Accession Number : ADA267443


Title :   Ensemble Forecasting Techniques in Medium-Range Forecasting


Descriptive Note : Master's thesis


Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA


Personal Author(s) : Warren, Steven W


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a267443.pdf


Report Date : Mar 1993


Pagination or Media Count : 125


Abstract : A 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.... Ensemble models, Regression technique, Forecast divergence, Systematic error


Descriptors :   *MATHEMATICAL MODELS , *WEATHER FORECASTING , WEATHER , PREDICTIONS , MODELS , REDUCTION , PHYSICS , OCEANOGRAPHY , POWER , REGRESSION ANALYSIS , NAVY , COMPARISON , FORECASTING , THESES


Subject Categories : Meteorology
      Numerical Mathematics


Distribution Statement : APPROVED FOR PUBLIC RELEASE