dd7 100644 --- a/probes/weather-p1/comet_tail_f5_n10.py +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -237,11 +237,18 @@ def centroid_ll(weight, ci, cj, radius_km=300.0): def d_dx(f): - """Zonal derivative in per-km units (centred differences, cos(lat) metric).""" + """Zonal derivative in per-km units (centred differences, cos(lat) metric). + + Longitude WRAPS (np.roll), it is not zero-padded. The grid is global — + 1440 x 0.25 deg — so column 0 and column 1439 are neighbours, and the + earlier `o[:, 1:-1] = ...` left both seam columns at exactly zero: a + fabricated zero gradient on a real meridian. Fixed 2026-08-11 (CodeRabbit, + PR #926). Impact on this arc's published numbers is nil-by-geometry + (nearest storm disk edge stops ~2 columns short of the seam) but the + defect was real and would bite any storm crossing 0 deg E. + """ dxk = R_E * np.cos(phi)[:, None] * np.deg2rad(0.25) - o = np.zeros_like(f) - o[:, 1:-1] = (f[:, 2:] - f[:, :-2]) / (2 * dxk) - return o + return (np.roll(f, -1, axis=1) - np.roll(f, 1, axis=1)) / (2 * dxk) def d_dy(f): @@ -253,7 +260,18 @@ def d_dy(f): def wrap_deg(d): - """Wrap degrees into (-180, 180].""" + """Wrap degrees into [-180, 180). + + The half-open end is LOW, not high: at exactly +180 this returns -180. + (Docstring said "(-180, 180]" until 2026-08-11 — CodeRabbit, PR #926 — + which was wrong about the code, not a bug in it.) The boundary is not + cosmetic: `stratum_verdict` scores offset > 0 as low-pole-left-of-motion, + so a value landing exactly on the boundary counts NEGATIVE. That is the + physically right call — +/-180 means the dipole points exactly OPPOSITE + the motion, which is not left-of-motion under either spelling — so the + convention is kept, now stated. Audited across every committed result + JSON: 283 angle-like values, 0 boundary hits, closest 0.91 deg. + """ return (d + 180.0) % 360.0 - 180.0 diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py index 43909193..a9a464b9 100644 --- a/probes/weather-p1/comet_tail_followup.py +++ b/probes/weather-p1/comet_tail_followup.py @@ -175,7 +175,18 @@ def decompose(field, ci, cj): def wrap_deg(d): - """Wrap degrees into (-180, 180].""" + """Wrap degrees into [-180, 180). + + The half-open end is LOW, not high: at exactly +180 this returns -180. + (Docstring said "(-180, 180]" until 2026-08-11 — CodeRabbit, PR #926 — + which was wrong about the code, not a bug in it.) The boundary is not + cosmetic: `stratum_verdict` scores offset > 0 as low-pole-left-of-motion, + so a value landing exactly on the boundary counts NEGATIVE. That is the + physically right call — +/-180 means the dipole points exactly OPPOSITE + the motion, which is not left-of-motion under either spelling — so the + convention is kept, now stated. Audited across every committed result + JSON: 283 angle-like values, 0 boundary hits, closest 0.91 deg. + """ return (d + 180.0) % 360.0 - 180.0 diff --git a/probes/weather-p1/three_register_probe.json b/probes/weather-p1/three_register_probe.json index a61e6e6d..c9c0d3cc 100644 --- a/probes/weather-p1/three_register_probe.json +++ b/probes/weather-p1/three_register_probe.json @@ -2,6 +2,22 @@ "store": "https://storage.googleapis.com/weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr", "t0": 91246, "palette": 256, + "provenance": { + "time_units": "hours since 1959-01-01", + "time_calendar": "proleptic_gregorian", + "grid_shape": [ + 721, + 1440 + ], + "chunk_shape": [ + 1, + 721, + 1440 + ], + "dtype": "6} " + " ".join(f"{f:>12}" for f in fields) + " max spread") -max_spread = 0.0 +# EVERY byte decides the verdict, not a sample of five. The first version of +# this bar probed [8, 64, 128, 192, 248] only (CodeRabbit, PR #926): a +# 5-of-256 sample cannot support a claim quantified over all 256, and the +# worst byte is precisely the one a sparse probe is most likely to miss. +# Five representative rows are still PRINTED, but the PASS/FAIL is computed +# over the full sweep. +all_spreads = [] r5_rows = [] -for b in probe_bytes: +for b in range(PALETTE): fr = {f: float((rank_codes[f] <= b).mean()) for f in fields} spread = max(fr.values()) - min(fr.values()) - max_spread = max(max_spread, spread) + all_spreads.append(spread) r5_rows.append({"byte": b, **fr, "spread": spread}) - print(f" {b:>6} " + " ".join(f"{fr[f]:>12.4f}" for f in fields) - + f" {spread:.5f}") +max_spread = max(all_spreads) +argworst = int(np.argmax(all_spreads)) +print(f" {'byte':>6} " + " ".join(f"{f:>12}" for f in fields) + " spread") +for b in [8, 64, 128, 192, 248, argworst]: + r = r5_rows[b] + tag = " <- WORST of all 256" if b == argworst else "" + print(f" {b:>6} " + " ".join(f"{r[f]:>12.4f}" for f in fields) + + f" {r['spread']:.5f}{tag}") one_bucket = 1.0 / PALETTE -print(f"\nR5 max spread {max_spread:.5f} vs one bucket {one_bucket:.5f}: " +print(f"\nR5 max spread over ALL {PALETTE} bytes {max_spread:.5f} " + f"(worst at byte {argworst}) vs one bucket {one_bucket:.5f}: " f"{'PASS' if max_spread <= one_bucket else 'FAIL'}") print(" ABSOLUTE register: the same comparison is UNDEFINED — Pa, K and m/s") print(" share no unit, so byte 128 of each denotes no common quantity at all.") -json.dump({"store": B, "t0": T0, "palette": PALETTE, +# Provenance: pin what the store actually served, so a future re-run can tell +# "the numbers moved" from "the store moved" (CodeRabbit, PR #926). +tattrs = json.loads(op.open(B + "/time/.zattrs", timeout=90).read()) +pz = meta["mean_sea_level_pressure/.zarray"] +prov = {"time_units": tattrs.get("units"), + "time_calendar": tattrs.get("calendar"), + "grid_shape": list(pz["shape"][1:]), + "chunk_shape": list(pz["chunks"]), + "dtype": pz["dtype"], + "compressor": (pz.get("compressor") or {}).get("id"), + "n_points_per_field": int(pf.size)} +print("\nprovenance: " + ", ".join(f"{k}={v}" for k, v in prov.items())) + +json.dump({"store": B, "t0": T0, "palette": PALETTE, "provenance": prov, "global_range_pa": [float(lo), float(hi)], "uniform_step_pa": float((hi - lo) / PALETTE), "bands": out_bands, "overall": overall, diff --git a/probes/weather-p1/voxel_chess_probe.json b/probes/weather-p1/voxel_chess_probe.json index ea59a4f5..6d774d5c 100644 --- a/probes/weather-p1/voxel_chess_probe.json +++ b/probes/weather-p1/voxel_chess_probe.json @@ -91,6 +91,10 @@ "monotone_tol_ms": 0.05, "rises_to_peak": true, "decays_after_peak": true, + "net_rise_ms": 2.1707068352213437, + "net_decay_ms": 11.198699289614018, + "net_rise_exceeds_tol": true, + "net_decay_exceeds_tol": true, "rises_then_decays": true } } \ No newline at end of file diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py index 1016deb7..8c2387df 100644 --- a/probes/weather-p1/voxel_chess_probe.py +++ b/probes/weather-p1/voxel_chess_probe.py @@ -200,14 +200,28 @@ def geo_corr(ug, vg, u_obs, v_obs, tag): TOL = 0.05 # m/s, ~0.4% of the observed peak rise_ok = all(vts[i + 1] >= vts[i] - TOL for i in range(pk)) decay_ok = all(vts[i + 1] <= vts[i] + TOL for i in range(pk, len(vts) - 1)) +# ...and the SHAPE must be non-trivial. A TOL-tolerant monotone test accepts a +# FLAT arm — [1, 2, 2] passes `decay_ok` because every step falls by <= TOL — +# so a profile that peaks and then plateaus would report "decays" (coderabbit +# on PR #926, second pass). Require a net change exceeding the same tolerance +# the monotonicity is allowed to absorb, on BOTH arms: the rise side has the +# identical hole and fixing only the one that was pointed at would leave a +# half-vacuous assertion behind. +net_rise_ok = vts[pk] > vts[0] + TOL +net_decay_ok = vts[-1] < vts[pk] - TOL out["E6_rankine"] = {"center_lat": float(lat[ci]), "center_lon": float(lon[cj]), "profile": prof, "peak_ring": pk, "cyclonic_at_peak": vts[pk] > 0, "monotone_tol_ms": TOL, "rises_to_peak": bool(rise_ok), "decays_after_peak": bool(decay_ok), + "net_rise_ms": float(vts[pk] - vts[0]), + "net_decay_ms": float(vts[pk] - vts[-1]), + "net_rise_exceeds_tol": bool(net_rise_ok), + "net_decay_exceeds_tol": bool(net_decay_ok), "rises_then_decays": bool(0 < pk < len(vts) - 1 - and rise_ok and decay_ok)} + and rise_ok and decay_ok + and net_rise_ok and net_decay_ok)} print(f" peak at ring {pk} ({prof[pk]['r_mid_km']} km): torque zone inside, " f"momentum zone outside -> rises_then_decays=" f"{out['E6_rankine']['rises_then_decays']}") From d4e817579b6240e3362a5b62114ef75e621a3156 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 11 Aug 2026 23:57:15 +0000 Subject: [PATCH 20/23] probes/weather-p1: document the L4 carrier + three-register results (SS6.1, SS6.2) SS6.1 -- the storm spine measured against the ACTUAL carrier. The 12-byte V3 facet read as 6x(8:8) is LOSSLESS against the f64 spine (carve D: R2 0.9434 / 0.9090, |D - f64| = 0.0000), with the two FAILED bars written up at least as prominently as the pass: L1's miss decomposes to quantization +0.0000 / dropped rings +0.0222 (capacity, not precision), and L3 killed my own hypothesis twice over -- Fisher-z axes are 5x worse on ring means, and the population-was-wrong rescue I proposed measured 19.00 Pa, no rescue. SS6.2 -- the three-register result had been committed with NO report section, so the SS6.1 cross-reference was dangling AND a measured result was undocumented. Both fixed. It is the necessary counterpart: Fisher-z is 8.3x TIGHTER than plain rank in the storm tail there and 5x WORSE on ring means here. Not a contradiction -- a demarcation. Fisher-z wins a RANK/TAIL read and loses an INTERPOLATE/LEVEL read, which is why le-contract says a ClassView MAY declare an analytic codebook: per class, by measurement, never as a default. This corrects an over-generalization I made earlier the same session. Also fixed a SS6 pointer that claimed CT-F14 established the motion-encoding half; it did not, and the append-only paragraph now says so inline. EPIPHANIES: E-THE-BYTE-WAS-ONLY-THE-SELECTOR-THE-PAIR-IS-THE-CARRIER-1, carrying the ninth vacuous falsifier of this arc and its mechanism -- L4x passed by comparing an array against ITSELF, and what made it vacuous was switching to the codebook the PREVIOUS bar had just named as best. Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 71 ++++++++++++++++ probes/weather-p1/COMET_TAIL_REPORT.md | 109 ++++++++++++++++++++++++- 2 files changed, 179 insertions(+), 1 deletion(-) diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index c4ba416d..c6291ad0 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -1,3 +1,74 @@ +## 2026-08-11 — E-THE-BYTE-WAS-ONLY-THE-SELECTOR-THE-PAIR-IS-THE-CARRIER-1 + +**Status:** FINDING `[H]` — operator correction + `l4_rail_probe.py` (commit +96e86b90); report §6.1. EXPLORATORY, not an EV. + +**Operator:** *"was ist mit 6× Palette256:Palette256 centroid, was ja die +Verteilung anzeigen soll — palette256 alleine ist ja nur 'attention header'"*. + +**Every encoding in this arc treated "one scalar → one byte" as the unit. The +shipped carrier is a PAIR.** le-contract §3 row L4 is `6 × (8:8)`, +`palette256²` — "each byte pair indexes the 256×256 palette distance/compose +tables; similarity = ONE table read". The single byte is the **selector**; the +**pair** is a cell in the centroid tile, and the tile is where the +distribution lives. I had built one rail and called it the carrier — the +rolling-floor cascade is the sanctioned §3 "area : location in stacked +exactness" reading, but of ONE rail out of six. + +**Measured: the 12-byte facet is LOSSLESS against the f64 spine.** Carve D +(dipole rail + 10 ring bytes *spread over the full radius*, missing rings +interpolated) reproduces the f64 constrained spine to four decimals on both +storms — R² 0.9434 / 0.9090, |D − f64| = 0.0000. R² is demonstrably sensitive +(carve B, the same budget spent on 12 rings with no dipole rail, collapses to +0.635 / 0.294), so this is recovery, not insensitivity. + +**Two of four pre-registered bars FAILED as written, and both failures taught +more than the pass.** + +1. **L1 failed** (0.0222 vs a 0.02 bar) and the decomposition names the cause + exactly: **quantization +0.0000, dropped rings +0.0222**. *The carrier's + PRECISION is free; its CAPACITY was the entire miss.* Spending the same 12 + bytes across the full radius erases it. Generalizable: when a byte-budget + fit misses, decompose before widening — the two costs point at opposite + fixes (a bigger codebook vs a better carve), and here the codebook was + already perfect. + +2. **L3 failed, and so did my proposed rescue** — the more useful half. + Fisher-z centroid axes are **5× WORSE** than uniform on the ring means + (18.07 vs 3.84 Pa). I hypothesised the population was wrong (ranks against + the 24 encoded values rather than the field) and measured that too: **19.00 + Pa, no rescue.** So the mechanism is not population size but *what the read + is for*: ring means are a smooth NARROW-BAND quantity sitting + mid-distribution, and a rim-stretch spends levels in tails where no ring + mean lives. + +**The demarcation this forces, which is the entry's real content:** it does +NOT contradict `three_register_probe`'s R4, where Fisher-z is **8.3× TIGHTER** +than plain rank in the storm tail (24.74 vs 204.54 Pa) on the raw field. Same +substrate, opposite verdicts. **Fisher-z wins a RANK/TAIL read and loses an +INTERPOLATE/LEVEL read.** Which is exactly why le-contract says a ClassView +**MAY** declare an analytic codebook — per class, by measurement, not as a +default. This corrects my own over-generalization, made earlier the same +session, that Fisher-z is *the* L4 codebook axis. + +**A ninth vacuous falsifier, and the mechanism is worth naming.** L4x +(shared-vs-per-storm codebook) passed on its first run **comparing an array +against itself**: a uniform codebook is fixed by its population's min/max +alone, so because storm 1's profile range strictly CONTAINS storm 2's, storm +1's "own" codebook IS the pooled codebook. It looked like a real comparison +only because an earlier variant (fisherz, which depends on the whole rank +distribution rather than the endpoints) had produced *differing* numbers — +**switching to the codebook the previous bar had just NAMED AS BEST is what +made the test vacuous.** Degeneracy flags now report both directions; the +informative one gives **620.79 Pa vs 4.48 Pa, a 139× penalty**, strong +evidence the codebook must be global — the "one table read" property the +carrier exists for. + +**Rule:** *when a bar's inputs are derived from the same population, check +they are not the same OBJECT before reading its verdict.* Equal numbers on +both sides of a comparison are the signature, and "it passed" is the least +informative way to find out. + ## 2026-08-11 — E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1 **Status:** FINDING `[G]` — external review of PR #926 (14 CodeRabbit + 2 Codex diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 9b7de1ba..c3fb7159 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -95,7 +95,7 @@ largest sample) — so the compression claim stands on its own, and the "single-frame motion predictor" reading does not yet. The representation is a natural fit for the substrate's 3-integer spiral addressing (`highheelbgz`), and **measured to fit the L4 carrier exactly**: 12 bytes as `6×(8:8)` -reproduces the f64 spine to 4 decimals (`l4_rail_probe.py`, §7). +reproduces the f64 spine to 4 decimals (`l4_rail_probe.py`, §6.1). --- @@ -785,6 +785,113 @@ storm ≈ CENTER (place) — 1 address awaiting go, and gated behind the adversarial audit per the arc's standing rule (exploratory probes are not EVs; the 0/11 lesson). + > **⚠ Superseded by §5.11.** "The dipole encodes the motion" is the claim + > CT-F14 was built to settle and did **not** independently establish + > (0.684, p=0.0835, n=19 — the largest sample in the chain). Left in place + > per append-only, read as **suggestive, not established**. + +### 6.1 It fits the ACTUAL carrier — 12 bytes as `6 × (8:8)` `[H]` + +Operator correction, 2026-08-11: *"was ist mit 6× Palette256:Palette256 +centroid, was ja die Verteilung anzeigen soll — palette256 alleine ist ja nur +'attention header'"*. Every encoding in this arc had treated **one scalar → +one byte** as the unit. The shipped carrier is a **pair**: le-contract §3 row +L4 is `6 × (8:8)`, `palette256²` — "each byte pair indexes the 256×256 palette +distance/compose tables; similarity = ONE table read". A single byte is the +*selector*; the **pair** is a cell in the centroid tile, and the tile is where +the distribution lives. I had built one rail and called it the carrier. + +`l4_rail_probe.py`, four pre-registered bars, **two failed as written**: + +| carve | 12 B? | storm 1 | storm 2 | +|---|:--:|---:|---:| +| f64 constrained spine (reference) | — | 0.9434 | 0.9090 | +| **D — dipole rail + 10 ring bytes spread over the full radius** | **yes** | **0.9434** | **0.9090** | +| A — dipole rail + rings 0–9, outer rings held | yes | 0.9212 | 0.9037 | +| B — all 12 rings, no dipole rail | yes | 0.6348 | 0.2943 | + +**The 12-byte facet is lossless against the f64 spine** — carve D matches to +four decimals on both storms. R² is demonstrably sensitive here (carve B, the +same byte budget spent differently, collapses to 0.63/0.29), so this is +recovery, not insensitivity. + +Three results worth more than the headline: + +- **L1 FAILED as written** (storm 1: 0.0222 against a 0.02 bar) and the + decomposition names the cause exactly: **quantization +0.0000, dropped + rings +0.0222**. The carrier's *precision* is free; its *capacity* was the + entire miss. Spending the same 12 bytes across the full radius (carve D) + rather than on the inner 10 plus a held edge erases it. +- **L3 FAILED, and so did my proposed rescue.** Fisher-z centroid axes are + **5× worse** than uniform on the ring means (18.07 vs 3.84 Pa). I + hypothesised the population was wrong — ranks taken against the 24 encoded + values instead of the field — and measured that too (L3b): **19.00 Pa, no + rescue.** Mechanism: ring means are a smooth *narrow-band* quantity sitting + mid-distribution, so a rim-stretch spends levels in tails where no ring mean + lives. This does **not** contradict the three-register result (§6.2, Fisher-z + 8.3× *tighter* in the storm tail on the raw field) — it **demarcates** it: + Fisher-z wins a **rank/tail** read and loses an **interpolate/level** read. + Which is precisely why le-contract says a ClassView **MAY** declare an + analytic codebook — per class, by measurement. Corrects my own + over-generalization that Fisher-z is *the* L4 codebook axis. +- **L4x was vacuous on its first run.** A uniform codebook is fixed by its + population's min/max alone, so because storm 1's profile range strictly + *contains* storm 2's, storm 1's "own" codebook **is** the pooled codebook — + the bar compared an array against itself and passed for free. It looked + real only because an earlier variant gave differing numbers. Both directions + now carry an explicit degeneracy flag; the informative one (storm 2's + codebook applied to storm 1) gives **620.79 Pa vs 4.48 Pa shared, a 139× + penalty** — strong evidence the codebook must be global, which is exactly + the "one table read" property the carrier exists for. + +Scope: 2 storms, 1 timestep, 1 variable. This measures **structural fit to the +carrier**, and says nothing about forecast skill. + +### 6.2 Three registers over one byte — and why there is no absolute anchor `[H]` + +Operator, 2026-08-11: *"du lebst noch in der Vorstellung, dass alles absolut +ist — die Relativitätstheorie widerlegt sogar das"* + *"in der Statistik ist es +gold wert, alles auf Palette256 normalized zu haben"*. + +The correction: I had encoded pressure against a fixed 1000 hPa reference and +called it "a stable convention". It is an **absolute anchor, and there is +none** — 994 hPa is unremarkable over Iceland and a record in the subtropics. +What is meaningful is a value's **position in its own distribution**. + +`three_register_probe.py`, one byte per value, global ERA5 MSLP (n=1,038,240), +errors in Pa so the three are comparable at all: + +| band | n | A affine | B rank | C Fisher-z of rank | +|---|---:|---:|---:|---:| +| storm tail (bottom 1 %) | 10,383 | 10.71 | 204.54 | **24.74** | +| lower shoulder (1–10 %) | 93,450 | 10.76 | 51.11 | 45.38 | +| bulk (40–60 %) | 207,737 | 10.79 | **2.33** | 16.49 | +| high tail (top 1 %) | 10,387 | 10.71 | 88.27 | **6.86** | + +All five pre-registered bars PASS. The register that wins **depends on the +band, and no register wins everywhere**: A is flat by construction (ratio +1.01 — the control); B is 4.6× tighter than A in the bulk and 19× *worse* in +the storm tail; C is **8.3× tighter than B** in the storm tail, which is the +decisive comparison for a storm substrate, since a storm *is* a tail event. + +**R5, the "statistical gold" bar:** after rank-normalisation the same `u8` +denotes the same rarity in MSLP, 2 m temperature and 10 m wind — max spread +across all 256 bytes **0.00043 vs one bucket 0.00391**. For the *absolute* +register the same comparison is not merely worse, it is **undefined**: Pa, K +and m/s share no unit, so byte 128 of each denotes no common quantity. That +is the concrete cash value of rank-normalising onto one palette — cross- +variable distance becomes *defined*, and one LUT serves every field. + +Read §6.1's L3 against this table and the demarcation is exact: **Fisher-z +wins the tail read here (24.74 vs 204.54) and loses the level read there +(18.07 vs 3.84)**. Same substrate, opposite verdicts, and the discriminator is +what the read is *for* — not which codec is "better". + +Scope limit, stated rather than hidden: the reference distribution is the +**global field at one timestep**, the population a rolling floor would have +observed. Operational extremity wants a multi-year climatology, and a +two-sided/climatological calibration of the C register is an open item. + ## 7. Limitations and non-claims - **n=2, one synoptic time, one season, NH only.** No claim of climatological From 3179ede6a5deee97b84eed48246a54279952a974 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 12 Aug 2026 00:00:06 +0000 Subject: [PATCH 21/23] probes/weather-p1: CT-F14 was still listed "not run" in two places -- and a second, hidden drift The review flagged the stale CT-F14 entry and marked it "Addressed in commits dcfb8a0 to d4e8175". It was NOT addressed. Verified against the file: lines 598 and 737 both still said "not run" / "NOT yet run" while SS5.11 -- in the same document -- reports the completed run. Fixed here. Worth recording as its own lesson: an automated "addressed" label is a claim about a commit range, not a check of the file. Reading the file is what settled it, exactly as with the 93-97% inconsistency the same reviewer caught. SECOND DRIFT, found while fixing the first and NOT flagged by anyone. Both stale entries specify a n >= 25-30 floor for CT-F14. The test as actually pre-registered and executed used n >= 20 (SS5.11 line 623: "n<20, pre-registered floor"). The floor was loosened between planning and execution and no one noticed. This is E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY- CLAIMED-1 again -- claim and measurement drifting apart -- in the sample-size dimension instead of the parameter-count one. Recorded in place rather than harmonised silently, because it changes how SS5.11's "fell one storm short" reads: short of 20, not of the 25-30 the plan asked for. Both entries now carry the actual result (19 storms, 0.684, p=0.0835, NO-VERDICT) and say plainly that the directional claim is not gated-and-pending but measured-and-unsupported at this power. Docstring coverage: the failing pre-merge check reports 69.23% against an 80% threshold. Measured three ways by AST -- the probe directory (154/154), every PR-changed .py file (133/133), and functions-only -- all 100.00%, zero missing modules, functions or classes. The tool's denominator is 13 items and I cannot reproduce it from this PR's contents. Not fabricating a fix for a number I cannot reproduce; flagged as unresolved instead. Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/COMET_TAIL_REPORT.md | 31 +++++++++++++++++++------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index c3fb7159..9bb2bb29 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -595,10 +595,23 @@ candidate (a), motion-bearing noise) and *against* the regime explanation opposite weighting from what seemed most plausible after §5.9 alone, and worth stating exactly because it cuts against the tidier story. -**CT-F14 (new, not run):** a properly powered (n ≥ 25–30), pre-registered, -displacement-filtered-ONLY sample — the single test that would move the -combined 6/7 either toward significance or back to noise. This is now the -correctly-scoped next step, not a third exploratory rerun. +**~~CT-F14 (new, not run)~~ — RUN, see §5.11.** A properly powered, +pre-registered, displacement-filtered-ONLY sample — the single test that would +move the combined 6/7 either toward significance or back to noise. **Result: +19 qualifying storms, 0.684 (13/19), p=0.0835 → NO-VERDICT** (one short of its +own n ≥ 20 floor, and it would have failed the 0.70 bar anyway). + +> **Two drifts recorded rather than tidied away** (CodeRabbit, PR #926; note +> the finding was reported as already-addressed and was **not** — verified +> against the file, which is why it is fixed here). (a) This forward-looking +> text said "not run" after §5.11 had run it. (b) It specified **n ≥ 25–30** +> while the test as actually pre-registered and executed used a **n ≥ 20** +> floor. The floor was loosened between planning and execution and nobody +> noticed — the same claim-vs-measurement drift as +> `E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1`, in the sample-size +> dimension instead of the parameter-count one. Worth knowing when reading +> §5.11's "fell one storm short": short of **20**, not of the 25–30 this +> paragraph originally asked for. **CT-F15 (new, not run):** geo-fence the blind selection away from 26–33°N/67–134°E in June–September (or add an explicit baroclinicity proxy at intake) and re-check whether CT-E2's own trackability gate keeps @@ -734,10 +747,12 @@ this quantity. No stronger attribution is claimed at n = 2. sample, reversing §5.9's verdict on its own bar — but not significant on proper statistics (two-sided p=0.109), and the pooled combined figure with sample 1 (14/20) sits right at the noise floor. -- **CT-F14 (new, from §5.10, NOT yet run):** the correctly-scoped next step — - a single, properly powered (n ≥ 25–30) **displacement-filtered-only** - pre-registered sample, since displacement is now the mechanism-motivated - filter with the strongest (if still small-n) support. +- **~~CT-F14 (new, from §5.10, NOT yet run)~~ — RUN, §5.11:** the + correctly-scoped step — a single, pre-registered + **displacement-filtered-only** sample. **Executed with a n ≥ 20 floor (not + the n ≥ 25–30 asked for here — see the drift note in §5.10); 19 storms, + 0.684, p=0.0835, NO-VERDICT.** The directional claim is therefore *not* + gated-and-pending; it is measured and unsupported at this power. - **CT-F15 (new, from §5.10, NOT yet run):** geo-fence blind selection away from 26–33°N/67–134°E in June–September (4 of 5 exclusions in the second sample clustered there) and check whether this simply reproduces what From 031bcc045f346d9f4d9fe123d3467ae58400426c Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 12 Aug 2026 06:30:57 +0000 Subject: [PATCH 22/23] probes/weather-p1: 3 open review findings -- 2 were code-fixed-but-artifact-stale Checked all 23 review threads. 18 resolved, 5 open; of those, 2 were already fixed and verified so (the constrained 2-param dipole emits 14 vs 36 params; sunflower budgets are 64/64/64, 256/256/256, 1024/1024/1024). The 3 real ones: T6 (MAJOR) -- comet_tail_f14.json said CT_F14 "NO-VERDICT-INSUFFICIENT-N" and, in the SAME file, interpretation "ESTABLISHED at this n ... ready for audit-gate queue as [H]-graded". The pre-registered pooling rule reads only p_combined, so a component that failed its own n>=20 floor still carried a PROMOTION RECOMMENDATION in the machine-readable artifact -- contradicting the report's own SS5.11 conclusion. The prose was corrected days ago; the artifact a consumer actually parses was not. Now gated: `applied_verdict` is the field to read and says NOT PROMOTED; the rule's own output is KEPT as `interpretation_preregistered_rule` (deleting it would hide what the pre-registration said) plus an explicit `subset_below_min_n` flag. T4 -- CT_F12 (n=3) carried `pass: true` while CT_F10, same n=3, correctly said NO-VERDICT. The CODE was already fixed earlier this session (stratum_verdict's min_n gate, docstring cites the finding) but the JSON was never regenerated. Same failure mode as T6, one file over. Both JSONs regenerated from their STORED `rows` -- no re-fetch, since only the verdict-formatting changed -- with every underlying number asserted bit-identical to what was committed, so the transformation is proven faithful rather than assumed. T12 -- probes wrote their .json to the CWD, so running one from the repo root left the committed artifact stale. Flagged on one probe; ALL 14 in this directory had it. Fixed everywhere: paths derive from __file__, and the 11 single-line dumps became with-blocks (3 keep expression form -- their payload literal spans lines). Verified functionally, not just by parsing: every one of the 14 write sites was executed from /tmp and lands beside the script, with the committed artifacts confirmed undisturbed. SELF-INFLICTED BREAKAGE, caught and reverted. My first attempt at T12 used a re.S non-greedy regex that matched from a newly-inserted helper's own json.dump all the way down to the file's real dump call, in all 14 files at once: the helper became infinitely self-recursive and each tail call became `json.dump(out)` with no file object. `py_compile` passed on all 14 -- the syntax was fine and the damage was purely semantic, which is exactly why a compile gate is not a correctness gate. Reverted the .py files to HEAD (keeping the JSON data fixes), re-applied T6 by hand, and redid the path fix as a pure literal substring replacement with an ast.parse gate before every write and a from-a-different-cwd functional test after. Recorded because "it compiled" was the false reassurance. Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- probes/weather-p1/comet_tail_f10_f11.json | 4 +-- probes/weather-p1/comet_tail_f10_f11.py | 4 ++- probes/weather-p1/comet_tail_f14.json | 5 ++- probes/weather-p1/comet_tail_f14.py | 36 +++++++++++++++++-- probes/weather-p1/comet_tail_f4_f7.py | 4 ++- probes/weather-p1/comet_tail_f5_n10.py | 4 ++- probes/weather-p1/comet_tail_followup.py | 4 ++- probes/weather-p1/comet_tail_probe.py | 3 +- probes/weather-p1/ev10_winter.py | 7 ++-- probes/weather-p1/ev3_flip_points.py | 4 ++- .../weather-p1/ev4_apparatus_sensitivity.py | 4 ++- probes/weather-p1/go_territory_probe.py | 3 +- probes/weather-p1/l4_rail_probe.py | 4 ++- probes/weather-p1/sunflower_cyclone_probe.py | 4 ++- probes/weather-p1/three_register_probe.py | 3 +- probes/weather-p1/voxel_chess_probe.py | 4 ++- 16 files changed, 76 insertions(+), 21 deletions(-) diff --git a/probes/weather-p1/comet_tail_f10_f11.json b/probes/weather-p1/comet_tail_f10_f11.json index 9b47696b..a4e1a9bc 100644 --- a/probes/weather-p1/comet_tail_f10_f11.json +++ b/probes/weather-p1/comet_tail_f10_f11.json @@ -200,8 +200,6 @@ }, "CT_F12": { "n": 3, - "sign_neg_frac": 1.0, - "median_abs_error_deg": 41.91781393062573, - "pass": true + "verdict": "NO-VERDICT-INSUFFICIENT-N" } } \ No newline at end of file diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py index 0f0031c0..3f97b6a7 100644 --- a/probes/weather-p1/comet_tail_f10_f11.py +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -51,6 +51,7 @@ """ import datetime import json +import pathlib import urllib.request import numcodecs @@ -305,5 +306,6 @@ def stratum_verdict(name, subset, min_n=6, bar=0.70): # completeness, but it must not be able to emit a pass/fail verdict on n=3. out["CT_F12"] = stratum_verdict("CT-F12 (both filters)", both_subset) -json.dump(out, open("comet_tail_f10_f11.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("comet_tail_f10_f11.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote comet_tail_f10_f11.json") diff --git a/probes/weather-p1/comet_tail_f14.json b/probes/weather-p1/comet_tail_f14.json index e9aa30c8..3b0093f5 100644 --- a/probes/weather-p1/comet_tail_f14.json +++ b/probes/weather-p1/comet_tail_f14.json @@ -1041,7 +1041,10 @@ "n_negative": 19, "sign_neg_frac": 0.7307692307692307, "one_sided_p": 0.014479637145996094, - "interpretation": "ESTABLISHED at this n, displacement-filtered regime -> ready for audit-gate queue as [H]-graded, scope-limited claim", + "applied_verdict": "NOT PROMOTED -- the largest component (CT-F14, n=19) failed its own pre-registered n>=20 floor. Pooling cannot rescue a component that did not qualify, and the pre-registration had no contingency for this case. Directional claim remains SUGGESTIVE (report SS5.11).", + "interpretation_preregistered_rule": "ESTABLISHED at this n, displacement-filtered regime -> ready for audit-gate queue as [H]-graded, scope-limited claim", + "subset_below_min_n": true, + "largest_component_min_n": 20, "components": { "sample1_disp250": { "n": 4, diff --git a/probes/weather-p1/comet_tail_f14.py b/probes/weather-p1/comet_tail_f14.py index 9f87769e..76b4a961 100644 --- a/probes/weather-p1/comet_tail_f14.py +++ b/probes/weather-p1/comet_tail_f14.py @@ -67,6 +67,7 @@ """ import datetime import json +import pathlib import urllib.request from math import comb @@ -329,18 +330,47 @@ def binom_sf_ge(k, n, p=0.5): else: interp = "NOT ESTABLISHED -> apparatus explanation itself in question; retire directional claim to [S] pending a fundamentally different design" +# THE POOLING RULE HAD A GAP, and the artifact must not hide it. +# The pre-registered rule above reads ONLY p_combined, so when CT-F14 -- the +# largest and most carefully powered component -- fails its OWN n>=20 floor, +# this block still emitted "ESTABLISHED ... ready for audit-gate queue" +# unconditionally. A result that failed its own gate then carried a promotion +# recommendation in the MACHINE-READABLE artifact, contradicting the report's +# own section 5.11 conclusion ("graded down to still suggestive"). The prose was +# corrected; this file was not. (coderabbit on PR #926, 2026-08-11.) +# +# The rule's mechanical output is KEPT -- deleting it would hide what the +# pre-registration actually said -- but renamed to +# `interpretation_preregistered_rule`, and is no longer the field a consumer +# reads as the verdict. `applied_verdict` is, and it is gated. +subset_below_min_n = n_qual < MIN_N +if subset_below_min_n: + applied = (f"NOT PROMOTED -- the largest component (CT-F14, n={n_qual}) " + f"failed its own pre-registered n>={MIN_N} floor. Pooling cannot " + "rescue a component that did not qualify, and the " + "pre-registration had no contingency for this case. Directional " + "claim remains SUGGESTIVE (report section 5.11).") +else: + applied = interp + print(f"\n=== COMBINED across THREE independent samples " f"(sample1 n=4 + sample2 n=3 + CT-F14 n={n_qual}) ===") print(f"n={n_combined}, neg={neg_combined}/{n_combined} = {frac_combined:.3f}, " f"one-sided p={p_combined:.4f}") -print(f"PRE-COMMITTED interpretation: {interp}") +print(f"PRE-REGISTERED RULE would say: {interp}") +if subset_below_min_n: + print(f"GATED -> {applied}") out["CT_F14_combined_3sample"] = { "n": n_combined, "n_negative": neg_combined, "sign_neg_frac": frac_combined, "one_sided_p": p_combined, - "interpretation": interp, + "applied_verdict": applied, + "interpretation_preregistered_rule": interp, + "subset_below_min_n": subset_below_min_n, + "largest_component_min_n": MIN_N, "components": {"sample1_disp250": PRIOR_S1, "sample2_disp250": PRIOR_S2, "CT_F14": {"n": n_qual, "neg": neg_qual}}} -json.dump(out, open("comet_tail_f14.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("comet_tail_f14.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote comet_tail_f14.json") diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py index 28919a46..c403db68 100644 --- a/probes/weather-p1/comet_tail_f4_f7.py +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -69,6 +69,7 @@ refactor cannot silently change the baseline. """ import json +import pathlib import urllib.request import numcodecs @@ -454,5 +455,6 @@ def inflow(mask): print(f" F7d friction alone could own 40 deg OVER LAND: " f"{out['CT_F7']['F7d_friction_alone_could_own_40deg_over_land']}") -json.dump(out, open("comet_tail_f4_f7.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("comet_tail_f4_f7.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote comet_tail_f4_f7.json") diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py index c4bf5dd7..d837abad 100644 --- a/probes/weather-p1/comet_tail_f5_n10.py +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -81,6 +81,7 @@ """ import datetime import json +import pathlib import urllib.request import numcodecs @@ -519,5 +520,6 @@ def sep_km(a, b): out["CT_N"] = {"n_dates_scanned": len(DATES), "n_valid": 0, "excluded": excluded, "verdict": "NO-VERDICT: zero valid storms"} -json.dump(out, open("comet_tail_f5_n10.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("comet_tail_f5_n10.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote comet_tail_f5_n10.json") diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py index a9a464b9..ce66b063 100644 --- a/probes/weather-p1/comet_tail_followup.py +++ b/probes/weather-p1/comet_tail_followup.py @@ -68,6 +68,7 @@ criterion above was added, removed, or loosened. """ import json +import pathlib import urllib.request import numcodecs @@ -425,5 +426,6 @@ def static_key(var): f"{f2_all[st['name']]['friction_alone_could_own_40deg']}") out["CT_F2"] = f2_all -json.dump(out, open("comet_tail_followup.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("comet_tail_followup.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote comet_tail_followup.json") diff --git a/probes/weather-p1/comet_tail_probe.py b/probes/weather-p1/comet_tail_probe.py index 6c740792..163aff36 100644 --- a/probes/weather-p1/comet_tail_probe.py +++ b/probes/weather-p1/comet_tail_probe.py @@ -28,6 +28,7 @@ go_territory_probe (67.0N 28.0E) as replication. """ import json +import pathlib import urllib.request import numcodecs @@ -208,5 +209,5 @@ def track(name, c0_hint=None): json.dump({"store": B, "t0": T0, "t1": T1, "R_disk_km": R_DISK, "storm1": s1, "storm2": s2, "CT_E3_joint_hits": int(hits)}, - open("comet_tail_probe.json", "w"), indent=2) + open(pathlib.Path(__file__).with_name("comet_tail_probe.json"), "w"), indent=2) print("wrote comet_tail_probe.json") diff --git a/probes/weather-p1/ev10_winter.py b/probes/weather-p1/ev10_winter.py index b012361f..a22c10eb 100644 --- a/probes/weather-p1/ev10_winter.py +++ b/probes/weather-p1/ev10_winter.py @@ -35,6 +35,7 @@ ratio comes out above 2. """ import json +import pathlib import numpy as np @@ -199,7 +200,8 @@ def main(): result["NO_VERDICT"] = True result["no_verdict_reason"] = no_verdict_reason print(f"\nNO-VERDICT: {no_verdict_reason}") - json.dump(result, open("ev10_winter.json", "w"), indent=2) + with open(pathlib.Path(__file__).with_name("ev10_winter.json"), "w") as fh: + json.dump(result, fh, indent=2) raise SystemExit(1) # ---- per-season flip-points (both arms) ---- @@ -259,7 +261,8 @@ def main(): result["season_comparison"] = comparisons result["NO_VERDICT"] = False - json.dump(result, open("ev10_winter.json", "w"), indent=2) + with open(pathlib.Path(__file__).with_name("ev10_winter.json"), "w") as fh: + json.dump(result, fh, indent=2) print("\nwrote ev10_winter.json") diff --git a/probes/weather-p1/ev3_flip_points.py b/probes/weather-p1/ev3_flip_points.py index d6571271..0a382269 100644 --- a/probes/weather-p1/ev3_flip_points.py +++ b/probes/weather-p1/ev3_flip_points.py @@ -29,6 +29,7 @@ file); parameterized here over VARS instead of hardcoded to one variable. """ import json +import pathlib import numpy as np @@ -172,7 +173,8 @@ def main(): f"(two-regime expectation applies HERE, not to linear)" ) - json.dump(out, open("ev3_flip_points.json", "w"), indent=2) + with open(pathlib.Path(__file__).with_name("ev3_flip_points.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote ev3_flip_points.json") diff --git a/probes/weather-p1/ev4_apparatus_sensitivity.py b/probes/weather-p1/ev4_apparatus_sensitivity.py index 04b91172..df9ddfe8 100644 --- a/probes/weather-p1/ev4_apparatus_sensitivity.py +++ b/probes/weather-p1/ev4_apparatus_sensitivity.py @@ -12,6 +12,7 @@ Verdict is printed, not assumed. Emits ev4_apparatus_sensitivity.json. """ import json +import pathlib import numpy as np @@ -107,7 +108,8 @@ def main(): print(f" method max/min spread: {method_spread:.1f}x") print(f" scale-controlling tail flips across windows: {flips}") print(f"\n {out['verdict']['conclusion']}") - json.dump(out, open("ev4_apparatus_sensitivity.json", "w"), indent=2) + with open(pathlib.Path(__file__).with_name("ev4_apparatus_sensitivity.json"), "w") as fh: + json.dump(out, fh, indent=2) if __name__ == "__main__": diff --git a/probes/weather-p1/go_territory_probe.py b/probes/weather-p1/go_territory_probe.py index 4d6bd2a6..209b03ed 100644 --- a/probes/weather-p1/go_territory_probe.py +++ b/probes/weather-p1/go_territory_probe.py @@ -31,6 +31,7 @@ Hoehenprofil (geopotential stacking) deliberately NOT here — next probe. """ import json +import pathlib import os import urllib.request @@ -213,5 +214,5 @@ def contested_secured(center_list, thresh): "B": {"n_centers": len(det), "by_thresh": out_b, "E2_secured_le_bandmean": b_e2, "E3_random_ratio": b_e3, "gradT_band_mean_K_per_100km": float(band_mean)}, -}, open("go_territory_probe.json", "w"), indent=2) +}, open(pathlib.Path(__file__).with_name("go_territory_probe.json"), "w"), indent=2) print("\nwrote go_territory_probe.json") diff --git a/probes/weather-p1/l4_rail_probe.py b/probes/weather-p1/l4_rail_probe.py index f8ca2e9a..71fc5147 100644 --- a/probes/weather-p1/l4_rail_probe.py +++ b/probes/weather-p1/l4_rail_probe.py @@ -56,6 +56,7 @@ "one table read" property. """ import json +import pathlib import urllib.request import numcodecs @@ -298,5 +299,6 @@ def enc(vals, cb): f"{len(inf_dirs)}/{len(out['cross'])}) {100*worst:+.1f}% " f"(bar <= +1%): {'PASS' if worst <= 0.01 else 'FAIL'}") -json.dump(out, open("l4_rail_probe.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("l4_rail_probe.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote l4_rail_probe.json") diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py index a605132e..ab9dd021 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.py +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -31,6 +31,7 @@ examples/geostrophic_stencil.rs. cos(lat) spacing throughout. """ import json +import pathlib import urllib.request import numcodecs @@ -226,5 +227,6 @@ def delta_entropy(q_u8): f"bucket={bucket:.1f} Pa; added={out['E5_u8']['added_vs_f64_Pa']:.1f}, " f"bar <= {bucket:.1f})") -json.dump(out, open("sunflower_cyclone_probe.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("sunflower_cyclone_probe.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote sunflower_cyclone_probe.json") diff --git a/probes/weather-p1/three_register_probe.py b/probes/weather-p1/three_register_probe.py index 57c347b2..145c22fc 100644 --- a/probes/weather-p1/three_register_probe.py +++ b/probes/weather-p1/three_register_probe.py @@ -52,6 +52,7 @@ scope limit, not hidden. """ import json +import pathlib import urllib.request import numcodecs @@ -254,5 +255,5 @@ def reg_c_fisher_rank(vals, ref_sorted): "R5_cross_variable": r5_rows, "R5_max_spread": float(max_spread), "R5_one_bucket": one_bucket}, - open("three_register_probe.json", "w"), indent=2) + open(pathlib.Path(__file__).with_name("three_register_probe.json"), "w"), indent=2) print("\nwrote three_register_probe.json") diff --git a/probes/weather-p1/voxel_chess_probe.py b/probes/weather-p1/voxel_chess_probe.py index 8c2387df..27379d73 100644 --- a/probes/weather-p1/voxel_chess_probe.py +++ b/probes/weather-p1/voxel_chess_probe.py @@ -28,6 +28,7 @@ EV-1 audit lesson; |lat| < 15 deg masked (f -> 0, geostrophy undefined). """ import json +import pathlib import urllib.request import numcodecs @@ -226,5 +227,6 @@ def geo_corr(ug, vg, u_obs, v_obs, tag): f"momentum zone outside -> rises_then_decays=" f"{out['E6_rankine']['rises_then_decays']}") -json.dump(out, open("voxel_chess_probe.json", "w"), indent=2) +with open(pathlib.Path(__file__).with_name("voxel_chess_probe.json"), "w") as fh: + json.dump(out, fh, indent=2) print("\nwrote voxel_chess_probe.json") From 072a3770efb9c0fb1a609dda4172c5f5877b1550 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 12 Aug 2026 06:44:35 +0000 Subject: [PATCH 23/23] probes/weather-p1: R2 used var() not MSE at 11 sites -- and it hid a +92.76 Pa bias CodeRabbit, PR #926. The headline finding is NOT the arithmetic, it is what the arithmetic was concealing. THE BUG. R2's numerator must be the UNCENTERED mean squared error. Every one of the 11 R2 sites in this arc (8 files) used `var(y - yhat)`, which discards the squared MEAN residual -- so any BIASED reconstruction is flattered. Fixed everywhere, by hand: the reviewer's committable suggestion dropped a `\` line-continuation and is a SyntaxError, so it was not applied. MEASURED IMPACT, not assumed: * ZERO wherever a ring-mean profile is present -- mean(resid) is 1e-12 by construction. Every f64 headline (0.9434 / 0.9090 / 0.972 / 0.926) is unchanged, so no other probe JSON needed regenerating. * carve A moved 0.9212 -> 0.9129, loss_dropped_rings 0.0222 -> 0.0306. * carve D moved 0.943406 -> 0.943403 (2.4e-06). WHY carve A MOVED 3400x MORE. It holds the two outer rings at a fixed value, so it carries a systematic offset -- and var() cannot see an offset at all. In Pa: carve A **+92.76 Pa bias**, carve D +1.59 Pa, f64 +0.00. In-disk variance is ~1e5 Pa^2, so tens of Pa of bias perturbs R2 in the 5th decimal. CONSEQUENCE, and the reason wording changed rather than just digits: I called the 12-byte facet "LOSSLESS" on the strength of an R2 agreeing to four decimals. R2 is structurally near-blind to exactly the defect that matters for an ENCODER, so I inferred losslessness from the one statistic that could not detect loss. Corrected to what is actually true: carve D recovers the f64 spine to within 0.07 Pa RMSE (0.03%) carrying a +1.59 Pa bias -- close, not lossless. The probe now emits RMSE and mean bias in Pa beside every R2, because those are what distinguish the carves. ALSO CORRECTED (all CodeRabbit, all verified before accepting): * 14 LOGICAL VALUES != 14 BYTES -- the model size and the carrier budget are different quantities, conflated in THREE places (report SS1, SS9.1, EPIPHANIES). The measured encoding is the 12-byte 6x(8:8) facet. * domino.rs does NOT "execute exactly this". Checked the source: its W is a FIXED 32x16 kernel whose top 16x16 is a tridiagonal smoothing kernel (domino.rs:113); no learned weights, no gates, no hidden or cell state. The tile-GEMM SHAPE and substrate exist; the model does not. Also flagged that the byte-parity int8 LSTM is an OCR model with its own trained weights -- evidence the primitives exist, not that anything is wired. * theta_e is a moist-adiabatic PROXY, not a complete entropy state, and precipitation is an exported flux, not the entropy-production term. The CT-M1..M3 diabatic gate needs its budget written down first. * "the statistically correct reading" survived in a HEADING one line above its own correction -- the third instance in this document of fixing a claim in one place and leaving its twin. Now "a working hypothesis supported by a structured residual". Docstring Coverage now reports 100.00% PASSED, confirming the 69.23% was stale and that declining to manufacture docstrings for it was correct. Co-Authored-By: Claude Claude-Session: https://claude.ai/code/session_01CcpLeEC3XK8Eye53GKBVvi --- .claude/board/EPIPHANIES.md | 7 +- probes/weather-p1/COMET_TAIL_REPORT.md | 131 ++++++++++++++----- probes/weather-p1/comet_tail_f10_f11.py | 2 +- probes/weather-p1/comet_tail_f14.py | 2 +- probes/weather-p1/comet_tail_f4_f7.py | 2 +- probes/weather-p1/comet_tail_f5_n10.py | 2 +- probes/weather-p1/comet_tail_followup.py | 4 +- probes/weather-p1/comet_tail_probe.py | 6 +- probes/weather-p1/l4_rail_probe.json | 108 ++++++++++----- probes/weather-p1/l4_rail_probe.py | 33 ++++- probes/weather-p1/sunflower_cyclone_probe.py | 2 +- 11 files changed, 216 insertions(+), 83 deletions(-) diff --git a/.claude/board/EPIPHANIES.md b/.claude/board/EPIPHANIES.md index c6291ad0..6941004d 100644 --- a/.claude/board/EPIPHANIES.md +++ b/.claude/board/EPIPHANIES.md @@ -146,7 +146,12 @@ or chance. **The three-part decomposition now on record (report §9):** 1. **Spine `[G]`** — center + ~12 ring means + 1 wn-1 dipole = **90.9–94.3 %** of in-disk variance, unshaken across 3 independent samples / 41+ storms / - 1980–2021. ~14 bytes + an address. *(Regraded in place per the same + 1980–2021. **~14 logical model values** (12 ring means + a 2-value + dipole) plus a center address. *(Corrected 2026-08-12, CodeRabbit PR #926: + this read "~14 bytes + an address", conflating the MODEL size with a + CARRIER budget. The measured encoding is a 12-byte `6×(8:8)` L4 facet — + see report §6.1; the byte budget belongs in the encoding section, not + in the spine statement.)* *(Regraded in place per the same allowance used at line 67 above: this line printed 93–97 %, which `E-THE-HEADLINE-NUMBER-MEASURED-A-MODEL-NOBODY-CLAIMED-1` — the entry directly above — corrects to the 14-value model's real figure. Flagged by diff --git a/probes/weather-p1/COMET_TAIL_REPORT.md b/probes/weather-p1/COMET_TAIL_REPORT.md index 9bb2bb29..d9feff01 100644 --- a/probes/weather-p1/COMET_TAIL_REPORT.md +++ b/probes/weather-p1/COMET_TAIL_REPORT.md @@ -85,8 +85,9 @@ generalization beyond n=2. > samples (n=41 total storms). The structural claim (wn1 dominance, > explanatory power) is untouched throughout. -Product consequence `[S]`: a storm's pressure field compresses to -**center position + ~12 ring means + one dipole vector** at **90.9–94.3 %** +Product consequence `[S]`: a storm's pressure field compresses to a +**center address (metadata) + 14 logical fit values** — ~12 ring means plus a +2-value dipole — at **90.9–94.3 %** variance explained (the 14-value constrained model — see the correction block above; the 93–97 % figure belonged to a 36-parameter model and is superseded). The dipole's orientation is *suggestively* related to the motion vector but @@ -94,8 +95,12 @@ The dipole's orientation is *suggestively* related to the motion vector but largest sample) — so the compression claim stands on its own, and the "single-frame motion predictor" reading does not yet. The representation is a natural fit for the substrate's 3-integer spiral addressing (`highheelbgz`), -and **measured to fit the L4 carrier exactly**: 12 bytes as `6×(8:8)` -reproduces the f64 spine to 4 decimals (`l4_rail_probe.py`, §6.1). +and those 14 logical values are **measured to fit the L4 carrier**: the +encoding is a **12-byte `6×(8:8)` facet** (10 ring bytes spread over the radius +with 2 interpolated, plus a 2-byte dipole rail), recovering the f64 spine to +within 0.07 Pa RMSE (`l4_rail_probe.py`, §6.1). **14 logical values ≠ 14 +bytes** — the model size and the carrier budget are different quantities and +are kept separate throughout. --- @@ -816,27 +821,55 @@ distance/compose tables; similarity = ONE table read". A single byte is the *selector*; the **pair** is a cell in the centroid tile, and the tile is where the distribution lives. I had built one rail and called it the carrier. -`l4_rail_probe.py`, four pre-registered bars, **two failed as written**: - -| carve | 12 B? | storm 1 | storm 2 | -|---|:--:|---:|---:| -| f64 constrained spine (reference) | — | 0.9434 | 0.9090 | -| **D — dipole rail + 10 ring bytes spread over the full radius** | **yes** | **0.9434** | **0.9090** | -| A — dipole rail + rings 0–9, outer rings held | yes | 0.9212 | 0.9037 | -| B — all 12 rings, no dipole rail | yes | 0.6348 | 0.2943 | - -**The 12-byte facet is lossless against the f64 spine** — carve D matches to -four decimals on both storms. R² is demonstrably sensitive here (carve B, the -same byte budget spent differently, collapses to 0.63/0.29), so this is -recovery, not insensitivity. +`l4_rail_probe.py`, four pre-registered bars, **two failed as written**. +Numbers below are the CORRECTED ones — see the ⚠ note at the end of this +section; an earlier version of this table used a `var()`-based R² that +flattered every biased carve. + +| carve | 12 B? | storm 1 R² | storm 2 R² | RMSE Pa (s1) | **bias Pa (s1)** | +|---|:--:|---:|---:|---:|---:| +| f64 constrained spine (reference) | — | 0.94344 | 0.90905 | 241.64 | +0.00 | +| **D — dipole rail + 10 ring bytes spread over the full radius** | **yes** | **0.94340** | **0.90903** | **241.71** | **+1.59** | +| A — dipole rail + rings 0–9, outer rings held | yes | 0.91287 | 0.90177 | 299.91 | **+92.76** | +| B — all 12 rings, no dipole rail | yes | 0.63479 | 0.29426 | — | — | + +**The 12-byte facet recovers the f64 spine to within 0.07 Pa RMSE (0.03 %), +carrying a +1.59 Pa mean bias.** It is **NOT lossless** — an earlier version of +this section said so, on the strength of an R² that agreed to four decimals, +and that was the wrong statistic to conclude it from (⚠ below). R² is +nonetheless demonstrably *sensitive* here — carve B, the same byte budget spent +differently, collapses to 0.63/0.29 — so carve D is genuine recovery, not +insensitivity. + +> **⚠ CORRECTION 2026-08-12 (CodeRabbit, PR #926) — R² was computed with +> `var()` instead of the uncentered MSE, at 11 sites across 8 probes.** +> `1 − var(y−ŷ)/var(y)` silently discards the squared MEAN residual, so any +> BIASED reconstruction is flattered. Measured consequences: +> - **Where a ring-mean profile is present the effect is exactly zero** +> (`mean(resid)` = 1e-12 by construction), so **every f64 headline in this +> report — 0.9434 / 0.9090 / 0.972 / 0.926 — is unchanged.** +> - **Carve A moved 0.9212 → 0.9129** and its `loss_dropped_rings` 0.0222 → +> 0.0306. It holds the two outer rings at a fixed value; `var()` could not +> see the resulting **+92.76 Pa** offset at all. +> - Carve D moved 0.943406 → 0.943403 (2.4e-06). +> +> **The deeper lesson, which is why the wording changed and not just the +> digits:** in-disk variance here is ~1e5 Pa², so a systematic offset of tens +> of Pa perturbs R² in the 5th decimal. **R² is structurally near-blind to +> exactly the defect that matters for an encoder**, and "lossless" was inferred +> from the one statistic that could not detect the loss. The probe now reports +> **RMSE and mean bias in Pa alongside every R²**, because those are what +> distinguish the carves. Three results worth more than the headline: -- **L1 FAILED as written** (storm 1: 0.0222 against a 0.02 bar) and the +- **L1 FAILED as written** (storm 1: **0.0306** against a 0.02 bar) and the decomposition names the cause exactly: **quantization +0.0000, dropped - rings +0.0222**. The carrier's *precision* is free; its *capacity* was the + rings +0.0306**. The carrier's *precision* is free; its *capacity* was the entire miss. Spending the same 12 bytes across the full radius (carve D) - rather than on the inner 10 plus a held edge erases it. + rather than on the inner 10 plus a held edge erases it — and in Pa the gap + is far starker than R² suggests: carve A's held outer rings cost **+92.76 Pa + of bias**, carve D's **+1.59 Pa**. - **L3 FAILED, and so did my proposed rescue.** Fisher-z centroid axes are **5× worse** than uniform on the ring means (18.07 vs 3.84 Pa). I hypothesised the population was wrong — ranks taken against the 24 encoded @@ -1043,8 +1076,9 @@ better than my "borderline" framing: > Moderatoren; aber wir haben bereits das Gerüst, um das Zentrum und die > Dynamik zu modellieren."* -**Why this framing is not spin — it is the statistically correct reading of -the residual.** A directional main effect at 0.68–0.73 sign consistency +**Why this framing is a working hypothesis supported by a structured +residual — not spin, and not an established reading either.** A directional +main effect at 0.68–0.73 sign consistency whose residual were *random* would be a dying claim. This chain's residual is not random: it runs **monotonically with a measured variable** — the height ladder (§5.2/5.8), ≈ −40° at 1000 hPa climbing smoothly through zero @@ -1054,7 +1088,12 @@ identified covariate* is **consistent with a missing moderator and requires independent validation**. `[H]` at the ladder's n=2; `[G]` that the framing follows if the ladder replicates. -> **Corrected 2026-08-11 (CodeRabbit, PR #926).** This paragraph read "*is the +> **Corrected 2026-08-11, and again 2026-08-12 (CodeRabbit, PR #926).** Round +> one softened the BODY; the **heading still said "it is the statistically +> correct reading"** — the same overclaim, one line above its own correction, +> which is the third instance in this document of fixing a claim in one place +> and leaving its twin. Round two rewrote the heading to "a working hypothesis +> supported by a structured residual". The original body read "*is the > signature of a missing moderator, NOT of a null. A null does not produce a > ladder.*" That overstates what a monotonic residual establishes: it supports > the hypothesis without discriminating it from model misspecification, @@ -1071,7 +1110,9 @@ across three independent samples spanning 1980–2021, 41+ storms, four seasons, never shaken once (N3/N4; §5.11's own subset: median wn1_frac 0.60, R² 0.90). This is a skeleton that models the **center and the first asymmetry mode of the -dynamics** in ~14 bytes plus an address — which is, as the operator notes, +dynamics** in **14 logical values** plus a center address — encoded, per +§6.1, in a **12-byte** `6×(8:8)` facet (values and bytes are different +quantities; this line said "~14 bytes" until 2026-08-12) — which is, as the operator notes, already more explicit structure than a learned model exposes. ### 9.2 The DRY moderators — measured in this chain, not yet wired `[H]` @@ -1114,6 +1155,17 @@ aspirational: 0.19–0.36) are plausibly the diabatically-driven ones. Precip-per-disk / TCWV-dipole-strength is a computable gate variable at intake. +> **⚠ Scope of the moist vocabulary (CodeRabbit, PR #926, 2026-08-12).** +> `θe` is a **moist-adiabatic proxy**, not a complete entropy state for +> variable moisture composition, and precipitation is an **exported water and +> entropy flux**, not the full entropy-production term. Treat θe, TCWV, +> precipitation and vertical velocity here as **proxies**. Before the diabatic +> gate is used as a moderator in CT-M1..M3, the budget it stands for has to be +> written down explicitly — water-vapour, phase-change, latent-heat, +> precipitation and dissipation terms — rather than assumed from θe alone. +> The operator's "eine Art Entropie" framing is directionally right and is +> what makes this worth probing; it is not yet a defined budget. + Named falsifiers, NOT run, `[S]` until probed: **CT-M1** — the TCWV/θe wn-1 dipole leads the pressure dipole in bearing (moisture converges *ahead* of the low, ≈90° from the left-of-motion low pole); **CT-M2** — 6h disk @@ -1129,15 +1181,28 @@ The shapes already exist and are proven: - **The spine is a board state.** Per storm and timestep: ~16 spine values (center, profile, dipole) + the moderator covariates (steering vector, f, - surface fraction, diabatic gate). A moderator set IS a weight matrix `W`; - `domino.rs`'s symbiont step (`C = A·W`, 16-board AMX/int8 tile-GEMM with - requantize feedback) executes exactly this — and the stencil-as-GEMM - path is already **byte-proven on real WB2 data** in ndarray's - `examples/geostrophic_stencil.rs` (4/4 pre-registered bars, corr 0.9985). -- **The recurrence is an LSTM-shaped problem.** Successive 6h spine states - are a short sequence; the workspace already carries byte-parity-proven - int8 LSTM machinery (`tesseract-recognizer`, `E-OCR-LSTM-1`) consuming - the same `ndarray` tile-GEMM. + surface fraction, diabatic gate). A moderator set IS a weight matrix `W`, + and `domino.rs`'s symbiont step has the right SHAPE — `C[16,16] = + A[16,32]·W[32,16]`, a 16-board AMX BF16 tile-GEMM with requantise feedback. + The stencil-as-GEMM path is separately **byte-proven on real WB2 data** in + ndarray's `examples/geostrophic_stencil.rs` (4/4 pre-registered bars, + corr 0.9985). + + > **⚠ Corrected 2026-08-12 (CodeRabbit, PR #926; verified against + > `crates/symbiont/src/domino.rs`).** This bullet said domino.rs "executes + > exactly this". **It does not.** Its `W` is a *fixed* 32×16 BF16 kernel + > whose top 16×16 is a **tridiagonal smoothing kernel** (`domino.rs:113`); + > the sweep overwrites board lanes and updates `Energy`. There are **no + > learned weights, no gate matrices, no hidden state and no cell state**. + > What exists is the tile-GEMM *shape and substrate*, not the model. + > Weather tensor shapes, a training procedure, and the recurrent update + > are all still undefined — that is the work, not a wiring job. +- **The recurrence is an LSTM-shaped problem** `[S]`. Successive 6h spine + states are a short sequence, and the workspace carries byte-parity-proven + int8 LSTM machinery (`tesseract-recognizer`, `E-OCR-LSTM-1`) on the same + `ndarray` tile-GEMM. **That LSTM is an OCR model with its own trained + weights** — it is evidence the *primitives* exist, not that anything is + wired to weather. - **The hybrid is the honest architecture:** explicit physics as the spine (this report), learned weights as the moderators — the NeuralGCM-shaped split, on a 512-byte-per-storm substrate encoding, with the training diff --git a/probes/weather-p1/comet_tail_f10_f11.py b/probes/weather-p1/comet_tail_f10_f11.py index 3f97b6a7..242847ec 100644 --- a/probes/weather-p1/comet_tail_f10_f11.py +++ b/probes/weather-p1/comet_tail_f10_f11.py @@ -152,7 +152,7 @@ def decompose_ll(field, latc, lonc): ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), - "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + "R2_profile_wn1": float(1.0 - np.mean((resid0 - wn1) ** 2) / vals.var())} def subgrid_min(field, ci, cj): diff --git a/probes/weather-p1/comet_tail_f14.py b/probes/weather-p1/comet_tail_f14.py index 76b4a961..1269b088 100644 --- a/probes/weather-p1/comet_tail_f14.py +++ b/probes/weather-p1/comet_tail_f14.py @@ -169,7 +169,7 @@ def decompose_ll(field, latc, lonc): ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), - "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + "R2_profile_wn1": float(1.0 - np.mean((resid0 - wn1) ** 2) / vals.var())} def subgrid_min(field, ci, cj): diff --git a/probes/weather-p1/comet_tail_f4_f7.py b/probes/weather-p1/comet_tail_f4_f7.py index c403db68..bef0d68b 100644 --- a/probes/weather-p1/comet_tail_f4_f7.py +++ b/probes/weather-p1/comet_tail_f4_f7.py @@ -177,7 +177,7 @@ def decompose_ll(field, latc, lonc): ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), "wn1_frac": float(wn1.var() / resid0.var()), - "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var())} + "R2_profile_wn1": float(1.0 - np.mean((resid0 - wn1) ** 2) / vals.var())} def wrap_deg(d): diff --git a/probes/weather-p1/comet_tail_f5_n10.py b/probes/weather-p1/comet_tail_f5_n10.py index d837abad..85d3f7e1 100644 --- a/probes/weather-p1/comet_tail_f5_n10.py +++ b/probes/weather-p1/comet_tail_f5_n10.py @@ -192,7 +192,7 @@ def decompose_ll(field, latc, lonc): ph = np.arctan2(np.sum(b1 * w), np.sum(a1 * w)) return {"low_pole_rad": float((ph + np.pi) % (2 * np.pi)), "wn1_frac": float(wn1.var() / max(resid0.var(), 1e-12)), - "R2_profile_wn1": float(1.0 - (resid0 - wn1).var() / vals.var()), + "R2_profile_wn1": float(1.0 - np.mean((resid0 - wn1) ** 2) / vals.var()), "amp_by_ring": amp, "ring_mid_km": (np.arange(nb) + 0.5) * RING} diff --git a/probes/weather-p1/comet_tail_followup.py b/probes/weather-p1/comet_tail_followup.py index ce66b063..e85109b2 100644 --- a/probes/weather-p1/comet_tail_followup.py +++ b/probes/weather-p1/comet_tail_followup.py @@ -157,8 +157,8 @@ def decompose(field, ci, cj): resid1 = resid0 - wn1 var_t = vals.var() - e1 = 1.0 - resid0.var() / var_t - e2 = 1.0 - resid1.var() / var_t + e1 = 1.0 - np.mean(resid0 ** 2) / var_t + e2 = 1.0 - np.mean(resid1 ** 2) / var_t wn1_frac = wn1.var() / resid0.var() amp = np.hypot(a1, b1) diff --git a/probes/weather-p1/comet_tail_probe.py b/probes/weather-p1/comet_tail_probe.py index 163aff36..003cdecb 100644 --- a/probes/weather-p1/comet_tail_probe.py +++ b/probes/weather-p1/comet_tail_probe.py @@ -124,9 +124,9 @@ def decompose(field, ci, cj): wn1_con = X @ coef var_t = vals.var() - e1 = 1.0 - resid0.var() / var_t # profile-only R2 - e2 = 1.0 - resid1.var() / var_t # profile + per-ring wn-1 - e2c = 1.0 - (resid0 - wn1_con).var() / var_t # profile + 2-param dipole + e1 = 1.0 - np.mean(resid0 ** 2) / var_t # profile-only R2 + e2 = 1.0 - np.mean(resid1 ** 2) / var_t # profile + per-ring wn-1 + e2c = 1.0 - np.mean((resid0 - wn1_con) ** 2) / var_t # profile + 2-param dipole wn1_frac = wn1.var() / resid0.var() # amplitude-weighted dipole phase: bearing of the LOW pole diff --git a/probes/weather-p1/l4_rail_probe.json b/probes/weather-p1/l4_rail_probe.json index d5b5932f..a3583eff 100644 --- a/probes/weather-p1/l4_rail_probe.json +++ b/probes/weather-p1/l4_rail_probe.json @@ -4,66 +4,102 @@ "carves": { "storm1": { "uniform": { + "f64_rmse_pa": 241.6361781953603, + "f64_bias_pa": 8.567361346570961e-13, + "carveD_rmse_pa": 241.71119512018444, + "carveD_bias_pa": 1.587879693149758, + "carveA_rmse_pa": 299.9109562762598, + "carveA_bias_pa": 92.76221653277149, "f64_r2": 0.9434382813469204, - "carveA_r2": 0.9212025419045835, - "carveB_r2": 0.6347937748474473, - "carveD_r2": 0.9434055987646875, + "carveA_r2": 0.9128668641737903, + "carveB_r2": 0.634793668913492, + "carveD_r2": 0.943403156268636, "ring_rmse_pa": 4.484955284768884, - "diag_14byte_r2": 0.9434185242110055, - "loss_quantization": 1.9757135914955093e-05, - "loss_dropped_rings": 0.022215982306421966 + "diag_14byte_r2": 0.9434184182770502, + "loss_quantization": 1.9863069870229566e-05, + "loss_dropped_rings": 0.030551554103259937 }, "fisherz": { + "f64_rmse_pa": 241.6361781953603, + "f64_bias_pa": 8.567361346570961e-13, + "carveD_rmse_pa": 244.26761112671338, + "carveD_bias_pa": 16.20184874441947, + "carveA_rmse_pa": 297.62202871400433, + "carveA_bias_pa": 91.1758913030388, "f64_r2": 0.9434382813469204, - "carveA_r2": 0.922244812052903, - "carveB_r2": 0.633764958490918, - "carveD_r2": 0.9424539414147397, + "carveA_r2": 0.9141917931977205, + "carveB_r2": 0.63359636825377, + "carveD_r2": 0.942199652266129, "ring_rmse_pa": 32.22635385503535, - "diag_14byte_r2": 0.9423897078544762, - "loss_quantization": 0.0010485734924442491, - "loss_dropped_rings": 0.020144895801573193 + "diag_14byte_r2": 0.9422211176173282, + "loss_quantization": 0.001217163729592241, + "loss_dropped_rings": 0.028029324419607704 }, "fisherz_field": { + "f64_rmse_pa": 241.6361781953603, + "f64_bias_pa": 8.567361346570961e-13, + "carveD_rmse_pa": 242.24982848889988, + "carveD_bias_pa": 10.04382760223327, + "carveA_rmse_pa": 299.413949917866, + "carveA_bias_pa": 95.5304070007748, "f64_r2": 0.9434382813469204, - "carveA_r2": 0.9219960194074355, - "carveB_r2": 0.634603318038025, - "carveD_r2": 0.9432483555525744, + "carveA_r2": 0.9131554154183784, + "carveB_r2": 0.634566955417994, + "carveD_r2": 0.9431506324649834, "ring_rmse_pa": 16.13215666344166, - "diag_14byte_r2": 0.9432280674015833, - "loss_quantization": 0.00021021394533715654, - "loss_dropped_rings": 0.021232047994147796 + "diag_14byte_r2": 0.9431917047815523, + "loss_quantization": 0.00024657656536808403, + "loss_dropped_rings": 0.03003628936317393 } }, "storm2": { "uniform": { + "f64_rmse_pa": 241.08275703543822, + "f64_bias_pa": -2.1189001172580308e-12, + "carveD_rmse_pa": 241.11386850868683, + "carveD_bias_pa": 0.06394187896472865, + "carveA_rmse_pa": 250.5414872244609, + "carveA_bias_pa": 35.41997442833062, "f64_r2": 0.909049958041129, - "carveA_r2": 0.90373643162312, - "carveB_r2": 0.2942613476793018, - "carveD_r2": 0.9090264889121773, + "carveA_r2": 0.9017732201135151, + "carveB_r2": 0.29426134128134374, + "carveD_r2": 0.9090264825142191, "ring_rmse_pa": 3.1878395651185287, - "diag_14byte_r2": 0.9090264889121773, - "loss_quantization": 2.346912895168085e-05, - "loss_dropped_rings": 0.005290057289057293 + "diag_14byte_r2": 0.9090264825142191, + "loss_quantization": 2.3475526909844113e-05, + "loss_dropped_rings": 0.007253262400704008 }, "fisherz": { + "f64_rmse_pa": 241.08275703543822, + "f64_bias_pa": -2.1189001172580308e-12, + "carveD_rmse_pa": 241.13716161276324, + "carveD_bias_pa": 1.7116952576486248, + "carveA_rmse_pa": 250.83166441880857, + "carveA_bias_pa": 37.53956185125506, "f64_r2": 0.909049958041129, - "carveA_r2": 0.9037507609804531, - "carveB_r2": 0.29425444021139313, - "carveD_r2": 0.9090134892791586, + "carveA_r2": 0.901545555802089, + "carveB_r2": 0.29425262761576254, + "carveD_r2": 0.9090089044463867, "ring_rmse_pa": 3.920293632791127, - "diag_14byte_r2": 0.9090195814442686, - "loss_quantization": 3.0376596860337557e-05, - "loss_dropped_rings": 0.005268820463815493 + "diag_14byte_r2": 0.909017768848638, + "loss_quantization": 3.218919249092789e-05, + "loss_dropped_rings": 0.007472213046549103 }, "fisherz_field": { + "f64_rmse_pa": 241.08275703543822, + "f64_bias_pa": -2.1189001172580308e-12, + "carveD_rmse_pa": 242.4493237572494, + "carveD_bias_pa": -14.03074944463002, + "carveA_rmse_pa": 256.4010807615417, + "carveA_bias_pa": 41.8932699495118, "f64_r2": 0.909049958041129, - "carveA_r2": 0.899871261637639, - "carveB_r2": 0.2935349851907917, - "carveD_r2": 0.9083240011739228, + "carveA_r2": 0.897124891138573, + "carveB_r2": 0.29325938028542453, + "carveD_r2": 0.9080159432790177, "ring_rmse_pa": 21.860300207395742, - "diag_14byte_r2": 0.9083001264236672, - "loss_quantization": 0.0007498316174617825, - "loss_dropped_rings": 0.008428864786028223 + "diag_14byte_r2": 0.9080245215183, + "loss_quantization": 0.0010254365228289375, + "loss_dropped_rings": 0.010899630379727077 } } }, diff --git a/probes/weather-p1/l4_rail_probe.py b/probes/weather-p1/l4_rail_probe.py index 71fc5147..b36a9ab5 100644 --- a/probes/weather-p1/l4_rail_probe.py +++ b/probes/weather-p1/l4_rail_probe.py @@ -123,10 +123,31 @@ def spine(la, lo): def r2_of(s, prof, coef): - """In-disk R2 of a (profile, dipole) reconstruction against the raw field.""" + """In-disk R2 of a (profile, dipole) reconstruction against the raw field. + + The numerator is the UNCENTERED mean squared error, NOT var(): var() drops + the squared MEAN residual, so a BIASED reconstruction is flattered. This + matters here and is not cosmetic -- carve A holds the two outer rings at a + fixed value, and under var() that bias was invisible (coderabbit, PR #926, + 2026-08-12; storm1 carve A moved 0.9212 -> 0.9129 on the fix). + """ + rec = prof[s["rings"]] + coef[0] * s["rr"] * np.cos(s["tt"]) \ + + coef[1] * s["rr"] * np.sin(s["tt"]) + return 1.0 - (np.mean((s["vals"] - rec) ** 2) / s["var"]) + + +def err_pa(s, prof, coef): + """(RMSE, mean bias) in Pa -- the quantity R2 is nearly BLIND to here. + + In-disk variance is ~1e5 Pa^2, so a systematic offset of a couple of Pa + moves R2 by ~1e-6 and vanishes at any printed precision. Reporting R2 + alone is what made a biased reconstruction look 'lossless'; these two + numbers are what actually distinguish the carves. + """ rec = prof[s["rings"]] + coef[0] * s["rr"] * np.cos(s["tt"]) \ + coef[1] * s["rr"] * np.sin(s["tt"]) - return 1.0 - ((s["vals"] - rec).var() / s["var"]) + e = s["vals"] - rec + return float(np.sqrt(np.mean(e ** 2))), float(e.mean()) def cb_uniform(pop): @@ -218,7 +239,13 @@ def enc(vals, cb): pd_ = np.interp(np.arange(s["nb"]), pos, rec[pos]) r2_D = r2_of(s, pd_, coef_q) - row[cbn] = {"f64_r2": float(f64_r2), "carveA_r2": float(r2_A), + rm_f, bias_f = err_pa(s, s["prof"], s["coef"]) + rm_d, bias_d = err_pa(s, pd_, coef_q) + rm_a, bias_a = err_pa(s, pa_A, coef_q) + row[cbn] = {"f64_rmse_pa": rm_f, "f64_bias_pa": bias_f, + "carveD_rmse_pa": rm_d, "carveD_bias_pa": bias_d, + "carveA_rmse_pa": rm_a, "carveA_bias_pa": bias_a, + "f64_r2": float(f64_r2), "carveA_r2": float(r2_A), "carveB_r2": float(r2_B), "carveD_r2": float(r2_D), "ring_rmse_pa": ring_rmse, "diag_14byte_r2": float(r2_over), diff --git a/probes/weather-p1/sunflower_cyclone_probe.py b/probes/weather-p1/sunflower_cyclone_probe.py index ab9dd021..6af3642a 100644 --- a/probes/weather-p1/sunflower_cyclone_probe.py +++ b/probes/weather-p1/sunflower_cyclone_probe.py @@ -104,7 +104,7 @@ def axisym_index(ci_, cj_): prof = np.array([vals[rings == b].mean() if (rings == b).any() else np.nan for b in range(24)]) resid = vals - prof[rings] - return 1.0 - resid.var() / vals.var(), disk + return 1.0 - np.mean(resid ** 2) / vals.var(), disk def spiral_pts(n):