ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
CoRR(2024)
摘要
Accurate prediction of climate in the subseasonal-to-seasonal scale is
crucial for disaster readiness, reduced economic risk, and improved
policy-making amidst climate change. Yet, S2S prediction remains challenging
due to the chaotic nature of the system. At present, existing benchmarks for
weather and climate applications, tend to (1) have shorter forecasting range of
up-to 14 days, (2) do not include a wide range of operational baseline
forecasts, and (3) lack physics-based constraints for explainability. Thus, we
propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for
S2S prediction. ChaosBench has over 460K frames of real-world observations and
simulations, each with 60 variable-channels and spanning for up-to 45 years. We
also propose several physics-based, in addition to vision-based metrics, that
enables for a more physically-consistent model. Furthermore, we include a
diverse set of physics-based forecasts from 4 national weather agencies as
baselines to our data-driven counterpart. We establish two tasks that vary in
complexity: full and sparse dynamics prediction. Our benchmark is one of the
first to perform large-scale evaluation on existing models including
PanguWeather, FourCastNetV2, GraphCast, and ClimaX, and finds methods
originally developed for weather-scale applications fails on S2S task. We
release our benchmark code and datasets at
https://leap-stc.github.io/ChaosBench.
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