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Improving infrared-based precipitati...

Nasrollahi, Nasrin.

Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imageryby Nasrin Nasrollahi.
Author:	Nasrollahi, Nasrin.
Published:	Cham :Springer International Publishing :2015.
Description:	xxi, 68 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Precipitation (Meteorology)Remote sensing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-12081-2
ISBN:	9783319120812 (electronic bk.)

Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery
Nasrollahi, Nasrin.

Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery[electronic resource] /by Nasrin Nasrollahi. - Cham :Springer International Publishing :2015. - xxi, 68 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

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.

ISBN: 9783319120812 (electronic bk.)

Standard No.: 10.1007/978-3-319-12081-2doiSubjects--Topical Terms:

711940
Precipitation (Meteorology)
--Remote sensing.

LC Class. No.: QC925

Dewey Class. No.: 551.5770285

Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery
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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.
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