Precipitation rate estimates from satellite infrared imagery: A new PERSIANN model
Nguyen, Phu ; Shearer, Eric J. ; Ombadi, Mohammed ; Gorooh, Vesta Afzali ; Hsu, Kuolin ; Sorooshian, Soroosh ; Logan, William S. ; Ralph, Marty
PERSIANN Dynamic Infrared-Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation
Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions - this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared-Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb-rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.</p>
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