Regional and Seasonal Estimates of Fractional Storm Coverage Based on Station Precipitation Observations

Abstract

Simulated climates using numerical atmospheric general circulation models (GCMS) have been shown to he highly sensitive to the fraction of GCM grid area assumed to be wetted during rain events. The model hydrologic cycle and land-surface water and energy balance are influenced by the parameter k?, which is the dimensionless fractional wetted area for GCM grids. Hourly precipitation records for over 1700 precipitation stations within the contiguous United States are used to obtain observation-based estimates of fractional wetting that exhibit regional and seasonal variations. The spatial parameter k? is estimated from the temporal raingauge data using conditional probability relations. Monthly k? values are estimated for rectangular grid areas over the contiguous United States as defined by the Goddard Institute for Space Studies 4° ? 5° GCM. A bias in the estimates is evident due to the unavoidably sparse raingauge network density, which causes some storms to go undetected by the network. This bias is corrected by deriving the probability of a storm escaping detection by the network. A Monte Carlo simulation study is also conducted that consists of synthetically generated storm arrivals over an artificial grid area. It is used to confirm the k? estimation procedure and to test the nature of the bias and its correction. These monthly fractional wetting estimates, based on the analysis of station precipitation data, provide an observational basis for assigning the influential parameter k? in GCM land-surface hydrology parameterizations.