license: gpl-3.0
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ChaosBench
ChaosBench is a benchmark project to improve long-term forecasting of chaotic systems, in particular subseasonal-to-seasonal (S2S) climate, using ML approaches.
🌐: https://leap-stc.github.io/ChaosBench/
📚: https://arxiv.org/abs/2402.00712
✨ 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
Step 0: Clone the 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.
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 ($Pas^{-1}$) ✓ 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 | $ms^{-1}$ |
- 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 | $N m^{-2}$ |
- Baselines:
- Physics-based models (including control/perturbed forecasts):
- 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
- GraphCast
- Fourcastnetv2
- Physics-based models (including control/perturbed forecasts):
🏅 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.
Deterministic-based:
- RMSE
- Bias
- Anomaly Correlation Coefficient (ACC)
- Multiscale Structural Similarity Index (MS-SSIM)
Physics-based:
- Spectral Divergence (SpecDiv)
- Spectral Residual (SpecRes)
Probabilistic-based:
- RMSE Ensemble
- Bias Ensemble
- ACC Ensemble
- MS-SSIM Ensemble
- SpecDiv Ensemble
- SpecRes Ensemble
- Continuous Ranked Probability Score (CRPS)
- Continuous Ranked Probability Skill Score (CRPSS)
- Spread
- 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/