Developing a Real-Time Rainfall Forecasting System Using Weather Satellite Imagery

• Main Authors: Wei-Chun Hong, 洪維均
• Other Authors: Ke-Sheng Cheng
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/39646297708215622177

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 93 === In this study, a feasible system must be built to realize a numerical model that can predict cumulative rainfall of Central Taiwan, by using GMS-5 geostationary meteorological satellite images and hourly rainfall data of some auto-recording rain gauges in Central Taiwan. During the study, rainfall records were used to estimate from dotted data to grid data by Block Kriging Estimation that match the pixels in satellite images, and satellite images were processed to represent the cloud top temperature data (CTT). The kernel function, that means the spatial characteristic of Central Taiwan, was calculated by the grid rainfall data and processed cloud top temperature above using Spatial Convolution Integral. Let the kernel function seemed as the state variable in a time-variant system, Kalman Filtering Algorithm was proposed to forecast this system’s state variable of next time. Then the forecasting can be calculated by Spatial Convolution Integral Technique again to transform into predictions of rainfall. Besides the numerical model development, this study built a practical system that predict rainfall of Central Taiwan by the model. By using the Graphical User Interface (GUI), and updating data hourly, the system will give the predicted data directly. After using two historical typhoon events as trials, the system can predict the trend during whole rainfall events and warn the dangerous districts; but there are still a problem of time-lag makes some error predictions.