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README.md
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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
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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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- [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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View our interactive leaderboard dashboard [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)
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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/`
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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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## 🪜 Leaderboard
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View our interactive leaderboard dashboard [here](https://leap-stc.github.io/ChaosBench/leaderboard.html)
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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/`
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## ✨ Features
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1️⃣ __Diverse Observations__. Spanning over 45 years (1979-), 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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- [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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