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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

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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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Series Title

-

Call Number

551 NAS i

Publisher

: ., 2015

Collation

XXI, 68

Language

English

ISBN/ISSN

978-3-319-12080-5

Classification

551

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Atmospheric Sciences,

Specific Detail Info

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Statement of Responsibility

Nasrin Nasrollahi

Other Information

Cataloger

Khusnun

Source

-

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Journal Volume

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Journal Issue

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