Paper
5 May 2016 Applying satellite remote sensing technique in disastrous rainfall systems around Taiwan
Gin-Rong Liu, Kwan-Ru Chen, Tsung-Hua Kuo, Chian-Yi Liu, Tang-Huang Lin, Liang-De Chen
Author Affiliations +
Abstract
Many people in Asia regions have been suffering from disastrous rainfalls year by year. The rainfall from typhoons or tropical cyclones (TCs) is one of their key water supply sources, but from another perspective such TCs may also bring forth unexpected heavy rainfall, thereby causing flash floods, mudslides or other disasters. So far we cannot stop or change a TC route or intensity via present techniques. Instead, however we could significantly mitigate the possible heavy casualties and economic losses if we can earlier know a TC’s formation and can estimate its rainfall amount and distribution more accurate before its landfalling. In light of these problems, this short article presents methods to detect a TC’s formation as earlier and to delineate its rainfall potential pattern more accurate in advance. For this first part, the satellite-retrieved air-sea parameters are obtained and used to estimate the thermal and dynamic energy fields and variation over open oceans to delineate the high-possibility typhoon occurring ocean areas and cloud clusters. For the second part, an improved tropical rainfall potential (TRaP) model is proposed with better assumptions then the original TRaP for TC rainfall band rotations, rainfall amount estimation, and topographic effect correction, to obtain more accurate TC rainfall distributions, especially for hilly and mountainous areas, such as Taiwan.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gin-Rong Liu, Kwan-Ru Chen, Tsung-Hua Kuo, Chian-Yi Liu, Tang-Huang Lin, and Liang-De Chen "Applying satellite remote sensing technique in disastrous rainfall systems around Taiwan", Proc. SPIE 9876, Remote Sensing of the Atmosphere, Clouds, and Precipitation VI, 98760B (5 May 2016); https://doi.org/10.1117/12.2229214
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Cited by 1 scholarly publication.
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KEYWORDS
Satellites

Technetium

Remote sensing

Floods

Clouds

Earth observing sensors

Algorithm development

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