WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models
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.
- Publication:
-
Journal of Advances in Modeling Earth Systems
- Pub Date:
- June 2024
- DOI:
- 10.1029/2023MS004019
- arXiv:
- arXiv:2308.15560
- Bibcode:
- 2024JAMES..1604019R
- Keywords:
-
- machine learning;
- NWP;
- artificial intelligence;
- benchmark;
- Physics - Atmospheric and Oceanic Physics;
- Computer Science - Artificial Intelligence