README.md

metadata

license: apache-2.0
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://github.com/leap-stc/

📚: https://arxiv.org/

Table of Content

• Getting Started
• Dataset Overview
• Evaluation Metrics
• 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
  ./process.sh climatology     # For climatology
  

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:
    • UKMO: UK Meteorological Office
    • NCEP: National Centers for Environmental Prediction
    • CMA: China Meteorological Administration
    • ECMWF: European Centre for Medium-Range Weather Forecasts
  • Data-driven models:
    • Lagged-Autoencoder
    • Fourier Neural Operator (FNO)
    • ResNet
    • UNet
    • ViT/ClimaX
    • PanguWeather
    • 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:
  • RMSE
  • Bias
  • Anomaly Correlation Coefficient (ACC)
  • Multiscale Structural Similarity Index (MS-SSIM)
• Physics-based:
  • Spectral Divergence (SpecDiv)
  • 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/