首页|Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

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  • NSTL
  • Amer Meteorological Soc
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a "knowledge-value" gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development-demonstrating both skill and utility across sectors-this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

EnsemblesForecast verification/skillClimate servicesDecision supportEmergency preparednessSocietal impactsHEAVY RAINFALLEAST-AFRICAEL-NINOWEATHERFORECASTSSYSTEMPREDICTABILITYVERIFICATIONDRIVERS

White, Christopher J.、Domeisen, Daniela I., V、DeFlorio, Michael J.、Delle Monache, Luca、Di Giuseppe, Francesca、Garcia-Solorzano, Ana Maria、Gibson, Peter B.、Goddard, Lisa、Romero, Carmen Gonzalez、Graham, Richard J.、Graham, Robert M.、Grams, Christian M.、Huang, W. T. Katty、Jensen, Kjeld、Kilavi, Mary、Lawal, Kamoru A.、Acharya, Nachiketa、Adefisan, Elijah A.、Anderson, Michael L.、Aura, Stella、Balogun, Ahmed A.、Bertram, Douglas、Bluhm, Sonia、Brayshaw, David J.、Browell, Jethro、Bueler, Dominik、Chourio, Xandre、Charlton-Perez, Andrew、Christel, Isadora、Coelho, Caio A. S.、Lee, Robert W.、MacLeod, David、Manrique-Sunen, Andrea、Martins, Eduardo S. P. R.、Maxwell, Carolyn J.、Merryfield, William J.、Munoz, Angel G.、Halford, Alan、Olaniyan, Eniola、Otieno, George、Oyedepo, John A.、Palma, Lluis、Pechlivanidis, Ilias G.、Pons, Diego、Ralph, F. Martin、Reis, Dirceu S.、Remenyi, Tomas A.、Risbey, James S.、Robertson, Donald J. C.、Robertson, Andrew W.、Smith, Stefan、Soret, Albert、Sun, Ting、Todd, Martin C.、Tozer, Carly R.、Vasconcelos, Francisco C., Jr.、Vigo, Ilaria、Waliser, Duane E.、Wetterhall, Fredrik、Wilson, Robert G.

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2022

10.1175/BAMS-D-20-0224.1

Bulletin of the American Meteorological Society

Bulletin of the American Meteorological Society

EISCI
ISSN：0003-0007
年,卷(期)：2022.103(6)
  • 32
  • 89