HR: 0800h
AN: H11F-0343    [Abstracts]
TI: Optimization and Uncertainty Estimates of WMO Regression Models for Precipitation-Gauge Bias in the United States
AU: * Xia, Y
EM: youlong.xia@noaa.gov
AF: Atmospheric and Oceanic Science Program and NOAA Geophysical Fluid Dynamics Laboratory, Princeton University, Forrestal Campus, US Route 1, Princeton, NJ 08542 United States
AU: Milly, P
EM: Chris.Milly@noaa.gov
AF: US Geological Survey and NOAA Geophysical Fluid Dynamics Laboratory, Forrestal Campus, US Route 1, Priceton, NJ 08542 United States
AU: Dunne, K
EM: Krista.A.Dunne@noaa.gov
AF: US Geological Survey and NOAA Geophysical Fluid Dynamics Laboratory, Forrestal Campus, US Route 1, Priceton, NJ 08542 United States
AB: WMO (World Meteorological Organization) regression models for precipitation-gauge bias developed by Goodison et al.(1998) were optimized using the Very Fast Simulated Annealing algorithm. The regression-model uncertainties were estimated using a Bayesian Stochastic Inversion (BSI) algorithm. Legates and Willmott­_s (1990) precipitation correction factor database (applicable to average monthly conditions) was used as a target database to constrain selection of model parameters. The NLDAS (North American Land Data Assimilation System) Project database, containing daily wind speed and precipitation, was used as an input database for the WMO regression model in the United States. The results show that the optimal regression model is reasonable as its parameters are bounded by those of the WMO Alter-shielded model and unshielded model for both rain and snow. The temporal and spatial analysis of the precipitation correction factors calculated using the optimized regression model shows that they are relatively consistent with the results of Legates and Willmott (1990) in the United States. The advantage of the optimal regression model is that it is able to describe daily and interannual variation of precipitation correction factors. The relations among model parameters and model uncertainties, including regression parameter uncertainty and input data uncertainty, are examined. The results show that there are strong relations between regression model uncertainties and uncertain wind speed from the NLDAS database. Uncertainty of NLDAS data has little effect on optimization of the WMO regression model. However, it has significant effects on uncertainty estimates of the regression model parameters and the precipitation correction factors.
DE: 3354 Precipitation (1854)
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting