Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
Language: English
Published by Springer, 2014
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Seller Inventory # 9783319120805
- Title
- Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
- Author
- Nasrin Nasrollahi
- Publisher
- Springer
- Publication year
- 2014
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3319120808
- ISBN 13
- 9783319120805
- Item weight
- 313 grams
- Dimensions
- 241x160x10 mm
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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