Coronavirus Information for the UC San Diego Community

Our leaders are working closely with federal and state officials to ensure your ongoing safety at the university. Stay up to date with the latest developments. Learn more.

PERSIANN Dynamic Infrared-Rain Rate Model (PDIR) for high-resolution, real-time satellite precipitation estimation

TitlePERSIANN Dynamic Infrared-Rain Rate Model (PDIR) for high-resolution, real-time satellite precipitation estimation
Publication TypeJournal Article
Year of Publication2020
AuthorsNguyen P., Shearer E.J, Ombadi M., Gorooh V.A, Hsu K., Sorooshian S., Logan W.S, Ralph M.
Date Published2020/03
Type of ArticleArticle
ISBN Number0003-0007
Accession NumberWOS:000528815600001
Keywordsalgorithm; california; cloud; Meteorology & Atmospheric Sciences; passive microwave; radar

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 (T-b) 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) T-b-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.

Student Publication: