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license: gpl-3.0 |
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viewer: false |
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--- |
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# ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction |
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<div align="center" style="display: flex; justify-content: center; gap: 10px;"> |
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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> |
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<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> |
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<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> |
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<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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</div> |
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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. |
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## ✨ Features |
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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) |
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2️⃣ __Diverse Baselines__. Wide selection of physics-based forecasts from leading national agencies in Europe, the UK, America, and Asia |
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3️⃣ __Differentiable Physics Metrics__. Introduces two differentiable physics-based metrics to minimize the decay of power spectra at long forecasting horizon (blurriness) |
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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 |
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## 🏁 Getting Started |
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> **_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. |
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**Step 0**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository |
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**Step 1**: Install package dependencies |
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``` |
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$ cd ChaosBench |
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$ pip install -r requirements.txt |
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``` |
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**Step 2**: Initialize the data space by running |
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``` |
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$ cd data/ |
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$ wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh |
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$ chmod +x process.sh |
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``` |
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**Step 3**: Download the data |
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``` |
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# Required for inputs and climatology (e.g., normalization) |
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$ ./process.sh era5 |
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$ ./process.sh lra5 |
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$ ./process.sh oras5 |
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$ ./process.sh climatology |
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# Optional: control (deterministic) forecasts |
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$ ./process.sh ukmo |
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$ ./process.sh ncep |
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$ ./process.sh cma |
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$ ./process.sh ecmwf |
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# Optional: perturbed (ensemble) forecasts |
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$ ./process.sh ukmo_ensemble |
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$ ./process.sh ncep_ensemble |
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$ ./process.sh cma_ensemble |
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$ ./process.sh ecmwf_ensemble |
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``` |
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## 🔍 Dataset Overview |
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All data has daily and 1.5-degree resolution. |
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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: |
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Parameters/Levels (hPa) | 1000 | 925 | 850 | 700 | 500 | 300 | 200 | 100 | 50 | 10 |
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:---------------------- | :----| :---| :---| :---| :---| :---| :---| :---| :--| :-| |
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Geopotential height, z ($gpm$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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Specific humidity, q ($kg kg^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | | | | |
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Temperature, t ($K$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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U component of wind, u ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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V component of wind, v ($ms^{-1}$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
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Vertical velocity, w ($Pas^{-1}$) | | | | | ✓ | | | | | | |
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2. __LRA5 Reanalysis__ for Terrestrial (1979-2023) |
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| Acronyms | Long Name | Units | |
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|------------------|-------------------------------------------|-----------------------| |
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| asn | snow albedo | (0 - 1) | |
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| d2m | 2-meter dewpoint temperature | K | |
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| e | total evaporation | m of water equivalent | |
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| es | snow evaporation | m of water equivalent | |
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| evabs | evaporation from bare soil | m of water equivalent | |
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| evaow | evaporation from open water | m of water equivalent | |
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| evatc | evaporation from top of canopy | m of water equivalent | |
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| evavt | evaporation from vegetation transpiration | m of water equivalent | |
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| fal | forecaste albedo | (0 - 1) | |
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| lai\_hv | leaf area index, high vegetation | $m^2 m^{-2}$ | |
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| lai\_lv | leaf area index, low vegetation | $m^2 m^{-2}$ | |
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| pev | potential evaporation | m | |
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| ro | runoff | m | |
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| rsn | snow density | $kg m^{-3}$ | |
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| sd | snow depth | m of water equivalent | |
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| sde | snow depth water equivalent | m | |
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| sf | snowfall | m of water equivalent | |
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| skt | skin temperature | K | |
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| slhf | surface latent heat flux | $J m^{-2}$ | |
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| smlt | snowmelt | m of water equivalent | |
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| snowc | snowcover | \% | |
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| sp | surface pressure | Pa | |
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| src | skin reservoir content | m of water equivalent | |
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| sro | surface runoff | m | |
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| sshf | surface sensible heat flux | $J m^{-2}$ | |
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| ssr | net solar radiation | $J m^{-2}$ | |
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| ssrd | download solar radiation | $J m^{-2}$ | |
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| ssro | sub-surface runoff | m | |
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| stl1 | soil temperature level 1 | K | |
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| stl2 | soil temperature level 2 | K | |
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| stl3 | soil temperature level 3 | K | |
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| stl4 | soil temperature level 4 | K | |
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| str | net thermal radiation | $J m^{-2}$ | |
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| strd | downward thermal radiation | $J m^{-2}$ | |
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| swvl1 | volumetric soil water layer 1 | $m^3 m^{-3}$ | |
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| swvl2 | volumetric soil water layer 2 | $m^3 m^{-3}$ | |
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| swvl3 | volumetric soil water layer 3 | $m^3 m^{-3}$ | |
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| swvl4 | volumetric soil water layer 4 | $m^3 m^{-3}$ | |
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| t2m | 2-meter temperature | K | |
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| tp | total precipitation | m | |
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| tsn | temperature of snow layer | K | |
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| u10 | 10-meter u-wind | $ms^{-1}$ | |
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| v10 | 10-meter v-wind | $ms^{-1}$ | |
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3. __ORAS Reanalysis__ for Sea-Ice (1979-2023) |
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| Acronyms | Long Name | Units | |
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|------------------|-------------------------------------------|-----------------------| |
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| iicethic | sea ice thickness | m | |
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| iicevelu | sea ice zonal velocity | $ms^{-1}$ | |
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| iicevelv | sea ice meridional velocity | $ms^{-1}$ | |
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| ileadfra | sea ice concentration | (0-1) | |
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| so14chgt | depth of 14$^\circ$ isotherm | m | |
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| so17chgt | depth of 17$^\circ$ isotherm | m | |
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| so20chgt | depth of 20$^\circ$ isotherm | m | |
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| so26chgt | depth of 26$^\circ$ isotherm | m | |
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| so28chgt | depth of 28$^\circ$ isotherm | m | |
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| sohefldo | net downward heat flux | $W m^{-2}$ | |
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| sohtc300 | heat content at upper 300m | $J m^{-2}$ | |
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| sohtc700 | heat content at upper 700m | $J m^{-2}$ | |
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| sohtcbtm | heat content for total water column | $J m^{-2}$ | |
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| sometauy | meridonial wind stress | $N m^{-2}$ | |
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| somxl010 | mixed layer depth 0.01 | m | |
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| somxl030 | mixed layer depth 0.03 | m | |
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| sosaline | salinity | Practical Salinity Units (PSU) | |
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| sossheig | sea surface height | m | |
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| sosstsst | sea surface temperature | $^\circ C$ | |
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| sowaflup | net upward water flux | $kg/m^2/s$ | |
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| sozotaux | zonal wind stress | $N m^{-2}$ | |
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## 💡 Baseline Models |
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In addition to climatology and persistence, we evaluate the following: |
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1. __Physics-based models (including control/perturbed forecasts)__: |
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- [x] UKMO: UK Meteorological Office |
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- [x] NCEP: National Centers for Environmental Prediction |
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- [x] CMA: China Meteorological Administration |
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- [x] ECMWF: European Centre for Medium-Range Weather Forecasts |
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2. __Data-driven models__: |
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- [x] Lagged-Autoencoder |
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- [x] Fourier Neural Operator (FNO) |
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- [x] ResNet |
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- [x] UNet |
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- [x] ViT/ClimaX |
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- [x] PanguWeather |
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- [x] GraphCast |
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- [x] Fourcastnetv2 |
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## 🏅 Evaluation Metrics |
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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. |
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1. __Deterministic-based:__ |
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- [x] RMSE |
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- [x] Bias |
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- [x] Anomaly Correlation Coefficient (ACC) |
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- [x] Multiscale Structural Similarity Index (MS-SSIM) |
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2. __Physics-based:__ |
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- [x] Spectral Divergence (SpecDiv) |
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- [x] Spectral Residual (SpecRes) |
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3. __Probabilistic-based:__ |
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- [x] RMSE Ensemble |
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- [x] Bias Ensemble |
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- [x] ACC Ensemble |
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- [x] MS-SSIM Ensemble |
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- [x] SpecDiv Ensemble |
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- [x] SpecRes Ensemble |
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- [x] Continuous Ranked Probability Score (CRPS) |
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- [x] Continuous Ranked Probability Skill Score (CRPSS) |
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- [x] Spread |
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- [x] Spread/Skill Ratio |
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## 🪜 Leaderboard |
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You can access the full score and checkpoints in `logs/<MODEL_NAME>` within the following subdirectory: |
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- Scores: `eval/<METRIC>.csv` |
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- Model checkpoints: `lightning_logs/` |
