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