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  • Author or Editor: Peitao Peng x
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Masao Kanamitsu
,
Arun Kumar
,
Hann-Ming Henry Juang
,
Jae-Kyung Schemm
,
Wanqui Wang
,
Fanglin Yang
,
Song-You Hong
,
Peitao Peng
,
Wilber Chen
,
Shrinivas Moorthi
, and
Ming Ji

The new National Centers for Environmental Prediction (NCEP) numerical seasonal forecast system is described in detail. The new system is aimed at a next-generation numerical seasonal prediction in which focus is placed on land processes, initial conditions, and ensemble methods, in addition to the tropical SST forcing. The atmospheric model physics is taken from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis model, which has more comprehensive land hydrology and improved physical processes. The model was further upgraded by introducing three new parameterization schemes: 1) the relaxed Arakawa–Schubert (RAS) convective parameterization, which improved middle latitude response to tropical heating; 2) Chou's shortwave radiation, which corrected surface radiation fluxes; and 3) Chou's longwave radiation scheme together with smoothed mean orography that reduced model warm bias. Atmospheric initial conditions were taken from the operational NCEP Global Data Assimilation System, allowing the seasonal forecast to start from realistic initial conditions and to seamlessly connect with the short- and medium-range forecasts. The Pacific basin ocean model is the same as that in the old NCEP seasonal system and is coupled to the new atmospheric model with a two-tier approach. The operational atmospheric forecast is performed once a month with a 20-member ensemble. Prior to the forecast, 10-member ensemble hindcasts of the same month from 1979 to the present are performed to define model climatology and model forecast skill. The system has been running routinely since April 2000, and the products are available online at NWS's ftp site.

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Ben P. Kirtman
,
Dughong Min
,
Johnna M. Infanti
,
James L. Kinter III
,
Daniel A. Paolino
,
Qin Zhang
,
Huug van den Dool
,
Suranjana Saha
,
Malaquias Pena Mendez
,
Emily Becker
,
Peitao Peng
,
Patrick Tripp
,
Jin Huang
,
David G. DeWitt
,
Michael K. Tippett
,
Anthony G. Barnston
,
Shuhua Li
,
Anthony Rosati
,
Siegfried D. Schubert
,
Michele Rienecker
,
Max Suarez
,
Zhao E. Li
,
Jelena Marshak
,
Young-Kwon Lim
,
Joseph Tribbia
,
Kathleen Pegion
,
William J. Merryfield
,
Bertrand Denis
, and
Eric F. Wood

The recent 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.

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