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Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop

Haupt, Sue Ellen; Chapman, William; Adams, Samantha V.; Kirkwood, Charlie; Hosking, J. Scott; Robinson, Niall H.; Lerch, Sebastian; Subramanian, Aneesh C.

Abstract:
The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results.

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Verlagsausgabe §
DOI: 10.5445/IR/1000130502
Veröffentlicht am 19.03.2021
Originalveröffentlichung
DOI: 10.1098/rsta.2020.0091
Scopus
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Mathematik (MATH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1364-503X, 0080-4614, 0264-3820, 0264-3952, 0962-…1-2962, 2053-9231, 2053-9258, 2054-0272, 2054-0299
KITopen-ID: 1000130502
Erschienen in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Verlag The Royal Society
Band 379
Heft 2194
Seiten Art.-Nr.: 20200091
Nachgewiesen in Scopus
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