A43K-08
Developing a U.S. Research Agenda to Advance Subseasonal to Seasonal Forecasting
Thursday, 17 December 2015: 15:25
3024 (Moscone West)
Mark Shafer1, Raymond J Ban2, Cecilia M Bitz3, Andy Brown4, Eric Chassignet5, Edward Dunlea6, John A Dutton7, Robert Hallberg8, Anke Kamrath9, Daryl T Kleist10, Pierre F J Lermusiaux11, Hai Lin12, Alison Macalady13, Claudia Mengelt13, Laura Myers14, Julie Pullen15, Scott A Sandgathe16, Duane E Waliser17 and Chidong Zhang18, (1)University of Oklahoma Norman Campus, Norman, OK, United States, (2)Ban and Associates, LLC, Marietta, GA, United States, (3)Univ of Washington, Seattle, WA, United States, (4)UK Met Office, Devon, United Kingdom, (5)Florida State University, Center for Ocean-Atmospheric Prediction Studies, Tallahassee, FL, United States, (6)National Academy of Sciences, Washington, DC, United States, (7)Prescient Weather, Ltd., State College, PA, United States, (8)Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States, (9)National Center for Atmospheric Research, Operations and Services, Boulder, CO, United States, (10)University of Maryland College Park, College Park, MD, United States, (11)Massachusetts Institute of Technology, Cambridge, MA, United States, (12)Environment Canada Dorval, Dorval, QC, Canada, (13)the National Academies, Washington, DC, United States, (14)University of Alabama, College of Engineering, Tuscaloosa, AL, United States, (15)Stevens Institute of Technology, Hoboken, NJ, United States, (16)University of Washington, Seattle, WA, United States, (17)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (18)Univ Miami-RSMAS/MPO, Miami, FL, United States
Abstract:
A National Academies of Sciences, Engineering, and Medicine committee was tasked with developing a strategy to increase the nation's scientific capability for research on sub-seasonal to seasonal prediction of weather and climate over the coming decade. The Committee’s report (released in the fall of 2015) discusses the advancement of S2S prediction skill for weather and ocean forecasts through various mechanisms, including improvements in coupled modeling systems, key observations, data assimilation techniques, and computational and data storage. Further, the report discusses the identification of potential sources of predictability and process studies for incorporating new sources of predictability. Key elements of a long-term research agenda also include understanding the needs of decision makers who use S2S forecasting information and exploring approaches to the communication of S2S prediction information in a way that is useful to and understandable by those decision makers.
