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标题：WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
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作者：Stephan Rasp ; Peter D. Dueben ; Sebastian Scher 等
期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2020
卷号：12
期号：11
页码：1-29
DOI：10.1029/2020MS002203
出版社：John Wiley & Sons, Ltd.
摘要：Data‐driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data‐driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common data set and evaluation metrics make intercomparison between studies difficult. Here we present a benchmark data set for data‐driven medium‐range weather forecasting (specifically 3–5 days), a topic of high scientific interest for atmospheric and computer scientists alike. We provide data derived from the ERA5 archive that has been processed to facilitate the use in machine learning models. We propose simple and clear evaluation metrics which will enable a direct comparison between different methods. Further, we provide baseline scores from simple linear regression techniques, deep learning models, as well as purely physical forecasting models. The data set is publicly available at https://github.com/pangeo‐data/WeatherBench and the companion code is reproducible with tutorials for getting started. We hope that this data set will accelerate research in data‐driven weather forecasting.
关键词：machine learning;NWP;artificial intelligence;benchmark