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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery (Springer Theses) - Tapa blanda

Nasrollahi, Nasrin

 
9783319363325: Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery (Springer Theses)
Tapa blanda
ISBN 10: 3319363328 ISBN 13: 9783319363325
Editorial: Springer, 2016

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3319363328
ISBN 13 
9783319363325
Encuadernación
Tapa blanda
Número de páginas
92
Contacto del fabricante
no disponible
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