{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Haupt SE"],"funding":["NOAA Climate Variability and Predictability Program","Deutsche Forschungsgemeinschaft","US National Science Foundation","Natural Environment Research Council","Polar Science for Planet Earth programme at the British Antarctic Survey","Office of Naval Research"],"pagination":["20200091"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7898128"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["379(2194)"],"pubmed_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"],"journal":["Philosophical transactions. Series A, Mathematical, physical, and engineering sciences"],"pubmed_title":["Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop."],"pmcid":["PMC7898128"],"funding_grant_id":["SFB/TRR 165","1852977","NA18OAR4310405","bas0100032","N00014-17-S-B001"],"pubmed_authors":["Kirkwood C","Chapman W","Robinson NH","Haupt SE","Hosking JS","Subramanian AC","Adams SV","Lerch S"],"additional_accession":[]},"is_claimable":false,"name":"Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop.","description":"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","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Apr","modification":"2025-05-18T11:26:12.117Z","creation":"2021-03-08T08:09:29Z"},"accession":"S-EPMC7898128","cross_references":{"pubmed":["33583264"],"doi":["10.1098/rsta.2020.0091"]}}