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---
license: gpl-3.0
viewer: false
---
# ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
<div align="center" style="display: flex; justify-content: center; gap: 10px;">
<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>
</div>
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 ($gpm$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
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 ($Pas^{-1}$) | | | | | ✓ | | | | | |
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/` |