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dc.contributor.authorVitart, Frederic
dc.contributor.authorRobertson, Andrew W.
dc.contributor.authorSpring, Aaron
dc.contributor.authorPinault, Florian
dc.contributor.authorRoskar, Rok
dc.contributor.authorLledó, Llorenç
dc.contributor.authorPalma, Lluis
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2023-03-14T13:30:53Z
dc.date.available2023-06-13T00:25:19Z
dc.date.issued2022
dc.identifier.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.
dc.identifier.issn0003-0007
dc.identifier.issn1520-0477
dc.identifier.urihttp://hdl.handle.net/2117/384938
dc.description.abstractThere 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.
dc.description.sponsorshipThe authors thank the Swiss Data Science Center (SDSC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for their support to this competition. The CRIMS2S team acknowledges support from the Ministère de l’économie, innovation et exportation (MEIE) of Gouvernement du Québec. The UConn team would like to acknowledge contributions from Krishna Pattipati and Peter Willett from the University of Connecticut, Jason Nachamkin from the Naval Research Laboratory Marine Meteorology Division, and Paolo Braca and Leonardo Millefiori from the NATO STO CMRE. The authors thank the three anonymous reviewers for their suggestions and comments that helped improve the manuscript.
dc.language.isoeng
dc.publisherAmerican Meteorological Society (AMS)
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
dc.subject.lcshWeather forecasting.
dc.subject.lcshPrecipitation forecasting
dc.subject.lcshArtificial intelligence
dc.subject.otherNeural networks
dc.subject.otherRegression analysis
dc.subject.otherStatistical techniques
dc.subject.otherForecast ­verification/skill
dc.subject.otherNumerical weather prediction/forecasting
dc.subject.otherModel evaluation/performance
dc.titleOutcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence
dc.typeArticle
dc.subject.lemacSimulació per ordinador
dc.identifier.doi10.1175/BAMS-D-22-0046.1
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://journals.ametsoc.org/view/journals/bams/103/12/BAMS-D-22-0046.1.xml
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
local.citation.publicationNameBulletin of the American Meteorological Society (BAMS)
local.citation.volume103
local.citation.number12
local.citation.startingPageE2878
local.citation.endingPageE2886
dc.description.authorship"Article signat per 22 autors/es: F. Vitart, A. W. Robertson, A. Spring, F. Pinault, R. Roškar, W. Cao, S. Bech, A. Bienkowski, N. Caltabiano, E. De Coning, B. Denis, A. Dirkson, J. Dramsch, P. Dueben, J. Gierschendorf, H. S. Kim, K. Nowak, D. Landry, L. Lledó, L. Palma, S. Rasp, S. Zhou"


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