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Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence

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10.1175/BAMS-D-22-0046.1
 
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hdl:2117/384938

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Vitart, Frederic
Robertson, Andrew W.
Spring, Aaron
Pinault, Florian
Roskar, Rok
Lledó, LlorençMés informació
Palma, Lluis
Document typeArticle
Defense date2022
PublisherAmerican Meteorological Society (AMS)
Rights accessRestricted access - publisher's policy (embargoed until 2023-06-13)
Attribution 4.0 International
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 4.0 International
Abstract
There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.
CitationVitart, F. [et al.]. Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence. "Bulletin of the American Meteorological Society (BAMS)", 2022, vol. 103, núm. 12, p. E2878-E2886. 
URIhttp://hdl.handle.net/2117/384938
DOI10.1175/BAMS-D-22-0046.1
ISSN0003-0007
1520-0477
Publisher versionhttps://journals.ametsoc.org/view/journals/bams/103/12/BAMS-D-22-0046.1.xml
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  • Earth Sciences - Articles de revista [339]
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