Optimization of multi-model ensemble forecasting of typhoon waves

作     者：Shun-qi Pan Yang-ming Fan Jia-ming Chen Chia-chuen Kao 

作者机构：Hydro-environmental Research Centre School of Engineering Cardiff University Coastal Ocean Monitoring Center National Cheng Kung University Department of Hydraulic and Ocean Engineering National Cheng Kung University 

出 版 物：《Water Science and Engineering》 (水科学与水工程（英文版）)

年 卷 期：2016年第9卷第1期

页      面：52-57页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 

基　　金：supported by the European Commission within FP7-THEME 6(Grant No.244104) the Natural Environment Research Council(NERC)of the UK(Grant No.NE/J005541/1) the Ministry of Science and Technology(MOST)of Taiwan(Grant No.MOST 104-2221-E-006-183) 

主　　题：Wave modeling Optimization Forecasting Typhoon waves WAVEWATCH III Locally weighted learning algorithm 

摘      要：Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models （model-ensembles）. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting.