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 ORCID: https://orcid.org/0000-0002-3646-3504; Robinson, Niall H.; Lerch, Sebastian; Subramanian, Aneesh C.. 2021 Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop [in special issue: Machine learning for weather and climate modelling] Philosophical Transactions of the Royal Society A, 379 (2194), 20200091. 21, pp. https://doi.org/10.1098/rsta.2020.0091

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Text (Open Access) © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. rsta.2020.0091.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1098/rsta.2020.0091
ISSN:	1471-2962
Additional Keywords:	Artificial Intelligence, machine learning, weather, climate, post-processing
Date made live:	22 Feb 2021 10:25 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/527694

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