WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
By Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter W. Battaglia, T. Russel, Alvaro Sanchez-Gonzalez, Vivian Q. Yang, Rob Carver, Shreya Agrawal and 8 more. Journal of Advances in Modeling Earth Systems, 2023
360 citations recorded by the source corpus.
WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/f46239e42370b0370427bfddda70869ca09fc758
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Citations in the corpus
- Neural general circulation models for weather and climate (2023, methodology): “Our evaluation set-up focuses on quantifying accuracy and physical consistency, following WeatherBench2 12 .”
- Probabilistic weather forecasting with machine learning (2024, neutral): “We convert the ground truth cyclone tracks from ERA5 and HRES-fc0 to binary ground truth maps for each initialization time and lead time.”