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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space.  Using satellite data to estimate precipitation from space...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nasrollahi, Nasrin (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Springer Theses, Recognizing Outstanding Ph.D. Research,
Temas:
Acceso en línea:Texto Completo

MARC

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300 |a XXI, 68 p. 41 illus., 38 illus. in color.  |b online resource. 
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490 1 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5061 
505 0 |a Introduction to the Current States of Satellite Precipitation Products -- False Alarm in Satellite Precipitation Data -- Satellite Observations -- Reducing False Rain in Satellite Precipitation Products Using CloudSat Cloud Classification Maps and MODIS Multi-Spectral Images -- Integration of CloudSat Precipitation Profile in Reduction of False Rain -- Cloud Classification and its Application in Reducing False Rain -- Summary and Conclusions. 
520 |a This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space.  Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved.  The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation. 
650 0 |a Atmospheric science. 
650 0 |a Environmental sciences. 
650 0 |a Physics. 
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