气候变化领域集成服务门户(CCPortal): PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation

CCPortal

DOI	10.1175/BAMS-D-19-0118.1
	PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation
	Nguyen P.; Shearer E.J.; Ombadi M.; Gorooh V.A.; Hsu K.; Sorooshian S.; Logan W.S.; Ralph M.
发表日期	2020
ISSN	00030007
起始页码	E286
结束页码	E302
卷号	101 期号:3
英文摘要	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. ©2020 American Meteorological Society.
语种	英语
scopus关键词	Decision making; Disaster prevention; Disasters; Flood control; Neural networks; Satellites; Soil moisture; Storms; Water resources; Brightness temperatures; Hydrological forecasting; Instantaneous availabilities; Precipitation estimation; Precipitation measurement; Quantitative precipitation estimation; Satellite precipitation; Spatio-temporal resolution; Rain
来源期刊	Bulletin of the American Meteorological Society
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177941
作者单位	Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, Irvine, CA, United States; Department of Earth Systems Science, University of California, Irvine, Irvine, CA, United States; International Center for Integrated Water Resources Management, Institute for Water Resources, U.S. Army Corps of Engineers, Alexandria, VA, United States; Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United States
推荐引用方式 GB/T 7714	Nguyen P.,Shearer E.J.,Ombadi M.,et al. PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation[J],2020,101(3).
APA	Nguyen P..,Shearer E.J..,Ombadi M..,Gorooh V.A..,Hsu K..,...&Ralph M..(2020).PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation.Bulletin of the American Meteorological Society,101(3).
MLA	Nguyen P.,et al."PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation".Bulletin of the American Meteorological Society 101.3(2020).