HR: 14:00h
AN: H43E-03    [Abstracts]
TI: Dependence of Global Model Ensemble Precipitation Forecast Skill on Space and Time Scales
AU: * Perica, S
EM: perica@eng.utah.edu
AF: University of Utah, Department of Civil and Environmental Eng., 122 South Central Campus Drive, Salt Lake City, UT 84112 United States
AU: Schaake, J
EM: john.schaake@noaa.gov
AF: NWS/NOAA, Office of Hydrologic Development, 1325 East-West Highway, Silver Spring, MD 20910 United States
AU: Seo, D
EM: dongjun.seo@noaa.gov
AF: NWS/NOAA, Office of Hydrologic Development, 1325 East-West Highway, Silver Spring, MD 20910 United States
AB: It is well known that some of the skill in precipitation forecasts comes from aggregation, both in space and time. Global model ensemble precipitation forecasts provide useful information on large scale precipitation patterns, but they have to be downscaled to match the input requirements of hydrologic forecast models. Reconstruction of detailed space-time precipitation variability is hence needed, but there is no one particular space-time scale combination that fits all hydrologic models operating at different scales. In this work, we investigate the spatial scale-dependency of the correlation between ensemble mean forecasts and the observed data for a range of space-time scales as a function of forecast lead time. The objective of the study is to develop statistical procedures for down/re-scaling precipitation fields that preserve forecast skill at all space and time scales of interest in support of the NOAA/National Weather Service's (NWS) ensemble hydrologic prediction. The study area is the continental U.S.A. The analysis is done using the daily global model ensemble precipitation reforecasts produced by the NOAA/Climate Diagnostic Center from a frozen version of the Global Forecast System of the NOAA/NWS/National Centers for Environmental Prediction. The 2-week ensemble reforecasts are archived on a 2.5 degree grid and are available for the 1979 - 2004 period. The observed precipitation data used for the analysis comes from the NOAA/National Climatic Data Center.
DE: 1821 Floods
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2005 Joint Assembly