---
license: apache-2.0
---
# 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://github.com/leap-stc/](https://github.com/leap-stc/)

📚: [https://arxiv.org/](https://arxiv.org/)
   
## Table of Content
- [Getting Started](#getting-started)  
- [Dataset Overview](#overview) 
- [Evaluation Metrics](#metrics) 
- [Leaderboard](#leaderboard) 

## Getting Started
1. Set up the project...
- Clone the `ChaosBench` Github repository
- Create local directory to store your data, e.g., `data/`
- Navigate to `chaosbench/config.py` and change the field `DATA_DIR = <YOUR_WORKING_DATA_DIR>`

2. Download the dataset...
- First we perform initialization:
  ```
  cd data/
  wget https://huggingface.co/datasets/juannat7/ChaosBench/blob/main/process.sh
  chmod +x process.sh
  ```
- And finally: (__NOTE__: you can also run each line _one at a time_ to retrieve each dataset)
  ```
  ./process.sh era5            # For input ERA5 data
  ./process.sh ukmo            # For simulation from UKMO
  ./process.sh ncep            # For simulation from NCEP
  ./process.sh cma             # For simulation from CMA
  ./process.sh ecmwf           # 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 ($gpm$) | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; |  
    Specific humidity, q ($kg kg^{-1}$) | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &nbsp; | &nbsp; | &nbsp; |  
    Temperature, t ($K$) | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; |  
    U component of wind, u ($ms^{-1}$) | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; |  
    V component of wind, v ($ms^{-1}$) | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; | &check; |  
    Vertical velocity, w ($Pas^{-1}$) | &nbsp; | &nbsp; | &nbsp; | &nbsp; | &check; | &nbsp; | &nbsp; | &nbsp; | &nbsp; | &nbsp; |  
    
- __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

## 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/`