ADVANCED CONCEPTS ON REMOTE SENSING OF PRECIPITATION AT MULTIPLE SCALES

被引:183
作者
Sorooshian, Soroosh [1 ]
AghaKouchak, Amir
Arkin, Phillip [2 ]
Eylander, John [3 ]
Foufoula-Georgiou, Efi [4 ]
Harmon, Russell [5 ]
Hendrickx, Jan M. H. [6 ]
Imam, Bisher
Kuligowski, Robert [7 ]
Skahill, Brian [8 ]
Skofronick-Jackson, Gail [9 ]
机构
[1] Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA 92697 USA
[2] Univ Maryland, College Pk, MD 20742 USA
[3] USA, Engineer Res & Dev Ctr, Hanover, NH USA
[4] Univ Minnesota, Minneapolis, MN USA
[5] USA, Res Lab, Durham, NC USA
[6] New Mexico Inst Min & Technol, Socorro, NM USA
[7] NOAA, NESDIS, STAR, Camp Springs, MD USA
[8] USA, Engineer Res & Dev Ctr, Vicksburg, MS USA
[9] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
关键词
PASSIVE MICROWAVE; RAINFALL; SYSTEM;
D O I
10.1175/2011BAMS3158.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Precipitation is the primary driver of the hydrologic cycle and the main input of hydrometeorological models and climate studies. The accuracy of hydrometeorological predictions significantly relies on the quality of observed precipitation intensity, pattern, duration, and aerial extent. Geostationary Operational Environmental Satellite-R (GOES-R) series will provide the spectral information required to produce precipitation data with 2-km/15-min resolution. An important step toward studying and assessing uncertainties in precipitation products is to define a set of metrics to quantify them. These metrics can serve as objective measures of how well satellite-derived precipitation estimates compare to ground reference observations. Each measure may emphasize a different aspect of performance and the users must decide which are more important to their purposes/applications. Development of uncertainty models for satellitebased precipitation estimates is highly desirable.
引用
收藏
页码:1353 / 1357
页数:5
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