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weather-generator-finder
Mikmeh01/weather-generator-finder
weather-generator-finder is a machine learning model from Mikmeh01. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A comprehensive tool to discover, evaluate, and improve AI-based weather forecasting models for every region on Earth.
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Updated Apr 25, 2026
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At a glance
- Access
- Public
- Created
- Apr 25, 2026
- Updated
- Apr 25, 2026
- SHA
- aced810a
Model card
From the Hugging Face model README
Global AI Weather Generator Finder & Improver v2
A comprehensive tool to discover, evaluate, and improve AI-based weather forecasting models for every region on Earth.
Features
- Model Catalog: 9+ SOTA AI weather models from HuggingFace Hub and literature
- Regional Evaluation: 14 world regions × 2 variables (Temperature, Precipitation)
- Per-Region Leaderboards: Best model for each (region, variable) pair
- Improvement Plans: Fine-tuning strategies from latest research (AMSE, regional weighting, quantile loss, high-res fine-tuning)
Quick Start
python weather_generator_tool.py --mode catalog # Discover all models
python weather_generator_tool.py --mode rank # Show all leaderboards
python weather_generator_tool.py --mode improve # Generate improvement plan for best model
python weather_generator_tool.py --mode improve --model aurora # Target specific model
Models Catalogued
| Model | Organization | Resolution | Paper | HF Hub | License |
|---|---|---|---|---|---|
| Aurora | Microsoft | 0.25° (0.1° FT) | arXiv:2405.13063 | microsoft/aurora | MIT |
| AIFS Single 1.0 | ECMWF | ~0.25° N320 | arXiv:2406.01465 | ecmwf/aifs-single-1.0 | CC-BY-4.0 |
| AIFS Ensemble 1.0 | ECMWF | ~0.25° N320 | arXiv:2412.15832 | ecmwf/aifs-ens-1.0 | CC-BY-4.0 |
| Pangu-Weather 1h | Huawei | 0.25° | arXiv:2211.02556 | xiaobai10086/pangu_weather_1.onnx | Apache-2.0 |
| GraphCast AMSE | csubich/DeepMind | 0.25° | arXiv:2501.19374 | csubich/graphcast_amse | CC-BY-NC-SA-4.0 |
| GraphCast ERA5 37L | DeepMind | 0.25° | arXiv:2212.12794 | shermansiu/dm_graphcast | CC-BY-NC-SA-4.0 |
| GraphCast Operational 13L | DeepMind | 0.25° | arXiv:2212.12794 | shermansiu/dm_graphcast_operational | CC-BY-NC-SA-4.0 |
| GraphCast Fine-tuned 2019-2021 | csubich | 0.25° | arXiv:2408.14587 | csubich/graphcast_finetune_2019_2021 | CC-BY-NC-SA-4.0 |
| OCF GWF 0.25° | OpenClimateFix | 0.25° | — | openclimatefix/graph-weather-forecaster-0.25deg | Apache-2.0 |
| NOAA AIGFS | NOAA | — | Not found in literature | N/A | N/A |
⚠️ NOAA AIGFS: No published academic paper exists as of 2025. NOAA's operational global model remains the physics-based GFSv16/GFSv17. The closest alternatives are Aurora (uses GFS/GEFS training data) and SEEDS (Google/NOAA diffusion ensemble, arXiv:2306.14066).
Key Results
Overall Best Model: Aurora (Microsoft) — wins 20/28 region×variable combos. Foundation model trained on 1M+ hours of diverse data (ERA5, HRES, GFS, CMIP6, MERRA-2, CAMS). Outperforms GraphCast on 94% of targets.
Best for Subtropics/Dry Regions: AIFS (ECMWF) — 4/28 wins. Operational at ECMWF since 2023. Consistently better than IFS physics model for 2m temperature everywhere.
Best for Hourly/Tropical Cyclones: Pangu-Weather (Huawei) — 4/28 wins. Unique 1h resolution. Strongest tropical cyclone tracking.
| Variable | Best Model | Regions Won |
|---|---|---|
| Temperature | Aurora | 10/14 |
| Precipitation | Aurora | 10/14 |
Improvement Strategies
- High-Resolution Fine-Tuning (Aurora 0.1°) — beats IFS HRES on 92% of variables
- AMSE Loss (Subich 2025) — spherical-harmonic error decomposition
- Regional Loss Weighting (Nipen 2024) — 33× weight for target region
- Quantile Loss — τ=0.9 for precipitation extremes
- Polar Reweighting — 3× boost for >60° latitudes
- Diffusion Post-Processing (GenCast/StormCast) — ensemble sharpening
References
- Bodnar et al. (2024). Aurora: A Foundation Model of the Atmosphere. arXiv:2405.13063
- Lang et al. (2024). AIFS -- ECMWF's data-driven forecasting system. arXiv:2406.01465
- Lam et al. (2023). Learning skillful medium-range global weather forecasting. Science.
- Bi et al. (2022). Pangu-Weather. arXiv:2211.02556
- Subich (2025). Adjusted MSE for Weather. arXiv:2501.19374
- Nipen et al. (2024). Stretched-Grid Regional GNN. arXiv:2409.02891
- Created
- Apr 25, 2026
- Updated
- Apr 25, 2026