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

Autor:	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
Přispěvatelé:	Barcelona Supercomputing Center
Rok vydání:	2022
Předmět:	Artificial intelligence Atmospheric Science Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC] Numerical weather prediction/forecasting Intel·ligència artificial Forecast ­verification/skill Model evaluation/performance Weather forecasting Simulació per ordinador Statistical techniques Precipitation forecasting Regression analysis Neural networks
Zdroj:	Bulletin of the American Meteorological Society. 103:E2878-E2886
ISSN:	1520-0477 0003-0007
DOI:	10.1175/bams-d-22-0046.1
Popis:	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. The 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. Peer Reviewed "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"
Databáze:	OpenAIRE
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