國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery/ by Nasrin Nasrollahi.
作者:	Nasrollahi, Nasrin.
面頁冊數:	XXI, 68 p. 41 illus., 38 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Atmospheric sciences. -
電子資源:	https://doi.org/10.1007/978-3-319-12081-2
ISBN:	9783319120812

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. - 1st ed. 2015. - XXI, 68 p. 41 illus., 38 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053. - Springer Theses, Recognizing Outstanding Ph.D. Research,.

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

Standard No.: 10.1007/978-3-319-12081-2doiSubjects--Topical Terms:

1179392
Atmospheric sciences.

LC Class. No.: QC851-999

Dewey Class. No.: 551.5

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