UID:
almahu_9947362712302882
Format:
XXI, 68 p. 41 illus., 38 illus. in color.
,
online resource.
ISBN:
9783319120812
Series Statement:
Springer Theses, Recognizing Outstanding Ph.D. Research,
Content:
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.
Note:
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.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783319120805
Language:
English
DOI:
10.1007/978-3-319-12081-2
URL:
http://dx.doi.org/10.1007/978-3-319-12081-2
URL:
Volltext
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