Update README.md

README.md

@@ -3,51 +3,63 @@ license: gpl-3.0
 viewer: false
 ---
 # ChaosBench
-We propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for subseasonal-to-seasonal (S2S) climate prediction. 
-It is framed as a high-dimensional video regression task that consists of 45-year, 60-channel observations 
-for validating physics-based and data-driven models, and training the latter. 
-Physics-based forecasts are generated from 4 national weather agencies with 44-day lead-time and serve as baselines to data-driven forecasts. 
-Our benchmark is one of the first to incorporate physics-based metrics to ensure physically-consistent and explainable models. 
-We establish two tasks: full and sparse dynamics prediction. 
+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://leap-stc.github.io/ChaosBench/](https://leap-stc.github.io/ChaosBench/)
 
 📚: [https://arxiv.org/abs/2402.00712](https://arxiv.org/abs/2402.00712)
 
-## Getting Started
-**Step 1**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository
+## ✨ Features
 
-**Step 2**: Install package dependencies
+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
+$ cd ChaosBench
+$ pip install -r requirements.txt
 ```
 
-**Step 3**: Initialize the data space by running
+**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
+$ cd data/
+$ wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh
+$ chmod +x process.sh
 ```
-**Step 5**: Download the data 
-
+**Step 3**: Download the data 
 ```
-# NOTE: you can also run each line one at a time to retrieve individual dataset
-
-./process.sh era5            # Required: For input ERA5 data
-./process.sh climatology     # Required: For climatology
-./process.sh ukmo            # Optional: For simulation from UKMO
-./process.sh ncep            # Optional: For simulation from NCEP
-./process.sh cma             # Optional: For simulation from CMA
-./process.sh ecmwf           # Optional: For simulation from ECMWF
+# 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
+## 🔍 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:
 
-- __Input:__ ERA5 Reanalysis (1979-2023)
-    
-- __Target:__ 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 ($gpm$) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |  
@@ -57,8 +69,84 @@ chmod +x process.sh
     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:
+    - Physics-based models (including control/perturbed forecasts):
         - [x] UKMO: UK Meteorological Office
         - [x] NCEP: National Centers for Environmental Prediction
         - [x] CMA: China Meteorological Administration
@@ -70,23 +158,36 @@ chmod +x process.sh
         - [x] UNet
         - [x] ViT/ClimaX
         - [x] PanguWeather
-        - [x] Fourcastnetv2
         - [x] GraphCast
+        - [x] Fourcastnetv2
 
-## Evaluation Metrics
-We divide our metrics into 2 classes: (1) ML-based, which cover evaluation used in conventional computer vision and forecasting tasks, (2) Physics-based, which are aimed to construct a more physically-faithful and explainable data-driven forecast.
+## 🏅 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.
 
-- __Vision-based:__
+
+1. __Deterministic-based:__
     - [x] RMSE
     - [x] Bias
     - [x] Anomaly Correlation Coefficient (ACC)
     - [x] Multiscale Structural Similarity Index (MS-SSIM)
-- __Physics-based:__
+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
+## 🪜 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/`