Improving Remotely-sensed Precipitation Estimates Over Mountainous Regions For Use In Hydrological Models

Abstract:

In support of the National Oceanic and Atmospheric Administration (NOAA) National Weather Service’s (NWS) flash flood warning and heavy precipitation forecast efforts, the NOAA National Environmental Satellite Data and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has been providing satellite based precipitation estimates operationally since 1978. Two of the satellite based rainfall algorithms are the Hydro-Estimator (HE) and the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR). However, unlike the HE algorithm the SCaMPR does not currently make any adjustments for the effects of complex topography on rainfall. This study investigates the potential for improving the SCaMPR algorithm by incorporating an orographic correction and humidity correction based calibration of the SCaMPR against rain gauge transects in northwestern Mexico to identify correctable biases related to elevation, slope, wind direction and humidity. Elevation-dependent bias structure of the SCaMPR algorithm suggest that the rainfall algorithm underestimates precipitation in case of upward atmospheric movements and overestimates rainfall in case of downward atmospheric movements along with mountainous terrain. A regionally dependent empirical elevation-based bias correction technique may help improve the quality of satellite-derived precipitation products. As well as orography, effect of atmospheric indices over precipitation estimates is analyzed. The findings suggest that continued improvement to the developed orographic correction scheme is warranted in order to advance quantitative precipitation estimation in complex terrain regions for use in weather forecasting and hydrologic  applications.