---
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

**Step 2**: Install package dependencies
```
cd ChaosBench
pip install -r requirements.txt
```

**Step 3**: Initialize the data space by running
```
cd 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/`