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dc.contributor.advisorNuss, Wendell A.
dc.contributor.authorWarren, Steven W.
dc.dateMarch 1993
dc.date.accessioned2014-03-26T23:23:45Z
dc.date.available2014-03-26T23:23:45Z
dc.date.issued1993-03
dc.identifier.urihttp://hdl.handle.net/10945/39902
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.description.abstractA continuing trend in numerical weather prediction (NWP) is the desire for reduced model forecast error. Developments in NWP such as advanced computing power and improved model physics and analysis methods have been successful in lowering error but are potentially limited The regression method of ensemble forecasting is used to further reduce mean forecast error when compared to individual model forecast performances. A statistical regression scheme is utilized to achieve an optimum combination fitting of the National Meteorological Center, the European Centre for Medium-Range Weather Forecasts, and the U.S. Navy Fleet Numerical Oceanography Center forecast models. The performance of the regression model is evaluated for 72-h and 108-h prediction cycles through statistical and subjective comparisons with the individual models and an equally weighted ensemble model at the surface and at 500 hPa. The regression model is shown to produce gains through the reduction of systematic error present in the individual model forecasts...en_US
dc.description.urihttp://archive.org/details/ensembleforecast1094539902
dc.format.extent111 p.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleEnsemble forecasting techniques in medium-range forecastingen_US
dc.typeThesisen_US
dc.contributor.secondreaderWash, Carlyle H.
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentMeteorology
dc.subject.authorEnsemble modelsen_US
dc.subject.authorRegression techniqueen_US
dc.subject.authorForecast divergenceen_US
dc.subject.authorSystematic erroren_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameM.S. in Meteorology and Physical Oceanographyen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineMeteorology and Physical Oceanographyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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