KE-SHENG CHENGSun F. Shih2018-09-102018-09-101992-01http://scholars.lib.ntu.edu.tw/handle/123456789/298598Geostationary operational environmental satellite (GOES) infrared (IR) images and rain gauge measurements are used to study five convective storm events in Florida. The study region is divided into a training area and a test area. An approach is initiated in this study to identify rainfall areas within GOES images using the coldest cloud-top temperature (CCTT) and the standard deviation of cloud-top temperatures (STD-DEV) within a group of pixels. A threshold value of satellite-derived cloud-top temperature is used to define the cloud-covered areas. GOES IR images are then divided into two categories: Expanding cloud images and contracting cloud images. The Bayesian optimal classifier using CCTT and STD-DEV is implemented to further classify the cloud pixels in each category into precipitation-free cloud pixels and precipitating cloud pixels. Cutoff rain rates from the gauge measurements are used to define the actual precipitation-free and precipitating pixels. The results show that the classification accuracies are about 73% in the training area, and 63% in test area. ? ASCE.[SDGs]SDG13Imaging Techniques; Remote Sensing--Agricultural Applications; Satellites--Geostationary; Coldest Cloud Top temperatures; Geostationary Operational Environmental Satellite (GOES); Infrared Images; Rainfall Area Identification; Rain and Rainfalljournal article10.1061/(asce)0733-9437(1992)118:1(179)