GEO-LEOedocs LogoGEO-LEOedocs Logo
  • GEO-LEO
    • Deutsch
    • English
  • GEO-LEO
  • English 
    • Deutsch
    • English
  • Login
View Item 
  •   Home
  • Alle Publikationen
  • Geophysik, Extraterrestische Forschung
  • View Item
  •   Home
  • Alle Publikationen
  • Geophysik, Extraterrestische Forschung
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting

Rasp, StephanORCIDiD
Dueben, Peter D.ORCIDiD
Scher, SebastianORCIDiD
Weyn, Jonathan A.ORCIDiD
Mouatadid, SoukaynaORCIDiD
Thuerey, NilsORCIDiD
DOI: https://doi.org/10.1029/2020MS002203
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9072
Rasp, Stephan; Dueben, Peter D.; Scher, Sebastian; Weyn, Jonathan A.; Mouatadid, Soukayna; Thuerey, Nils, 2020: WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting. In: Journal of Advances in Modeling Earth Systems, Band 12, 11, DOI: 10.1029/2020MS002203.
 
Thumbnail
View/Open
JAME_JAME21209.pdf (5.006Mb)
Metadata Export:
Endnote
BibTex
RIS
  • 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 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.
Statistik:
View Statistics
Collection
  • Geophysik, Extraterrestische Forschung [826]
Subjects:
machine learning
NWP
artificial intelligence
benchmark
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

ImpressumPrivacy (Opt-Out)About usDeposit LicenseSupport: fid-geo-digi@sub.uni-goettingen.de
DFGSUBFID GEOFID Montan
 

 

Submit here
Submission hints
Search hints

All of Geo-Leo e-docsCommunities & CollectionsBy Issue DateContributorsSubjectsPeriodicalsTitlesThis CollectionBy Issue DateContributorsSubjectsPeriodicalsTitles

Statistics

View Usage Statistics

ImpressumPrivacy (Opt-Out)About usDeposit LicenseSupport: fid-geo-digi@sub.uni-goettingen.de
DFGSUBFID GEOFID Montan