NatureV1

A probabilistic weather and hurricane-track model that treats satellite imagery as geolocated samples on a sphere, not a rectangle of pixels.

pip install "naturev1[all]"

Why not a ViT or CNN over the image

Three things break when you crop a GOES scene, divide by 255 and hand it to a vision backbone:

1. A GOES pixel is not a fixed size. ABI scans in fixed angular steps from geostationary orbit. Measured off a real scene, the limb pixel covers 64× the ground area of the nadir pixel. A convolution weights them equally and silently over-counts the stretched edge of the disk.
2. An array index is not a place. Nothing in image[400, 1200] says 24.7°N 81.3°W.
3. Normalizing to 0–1 destroys the physics. Channel 13 is a brightness temperature: 180 K is an overshooting top, 300 K is warm ocean. Per-image rescaling erases exactly what was measured.

NatureV1 solves the fixed-grid projection for every pixel's latitude/longitude (validated against the file's own metadata — it recovers 2.07 km at nadir where nominal C13 resolution is 2 km), computes each pixel's true ground footprint and uses it as a quadrature weight, and keeps brightness temperature in kelvin. Satellite and analysis data then fuse without either being resampled onto the other's grid.

Every output is a distribution

Track comes out as weighted scenarios, each a full trajectory with a tilting uncertainty ellipse. Collapsing them to one line scores 116 under the mixture likelihood where keeping both branches scores 8.2 — because the average of "recurves offshore" and "hits the coast" is a track through somewhere the storm was never going. Fields carry per-point variance, landfall a probability per lead time, intensity a Saffir-Simpson category with an interval.

It trains twice, because the labels are 4 orders of magnitude too few

An 88M-parameter model fitted to the Atlantic best-track archive is not a weather model. The archive is 55,230 points — 1,167 landfalls, 1,839 rapid intensifications, 2,587 with a radius of maximum wind. That is 0.0006 supervised values per parameter, and what you get back is an expensive lookup table for storms that already happened.

So the backbone never sees it.

Stage one is self-supervised on ERA5 reanalysis, where the label is free: given the atmosphere now, predict it at +6 through +120 hours. The WeatherBench 2 six-hourly store is 92,040 timesteps from 1959 to 2021 — 1.6×10¹⁰ supervised values, 180 per parameter, and nobody annotates anything.

Stage two fine-tunes the storm heads with the backbone frozen. 0.96M parameters of 89M — 1.1% — ever see a best track. Those 24,585 paired storm points can fit 0.96M parameters; they cannot fit 89M.

from naturev1 import ERA5Window, StormWindow, pair_tracks_with_reanalysis

pretrain = ERA5Window(era5, indices=splits["train"], lead_steps=offsets)   # free labels
starts, targets, _ = pair_tracks_with_reanalysis(tracks, store_times, offsets)
finetune = StormWindow(ERA5Window(era5, indices=starts), targets)          # real outcomes
model.freeze_backbone(True)

Longitude rotation is an exact augmentation here, not an approximation: on an equiangular grid, rotating the globe by a whole number of cells is a roll of the array, and this architecture is exactly equivariant to it. 240 valid atmospheres, free, nothing resampled.

Four things the obvious implementation gets wrong

Each of these ran clean on shapes, ranges and a falling loss while the data was wrong, so the regression tests assert on physics and geometry instead:

1. Dimension order. WeatherBench 2 stores (time, longitude, latitude); the coordinate mesh is latitude-major. Read untransposed you get the right point count, sane values, and every sample bound to the wrong place on Earth. Correlation of temperature against its assigned latitude: ~0 → −0.873.
2. Cell weights. cos(latitude) gives the pole row 6.1e-17 — zero in float32 — deleting the poles from every area-weighted sum. True spherical cell area makes a pole cell 306× lighter, not 10¹⁶×.
3. Precipitation. 15% of the grid is exactly zero and the max sits 30σ out, so a z-score trains the model to predict zero everywhere. log1p(x/0.1mm) brings it to 4.5σ and inverts exactly.
4. Missing is not average. Sea-surface temperature is absent over 27.9% of the grid; filling it with the ocean mean asserts warm water over Kansas. Gappy variables get an observation channel.

Heads start at climatology, not at zero

A linear head emits about zero, so asked for central pressure in hPa it opens 1000 off — a squared error of a million. Measured on a real fine-tuning step, the intensity term was 119,191 of a 60,565 total, with a gradient norm of 207,018. Anchoring each head at Atlantic climatology and starting each log-variance at the observed spread (rather than claiming ±1 kt about peak wind) brings the same step to total 58, intensity 4, eyewall 5, gradient norm 13 — with no architecture change. The RI classifier opens at the measured 4.11% base rate, not at even odds.

Built for a runtime that dies

Checkpoints go down on a wall-clock interval, written to a temp file and renamed into place, so a killed cell leaves the previous checkpoint intact. Resume restores optimizer moments, LR schedule, scaler and RNG state — not just weights.

0.8: the whole atmosphere, and an audit

The model now predicts and evolves every prognostic channel — 13 pressure levels included — as a residual from persistence, with solar forcing computed from orbital geometry, mass conserved, a pressure- and area-weighted loss, and fair-CRPS ensembles. Storm heads pool around the storm and are given its current intensity and motion. Stage two runs the frozen backbone once per storm and trains the heads on the cached features, which turned days per epoch into seconds.

A full read of the code found and fixed, among others: a GPU memory leak on every training step with DataLoader workers; hurricane lead targets at the wrong valid time on 10.9% of samples (92% of them on landfalling stretches); a rollout loop that never rolled out; a publish gate that could never open; and untrained heads printed as forecasts. Each has a regression test in tests/test_fixes_080.py.

0.12: the satellite path rebuilt, every basin, 8x B300

Built from what the trained model and its red team showed: intensity biased low, rapid intensification missed, southern-hemisphere tracks worse than physics, poleward turns missed, a storm centre required.

• naturev1.ibtracs — every basin since 1980 (IBTrACS), wind normalised to one-minute, ~230,000 labelled fixes.
• naturev1.satstage — GridSat-B1 1980-2021 (4-6 TB) staged into quarter-degree global frames (mean, coldest pixel, spread, coverage, satellite, view angle) and 320 px storm loops; hand-decoded, quality-controlled, resumable.
• naturev1.satnet — SatNet-Storm (intensity, centre fix from a 150 km-wrong guess, radii, intensity change, ordinal RI, motion; hemisphere-mirrored) and the satellite retrieval (stale state + satellite loop + cloud motion → full state, starting exactly at the stale state).
• naturev1.stormtrain — stage B: heads + the backbone's last blocks on every basin, retrieval in the loop, RI 2.0, poleward frame; banks with two rollout track members; consensus, calibration, intensity bias correction; scored on test seasons against the model it started from.
• naturev1.tracker — vortex tracking through rollouts, storm detection.
• naturev1.engine — DDP on N GPUs, bf16, a clock-driven schedule, every checkpoint pushed to the Hub in order.
• b300_naturev1.py — the runbook: preflight, staging, stage A (3 h, both satellite networks side by side), stage B, and a publish step that only replaces the release if the new model beat it.

Then more ways to be right, each weighted by the held-out seasons (weights may be zero):

• naturev1.thermo — potential intensity (Bister-Emanuel), ventilation, entropy deficit, genesis potential, cyclone phase space and SHIPS-like predictors from the model's own atmosphere, fed to every storm head.
• naturev1.windfield — asymmetric Holland wind field, NHC-style 34/50/64 kt probabilities and arrival times (points or a 0.25 degree map), inland decay, gusts, R-CLIPER rain.
• naturev1.statistical — SHIPS-style regression, LGEM-style logistic growth, climatology-persistence, an intensity consensus with per-lead weights and biases, track bias correction, a predicted-error cone, an RI consensus.
• naturev1.tracker — multi-field centre fix, intensity and phase along the rollout, rollout-ensemble and analog track members; the satellite centre fix iterates to convergence.
• Training and forecast time: intensity loss rebalanced by class, an average of the weights (EMA), and a test-time rotation ensemble used only if it beats a single pass on held-out seasons.

Tested end to end on CPU, including two-process data parallelism; not yet run on B300 hardware.

0.13: hurricane physics that beats extrapolation -- measured, not promised

Technical report: Hurricane tracks without a neural network (CorX Labs report 05, also as a PDF).

naturev1.cyclone -- 39 modules, each one idea, no neural network -- forecasts a storm's track and intensity from one analysis and the best track so far, and detects storms before they form. Every constant was fitted on 1980-2014 best tracks or the 2016/2017/2019 seasons. On the held-out seasons (2018, 2020, 2021, 2022; 4,447 forecasts, 407 storms, every basin; storm-bootstrap 90% intervals):

Intensity (SHIFOR + SHIPS-style + logistic growth, blended per lead): mean absolute error 4/6/11/14/17/20/21/22 kt at 6-120 h against persistence's 4/8/14/19/23/28/31/32 -- +14% to +31%, every interval clear of zero.

It beats extrapolation and CLIPER at every lead, and loses to every modern global model (GraphCast, Pangu, HRES, Aurora, FGN/WeatherNext 2, FuXi, GenCast) past 6 hours; combined with one of them, lead by lead, it is never worse than that model and better at 6-12 hours. Genesis: 68% of formations flagged a median 24 hours ahead (52% false alarms), Brier skill +0.22.

from naturev1.cyclone import physics_forecast       # track, members, cone, regime, Dorian-type stall warning,
fc = physics_forecast(case, environment)            # shear profile, intensity along the track

Also in 0.13: naturev1.distill (multi-teacher distillation with spatial / vertical / temporal alignment of every teacher into NatureV1's space), naturev1.assimilation (bias correction, quality control, local optimal interpolation for live inputs), naturev1.highres (decoding the latent mesh at 0.25 degrees), and molab_naturev1.py, the runbook for one 96 GB GPU. Distillation and the 0.25-degree decoder are tested on CPU, not yet trained.

0.13: the swarm sandbox, the satellite path and Caribbean impacts

naturev1.swarm (idea: Nathan) runs alongside the physics. It is a particle filter over possible futures. Each world is a 4D worldline (longitude, latitude, steering depth, time) with its own physics, integrated in micro-segments and re-weighted minute by minute by NHC's satellite, recon and radar centre fixes. Its wind and rain are then painted onto every parish, province and district of the Caribbean: 309 units, with terrain.

from naturev1.swarm.geography import CARIBBEAN, RegionMap
from naturev1.swarm.live import forecast_storm
region = RegionMap.build(CARIBBEAN)                                   # once; cached
run = forecast_storm("al132025", when="2025-10-27T00", archive=True,  # Melissa as it was known then
                     particles=100_000, region=region)
print(run["impact"].table())                                          # parish-by-parish probabilities

On unseen seasons, its stall probabilities are skilful (Brier +0.08 over climatology) and its loop probabilities are reliable. Its cone is not better than the fixed one, and its own mean track is worse than the consensus, which stays the track. On Melissa the consensus missed the northward turn. The swarm kept western Jamaica at risk, but at 13-20% for hurricane-force wind where the storm brought it to most of the island, because a 160 kt Category 5 was forecast at 111-125 kt. molab_swarm_cell.py is the one-cell molab runbook for live storms.

Not trained

No weights here have seen real data. The architecture, losses, ingest and training loop are complete and exercised end to end on real GOES-19 imagery, but nothing here should inform a decision about a real storm. The National Hurricane Center is the authoritative source for tropical cyclone forecasts.

Made by Nathan.