---
license: gpl-3.0
viewer: false
---
# ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

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  <a href="https://leap-stc.github.io/ChaosBench"><img src="https://img.shields.io/badge/View-Documentation-blue?style=for-the-badge" alt="Homepage"/></a>
  <a href="https://arxiv.org/abs/2402.00712"><img src="https://img.shields.io/badge/ArXiV-2402.00712-b31b1b.svg?style=for-the-badge" alt="arXiv"/></a>
  <a href="https://huggingface.co/datasets/LEAP/ChaosBench"><img src="https://img.shields.io/badge/Dataset-HuggingFace-ffd21e?style=for-the-badge" alt="Huggingface Dataset"/></a>
  <a href="https://github.com/leap-stc/ChaosBench/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-GNU%20GPL-green?style=for-the-badge" alt="License Badge"/></a>
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ChaosBench is a benchmark project to improve and extend the predictability range of deep weather emulators to the subseasonal-to-seasonal (S2S) range. Predictability at this scale is more challenging due to its: (1) __double sensitivities__ to intial condition (in weather-scale) and boundary condition (in climate-scale), (2) __butterfly effect__, and our (3) __inherent lack of understanding__ of physical processes operating at this scale. Thus, given the __high socioeconomic stakes__ for accurate, reliable, and stable S2S forecasts (e.g., for disaster/extremes preparedness), this benchmark is timely for DL-accelerated solutions.

## ✨ Features

1️⃣ __Diverse Observations__. Spanning over 45 years (1979 - 2023), we include ERA5/LRA5/ORAS5 reanalysis for a fully-coupled Earth system emulation (atmosphere-terrestrial-sea-ice)

2️⃣ __Diverse Baselines__. Wide selection of physics-based forecasts from leading national agencies in Europe, the UK, America, and Asia

3️⃣ __Differentiable Physics Metrics__. Introduces two differentiable physics-based metrics to minimize the decay of power spectra at long forecasting horizon (blurriness)

4️⃣ __Large-Scale Benchmarking__. Systematic evaluation (deterministic, probabilistic, physics-based) for state-of-the-art ML-based weather emulators like ViT/ClimaX, PanguWeather, GraphCast, and FourcastNetV2

## 🏁 Getting Started
> **_NOTE:_**  Only need the dataset? Jump directly to **Step 2**. If you find any problems, feel free to contact us or raise a GitHub issue.

**Step 0**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository

**Step 1**: Install package dependencies
```
$ cd ChaosBench
$ pip install -r requirements.txt
```

**Step 2**: Initialize the data space by running
```
$ cd data/
$ wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh
$ chmod +x process.sh
```
**Step 3**: Download the data 
```
# Required for inputs and climatology (e.g., normalization)
$ ./process.sh era5
$ ./process.sh lra5
$ ./process.sh oras5
$ ./process.sh climatology

# Optional: control (deterministic) forecasts
$ ./process.sh ukmo
$ ./process.sh ncep
$ ./process.sh cma
$ ./process.sh ecmwf

# Optional: perturbed (ensemble) forecasts
$ ./process.sh ukmo_ensemble
$ ./process.sh ncep_ensemble
$ ./process.sh cma_ensemble
$ ./process.sh ecmwf_ensemble
```

## 🔍 Dataset Overview
All data has daily and 1.5-degree resolution.

1. __ERA5 Reanalysis__ for Surface-Atmosphere (1979-2023). 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; |  
    
2. __LRA5 Reanalysis__ for Terrestrial (1979-2023)

| Acronyms    | Long Name                            | Units            |
|------------------|-------------------------------------------|-----------------------|
| asn     | snow albedo                               | (0 - 1)               |
| d2m     | 2-meter dewpoint temperature              | K                     |
| e       | total evaporation                         | m of water equivalent |
| es      | snow evaporation                          | m of water equivalent |
| evabs   | evaporation from bare soil                | m of water equivalent |
| evaow   | evaporation from open water               | m of water equivalent |
| evatc   | evaporation from top of canopy            | m of water equivalent |
| evavt   | evaporation from vegetation transpiration | m of water equivalent |
| fal     | forecaste albedo                          | (0 - 1)               |
| lai\_hv | leaf area index, high vegetation          | $m^2 m^{-2}$          |
| lai\_lv | leaf area index, low vegetation           | $m^2 m^{-2}$          |
| pev     | potential evaporation                     | m                     |
| ro      | runoff                                    | m                     |
| rsn     | snow density                              | $kg m^{-3}$           |
| sd      | snow depth                                | m of water equivalent |
| sde     | snow depth water equivalent               | m                     |
| sf      | snowfall                                  | m of water equivalent |
| skt     | skin temperature                          | K                     |
| slhf    | surface latent heat flux                  | $J m^{-2}$            |
| smlt    | snowmelt                                  | m of water equivalent |
| snowc   | snowcover                                 | \%                    |
| sp      | surface pressure                          | Pa                    |
| src     | skin reservoir content                    | m of water equivalent |
| sro     | surface runoff                            | m                     |
| sshf    | surface sensible heat flux                | $J m^{-2}$            |
| ssr     | net solar radiation                       | $J m^{-2}$            |
| ssrd    | download solar radiation                  | $J m^{-2}$            |
| ssro    | sub-surface runoff                        | m                     |
| stl1    | soil temperature level 1                  | K                     |
| stl2    | soil temperature level 2                  | K                     |
| stl3    | soil temperature level 3                  | K                     |
| stl4    | soil temperature level 4                  | K                     |
| str     | net thermal radiation                     | $J m^{-2}$            |
| strd    | downward thermal radiation                | $J m^{-2}$            |
| swvl1   | volumetric soil water layer 1             | $m^3 m^{-3}$          |
| swvl2   | volumetric soil water layer 2             | $m^3 m^{-3}$          |
| swvl3   | volumetric soil water layer 3             | $m^3 m^{-3}$          |
| swvl4   | volumetric soil water layer 4             | $m^3 m^{-3}$          |
| t2m     | 2-meter temperature                       | K                     |
| tp      | total precipitation                       | m                     |
| tsn     | temperature of snow layer                 | K                     |
| u10     | 10-meter u-wind                           | $ms^{-1}$             |
| v10     | 10-meter v-wind                           | $ms^{-1}$             |


3. __ORAS Reanalysis__ for Sea-Ice (1979-2023)

| Acronyms    | Long Name                            | Units            |
|------------------|-------------------------------------------|-----------------------|
| iicethic | sea ice thickness                          | m                            |
| iicevelu | sea ice zonal velocity                     | $ms^{-1}$  |
| iicevelv | sea ice meridional velocity                | $ms^{-1}$  |
| ileadfra | sea ice concentration                      | (0-1)      |
| so14chgt | depth of 14$^\circ$ isotherm               | m          |
| so17chgt | depth of 17$^\circ$ isotherm               | m          |
| so20chgt | depth of 20$^\circ$ isotherm               | m          |
| so26chgt | depth of 26$^\circ$ isotherm               | m          |
| so28chgt | depth of 28$^\circ$ isotherm               | m          |
| sohefldo | net downward heat flux                     | $W m^{-2}$ |
| sohtc300 | heat content at upper 300m  | $J m^{-2}$ |
| sohtc700 | heat content at upper 700m | $J m^{-2}$ |
| sohtcbtm | heat content for total water column        | $J m^{-2}$ |
| sometauy | meridonial wind stress                     | $N m^{-2}$ |
| somxl010 | mixed layer depth 0.01                     | m          |
| somxl030 | mixed layer depth 0.03                     | m          |
| sosaline | salinity                                   | Practical Salinity Units (PSU) |
| sossheig | sea surface height                         | m                       |
| sosstsst | sea surface temperature                    | $^\circ C$ |
| sowaflup | net upward water flux                      | $kg/m^2/s$ |
| sozotaux | zonal wind stress                          | $N m^{-2}$ |
 
    
## 💡 Baseline Models
In addition to climatology and persistence, we evaluate the following:

1. __Physics-based models (including control/perturbed forecasts)__:
    - [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
2. __Data-driven models__:
    - [x] Lagged-Autoencoder
    - [x] Fourier Neural Operator (FNO)
    - [x] ResNet
    - [x] UNet
    - [x] ViT/ClimaX
    - [x] PanguWeather
    - [x] GraphCast
    - [x] Fourcastnetv2

## 🏅 Evaluation Metrics
We divide our metrics into 3 classes: (1) Deterministic-based, which cover evaluation used in conventional deterministic forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast, and (3) Probabilistic-based, which account for the skillfulness of ensemble forecasts.


1. __Deterministic-based:__
    - [x] RMSE
    - [x] Bias
    - [x] Anomaly Correlation Coefficient (ACC)
    - [x] Multiscale Structural Similarity Index (MS-SSIM)
2. __Physics-based:__
    - [x] Spectral Divergence (SpecDiv)
    - [x] Spectral Residual (SpecRes)
    
3. __Probabilistic-based:__
    - [x] RMSE Ensemble
    - [x] Bias Ensemble
    - [x] ACC Ensemble
    - [x] MS-SSIM Ensemble
    - [x] SpecDiv Ensemble
    - [x] SpecRes Ensemble
    - [x] Continuous Ranked Probability Score (CRPS)
    - [x] Continuous Ranked Probability Skill Score (CRPSS)
    - [x] Spread
    - [x] Spread/Skill Ratio
 
  
## 🪜 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/`