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Weather prediction / Environmental engineering / Hydraulic engineering / Physical geography / Tropical Rainfall Measuring Mission / Precipitation / Rain / Hydrology / Global Energy and Water Cycle Experiment / Atmospheric sciences / Earth / Meteorology


Global Precipitation Estimation from Satellite Image Using Artificial Neural Networks Soroosh Sorooshian, Kuo-lin Hsu, Bisher Imam, and Yang Hong Department of Civil and Environmental Engineering University of California
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Document Date: 2011-03-09 18:16:00

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File Size: 626,66 KB

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City

Madrid / Collins / Honolulu / /

Company

Precipitation Working Group / Naval Research Laboratory / Artificial Neural Networks / /

Continent

North America / /

Country

Mexico / United States / Brazil / Australia / Spain / North America / /

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Event

Natural Disaster / /

Facility

Environmental Engineering University of California / University of Arizona / /

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IndustryTerm

near-real-time global precipitation products / satellite-based estimates / satellite-based technologies / satellite high-resolution precipitation products / interactive map server / energy cycle / basin scale hydrologic applications / rain-rate retrieval algorithms / neural networks / water resources applications / satellite rainfall estimates / satellite-based precipitation algorithms / country-based web / satellite-based precipitation measurement algorithm / operational and semi-operational satellite precipitation estimates / ecological applications / satellite sensor technology / satellite infrared imagery / precipitation product / satellite-based algorithm / neural network / satellite information / orbital satellite imagery / multispectral rainfall algorithm / satellite-based rain retrieval algorithms / satellite image / http /

NaturalFeature

Colorado River / /

Organization

DMSP / National Science Foundation / Yang Hong Department of Civil / National Oceanic and Atmospheric Administration / Microwave Sounding Unit / University of California / Irvine / Civil and Environmental Engineering University / Center for Hydrometeorology and Remote Sensing / University of Birmingham / American Meteorology Society / National Aeronautics and Space Administration / University of Arizona / Department of Hydrology and Water Resources / United Nations Educational Scientific and Cultural Organization / National Weather Service / /

Person

Yang Hong / B. Imam / W.J. Shuttleworth / Dan Braithwaite / R.A. Maddox / J. Sohn / V / Y. Hong / X. Gao / D. Braithwaite / Diane Hohnbaum / L. Lu / H.V. Gupta / K. Hsu / Turk / S. Sorooshian / /

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Position

D.J. / Governor / /

Product

F-13 / F-15 / F-14 / Hydrologic Data / /

ProvinceOrState

M.B. / Mississippi / Arizona / /

PublishedMedium

Journal of Hydrometeorology / Monthly Weather Review / Journal of Climate / Journal of Geophysical Research / /

Region

southwest United States / Northern America / Southwest U.S. / Western Europe / /

Technology

SSM/I rain-rate retrieval algorithms / multispectral rainfall algorithm / satellite-based algorithm / remote sensing / satellite-based rain retrieval algorithms / TRMM-calibrated infrared rainfall algorithm / microwave / Atmospheric Oceanic Technology / satellite sensor technology / precipitation algorithms / image processing / satellite-based precipitation algorithms / neural network / PERSIANN algorithm / Environmental Engineering / TRMM-adjusted PERSIANN algorithm / http / simulation / satellite-based precipitation measurement algorithm / satellite-based technologies / /

URL

http /

SocialTag