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WeatherBench: A benchmark dataset for data-driven weather forecasting | | Stephan Rasp
; Peter D. Dueben
; Sebastian Scher
; Jonathan A. Weyn
; Soukayna Mouatadid
; Nils Thuerey
; | | Date: |
2 Feb 2020 | | Abstract: | 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 for numerical weather prediction. First
studies show promise but the lack of a common dataset and evaluation metrics
make inter-comparison between studies difficult. Here we present a benchmark
dataset for data-driven medium-range weather forecasting, 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 a simple and clear evaluation metric
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. All data is publicly
available and the companion code is reproducible with tutorials for getting
started. We hope that this dataset will accelerate research in data-driven
weather forecasting. | | Source: | arXiv, 2002.0469 | | Services: | Forum | Review | PDF | Favorites | |
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