Overview
A multi-model archive of long-range reforecasts from AI weather models, initialized 2000–2025 and rolled out to 50 days — beyond the models' traditional forecast lengths — at daily cadence on a global 0.25° grid.* The archive supports benchmarking of AI weather models, training of downstream ML models, and multi-model blending. It also powers AI-Almanac, UChicago-LAUDE's interactive, human-centered forecast platform. Real-time forecasts initialized from operational ECMWF IFS analyses are planned alongside the reforecasts.
Models
| Model | Class | Grid | Members | Forecast length |
|---|---|---|---|---|
| GraphCast | deterministic | 721 × 1440 (0.25°) | — | 50 d |
| Aurora 1.5 | deterministic | 720 × 1440 (0.25°) | — | 50 d |
| AIFS-single v2 | deterministic | 721 × 1440 (0.25°) | — | 50 d |
| AIFS-ENS v2 | ensemble | 721 × 1440 (0.25°) | 51 | 50 d |
| FGN | ensemble | 721 × 1440 (0.25°) | 51 | 50 d |
| Aurora 1.5 ENS | ensemble | 720 × 1440 (0.25°) | 51 | 50 d |
| NeuralGCM | ensemble | 64 × 128 (≈2.8°) | 51 | 50 d |
Planned updates
The archive is maintained as a living resource. New AI weather models are added as they are released — we expect roughly one to three additions per year, tracking the current pace of model releases.
Each new model is run under the same protocol as those already in the archive: the same 2000–2025 initialization dates, 50-day rollouts, and variable save set. Newly added models are directly comparable to the existing ones without additional reprocessing.
Archive structure & format
Forecasts are stored as one zarr store per model per initialization:
forecasts_<model>/init_YYYYMMDDT00.zarr. Dimensions are
(time, [number,] prediction_timedelta, lat, lon) for 6-hourly
fields and prediction_timedelta_daily for daily aggregates;
number is the ensemble-member dimension. All data are
float32.
Daily aggregates are given by: means of the four 6-hourly values in the UTC calendar day; precipitation is the daily accumulation; 2 m temperature max/min are daily extremes.
Acknowledgements
The reforecasts were generated on the DSI Cluster at the University of Chicago.
Licensing & attribution
Forecasts were produced with third-party models. The archive does not relicense them; the terms below apply to the weights that generated each store.
| Provider | Model | Code | Model weights |
|---|---|---|---|
| ECMWF | AIFS-single v2 | Anemoi, Apache 2.0 | CC BY 4.0 |
| ECMWF | AIFS-ENS v2 | Anemoi, Apache 2.0 | CC BY 4.0 |
| Google DeepMind | GraphCast | Apache 2.0 | CC BY 4.0 |
| Google DeepMind | FGN (WeatherNext 2) | Apache 2.0 | CC BY 4.0 |
| Google Research | NeuralGCM | Apache 2.0 | CC BY-SA 4.0 |
| Microsoft Research | Aurora 1.5 | MIT | MIT |
| Microsoft Research | Aurora 1.5 ENS | MIT | MIT |
ECMWF data
Copyright © European Centre for Medium-Range Weather Forecasts (ECMWF). Source: www.ecmwf.int. Licence: CC BY 4.0. ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use. Contains modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use of the Copernicus information or data.
* NeuralGCM is the exception, on a ≈2.8° grid.