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Improving infrared-based precipitati...

Nasrollahi, Nasrin.

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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 imagery/ by Nasrin Nasrollahi.
Author:	Nasrollahi, Nasrin.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xxi, 68 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
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:

2131750
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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100 1 $a Nasrollahi, Nasrin. $3 2131749
245 1 0 $a Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery $h [electronic resource] / $c by Nasrin Nasrollahi.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xxi, 68 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
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 Precipitation (Meteorology) $x Remote sensing. $3 2131750
650 0 $a Infrared detectors. $3 739201
650 1 4 $a Earth Sciences. $3 642591
650 2 4 $a Atmospheric Sciences. $3 1019179
650 2 4 $a Geophysics and Environmental Physics. $3 1530865
650 2 4 $a Meteorology. $3 542822
650 2 4 $a Environmental Physics. $3 896229
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-12081-2
950 $a Earth and Environmental Science (Springer-11646)