TY - JOUR
T1 - Advancing the remote sensing of precipitation
AU - Sorooshian, Soroosh
AU - Aghakouchak, Amir
AU - Arkin, Phillip
AU - Eylander, John
AU - Foufoula-Georgiou, Efi
AU - Harmon, Russell
AU - Hendrickx, Jan M.H.
AU - Imam, Bisher
AU - Kuligowski, Robert
AU - Skahill, Brian
AU - Skofronick-Jackson, Gail
PY - 2011/10
Y1 - 2011/10
N2 - Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable.
AB - Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable.
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U2 - 10.1175/BAMS-D-11-00116.1
DO - 10.1175/BAMS-D-11-00116.1
M3 - Article
AN - SCOPUS:80555146665
SN - 0003-0007
VL - 92
SP - 1271
EP - 1272
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
IS - 10
ER -