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--- |
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license: gpl-3.0 |
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viewer: false |
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--- |
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# ChaosBench |
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We propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for subseasonal-to-seasonal (S2S) climate prediction. |
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It is framed as a high-dimensional video regression task that consists of 45-year, 60-channel observations |
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for validating physics-based and data-driven models, and training the latter. |
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Physics-based forecasts are generated from 4 national weather agencies with 44-day lead-time and serve as baselines to data-driven forecasts. |
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Our benchmark is one of the first to incorporate physics-based metrics to ensure physically-consistent and explainable models. |
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We establish two tasks: full and sparse dynamics prediction. |
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🔗: [https://leap-stc.github.io/ChaosBench/](https://leap-stc.github.io/ChaosBench/) |
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📚: [https://arxiv.org/abs/2402.00712](https://arxiv.org/abs/2402.00712) |
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## Getting Started |
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**Step 1**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository and install requirements |
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``` |
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pip install -r requirements.txt |
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``` |
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**Step 2**: Create local directory to store your data, e.g., |
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``` |
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cd ChaosBench |
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mkdir data |
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``` |
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**Step 3**: Navigate to `chaosbench/config.py` and change the field `DATA_DIR = ChaosBench/data` |
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**Step 4**: Initialize the space by running |
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``` |
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cd ChaosBench/data/ |
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wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh |
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chmod +x process.sh |
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``` |
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**Step 5**: Download the data |
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``` |
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# NOTE: you can also run each line one at a time to retrieve individual dataset |
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./process.sh era5 # Required: For input ERA5 data |
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./process.sh climatology # Required: For climatology |
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./process.sh ukmo # Optional: For simulation from UKMO |
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./process.sh ncep # Optional: For simulation from NCEP |
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./process.sh cma # Optional: For simulation from CMA |
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./process.sh ecmwf # Optional: For simulation from ECMWF |
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``` |
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## Dataset Overview |
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- __Input:__ ERA5 Reanalysis (1979-2023) |
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- __Target:__ The following table indicates the 48 variables (channels) that are available for Physics-based models. Note that the __Input__ ERA5 observations contains __ALL__ fields, including the unchecked boxes: |
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Parameters/Levels (hPa) | 1000 | 925 | 850 | 700 | 500 | 300 | 200 | 100 | 50 | 10 |
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:---------------------- | :----| :---| :---| :---| :---| :---| :---| :---| :--| :-| |
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Geopotential height, z ($gpm$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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Specific humidity, q ($kg kg^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | | | |
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Temperature, t ($K$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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U component of wind, u ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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V component of wind, v ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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Vertical velocity, w ($Pas^{-1}$) | | | | | ✓ | | | | | | |
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- __Baselines:__ |
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- Physics-based models: |
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- [x] UKMO: UK Meteorological Office |
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- [x] NCEP: National Centers for Environmental Prediction |
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- [x] CMA: China Meteorological Administration |
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- [x] ECMWF: European Centre for Medium-Range Weather Forecasts |
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- Data-driven models: |
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- [x] Lagged-Autoencoder |
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- [x] Fourier Neural Operator (FNO) |
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- [x] ResNet |
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- [x] UNet |
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- [x] ViT/ClimaX |
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- [x] PanguWeather |
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- [x] Fourcastnetv2 |
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- [x] GraphCast |
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## Evaluation Metrics |
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We divide our metrics into 2 classes: (1) ML-based, which cover evaluation used in conventional computer vision and forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast. |
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- __Vision-based:__ |
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- [x] RMSE |
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- [x] Bias |
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- [x] Anomaly Correlation Coefficient (ACC) |
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- [x] Multiscale Structural Similarity Index (MS-SSIM) |
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- __Physics-based:__ |
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- [x] Spectral Divergence (SpecDiv) |
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- [x] Spectral Residual (SpecRes) |
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## Leaderboard |
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You can access the full score and checkpoints in `logs/<MODEL_NAME>` within the following subdirectory: |
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- Scores: `eval/<METRIC>.csv` |
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- Model checkpoints: `lightning_logs/` |
