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README.md

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 license: apache-2.0
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+# 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/`