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03147nam a22005055i 4500 |
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978-3-319-12081-2 |
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DE-He213 |
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20151030111409.0 |
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cr nn 008mamaa |
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141107s2015 gw | s |||| 0|eng d |
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|a 9783319120812
|9 978-3-319-12081-2
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|a 10.1007/978-3-319-12081-2
|2 doi
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|a QC851-999
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|a SCI042000
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|a 551.5
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|a Nasrollahi, Nasrin.
|e author.
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|a Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
|h [electronic resource] /
|c by Nasrin Nasrollahi.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2015.
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|a XXI, 68 p. 41 illus., 38 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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|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.
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|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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|a Earth sciences.
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|a Meteorology.
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|a Atmospheric sciences.
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|a Geophysics.
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|a Environmental sciences.
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4 |
|a Earth Sciences.
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| 650 |
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|a Atmospheric Sciences.
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| 650 |
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|a Geophysics and Environmental Physics.
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| 650 |
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|a Meteorology.
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| 650 |
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|a Environmental Physics.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783319120805
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| 830 |
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-12081-2
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-EES
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| 950 |
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|a Earth and Environmental Science (Springer-11646)
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