Real-time Error Correction for Hourly Rainfall Ensemble Forecasting

• Main Authors: ZeYuan, 袁舴
• Other Authors: Pao-Shan Yu
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/87588595661855293860

碩士 === 國立成功大學 === 水利及海洋工程學系 === 104 === The main purpose of this study is to correct the errors of weather research and forecasting (WRF) model in precipitation forecasting. The study area is Kaoping River Basin located in southern Taiwan where usually suffers from floods during typhoon season. In this study, the radar precipitation data are regard as the reference (i.e., true values) to eliminate WRF forecasting error. Two methods, random forests (RF) and support vector machines (SVM), are used to correct the discrepancies between the forecasted precipitation data from WRF model and the radar precipitation data. The correction is based on a real-time updating procedure where two error correction models will be updated every 6 hours (i.e., WRF model provides 6-hour forecast in each run). For example, the error correction models applied to correct the 6-hour ahead precipitation forecasts from WRF model which were set up based on the precipitation data (WRF and the radar precipitation data) from the past 6 hours. The results reveal that the error correction models can further improve WRF model rainfall forecasting by using the real-time updating procedure, however, the accuracy of forecasting decreases with lead time increasing. The results also show that either SVM-based model or RF-based model performs well on two performance indexes (i.e., correlation coefficient and root mean squared error) which can improve the accuracy of precipitation forecasting by 5% to 300% when it compares to original WRF model.