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Proceedings Paper

Parameterization of infrared satellite cloud imagery and its application in flood monitoring
Author(s): Xiao-Ping Gu; Chang-Yao Wang; Wen Wang; Yu-Lin Zhan; Shu-Jie Yuan
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Paper Abstract

Weather Satellite data has great potential for Precipitation forecast which plays an important role in flood disaster monitoring. In this paper, the GMS-5 infrared cloud imagery combined with surface temperature data for two years in Binjiang reaches of Guangdong province in China is used to study the relationship between infrared cloud imagery and surface rainfall rates. First, parameterization estimate of infrared cloud imagery is made one the base of atmospheric probing principle, then some parameterization estimate result have been obtained under different analysis field from 3×3 to 15×15 pixels. The result shows:1 there exist obvious correlation between the probability of rain and parameterization estimate such as average brightness temperature(Tb), brightness temperature variance(fc), equivalent cloudage(CN),brightness temperature area index(A1--the first A5--the fifth grade, A6-the sixth grade );2 The rainfall intensity increase with Tb and f and CN, and that it decrease with Tb and A1.Finally,the prediction empirical formula of rainfall intensity has been established by means of optimized subclass regression under different analysis field. The following formula is made under analysis field of 11×11 pixels. The statistical result shows that the average precision of rainfall intensity is about 80% using infrared cloud imagery parameters and the size of analysis field has slight effect on it. If the rainfall intensity reached the storm standard, the flood alarm would be sent out.

Paper Details

Date Published: 22 October 2004
PDF: 9 pages
Proc. SPIE 5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, (22 October 2004); doi: 10.1117/12.565346
Show Author Affiliations
Xiao-Ping Gu, Institute of Remote Sensing Application, CAS (China)
Institute of Mountainous Climate of Guiyang (China)
Chang-Yao Wang, Institute of Remote Sensing Application, CAS (China)
Wen Wang, Institute of Remote Sensing Application, CAS (China)
Yu-Lin Zhan, Institute of Remote Sensing Application, CAS (China)
Shu-Jie Yuan, Heibei Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 5574:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV
Manfred Ehlers; Francesco Posa, Editor(s)

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