Cited by 247Meteorological Phenomena and SimulationsHydrological Forecasting Using AIModel Reduction and Neural Networks

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WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

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Rasp, Stephan, Hoyer, Stephan, Merose, Alexander, Langmore, Ian, Battaglia, Peter, Russell, Tyler, Sanchez‐Gonzalez, Alvaro, Yang, Vivian, Carver, Rob, Agrawal, Shreya, Chantry, Matthew, Ben Bouallegue, Zied, Dueben, Peter, Bromberg, Carla, Sisk, Jared, Barrington, Luke, Bell, Aaron, Sha, Fei·2024·Journal of Advances in Modeling Earth Systems

DOI 10.1029/2023ms004019

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Abstract

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.

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Authors: Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez‐Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Ben Bouallegue, Z., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., & Sha, F.
Affiliations: Google (United States) (US)
Google DeepMind (United Kingdom) (GB)
Google DeepMind London UK
Google Research Mountain View CA USA
European Centre for Medium-Range Weather Forecasts (GB)
European Centre for Medium‐Range Weather Forecasts Reading UK
Corresponding author: Rasp, Stephan
ORCID: Rasp, Stephan
Hoyer, Stephan
Merose, Alexander
Battaglia, Peter
Agrawal, Shreya
Chantry, Matthew
Dueben, Peter
Sha, Fei
Venue: Journal of Advances in Modeling Earth Systems
Publisher: American Geophysical Union (AGU)
Published: 2024-06
Volume: 16
Issue: 6
ISSN: 1942-2466 (ISSN-L)
DOI: 10.1029/2023ms004019
OpenAlex: W4399779937
Cited by: 245
References: 54
Citations by year: 2026: 111 · 2025: 95 · 2024: 39
FWCI: 44.90
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 17 d)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Journal of Advances in Modeling Earth Systems (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
arXiv (Cornell University) (cc-by)
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords: medium-range weather forecasting, forecast evaluation metrics

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WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

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