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contributor authorKirtman, Ben P.
contributor authorMin, Dughong
contributor authorInfanti, Johnna M.
contributor authorKinter, James L.
contributor authorPaolino, Daniel A.
contributor authorZhang, Qin
contributor authorvan den Dool, Huug
contributor authorSaha, Suranjana
contributor authorMendez, Malaquias Pena
contributor authorBecker, Emily
contributor authorPeng, Peitao
contributor authorTripp, Patrick
contributor authorHuang, Jin
contributor authorDeWitt, David G.
contributor authorTippett, Michael K.
contributor authorBarnston, Anthony G.
contributor authorLi, Shuhua
contributor authorRosati, Anthony
contributor authorSchubert, Siegfried D.
contributor authorRienecker, Michele
contributor authorSuarez, Max
contributor authorLi, Zhao E.
contributor authorMarshak, Jelena
contributor authorLim, Young-Kwon
contributor authorTribbia, Joseph
contributor authorPegion, Kathleen
contributor authorMerryfield, William J.
contributor authorDenis, Bertrand
contributor authorWood, Eric F.
date accessioned2017-06-09T16:44:26Z
date available2017-06-09T16:44:26Z
date copyright2014/04/01
date issued2013
identifier issn0003-0007
identifier otherams-73280.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4215376
description abstractt U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.
publisherAmerican Meteorological Society
titleThe North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction
typeJournal Paper
journal volume95
journal issue4
journal titleBulletin of the American Meteorological Society
identifier doi10.1175/BAMS-D-12-00050.1
journal fristpage585
journal lastpage601
treeBulletin of the American Meteorological Society:;2013:;volume( 095 ):;issue: 004
contenttypeFulltext


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