--- license: gpl-3.0 viewer: false --- # 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://leap-stc.github.io/ChaosBench/) 📚: [https://arxiv.org/abs/2402.00712](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](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 | $N m^{-2}$ | - __Baselines:__ - 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 - 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/` within the following subdirectory: - Scores: `eval/.csv` - Model checkpoints: `lightning_logs/`