耦合台风天气预报模式和实测数据的神经网络风速预测

基于准定常假定,风荷载与风速平方成正比.为了实现对结构的台风动力效应进行分析预测,建立了耦合数值天气预报(weather research and forecast,WRF)模式和现场实测数据的风速预测神经网络模型,开展台风短期风速的高精度预测.利用该模型对2017年"泰利"和2018年"康妮"的台风风场进行模拟和预测...

Neural network forecasts of typhoon wind speeds coupled with WRF and measured data

Based on the quasi-constant assumption, the wind load is proportional to the square of the wind speed. In order to realize the analysis and prediction of the typhoon dynamic effects of the structure, artificial neural networks coupled with weather research and forecast (WRF) and measured data were developed and trained to forecast the short-term wind speed of Typhoon Talim in 2017 and Typhoon Kong-rey in 2018. The wind speed data were decomposed by wavelet (WT) decomposition and ensemble empirical mode decomposition (EMMD) to improve the training efficiency of the neural network and the accuracy of short-term wind speed prediction. The obtained components would be served as inputs for the neural network. Combined with the data decomposition method, four kinds of neural network models, i.e., back propagation (BP), Elman, general regression neural network (GRNN), and adaptive neural fuzzy inference system (ANFIS) were employed to carry out a six-step prediction of the mean wind speed at the full-scale measurement site under influences of Typhoon Talim and Kong-rey. The result shows the EMMD or WT-based neural networks improve the prediction accuracy by more than 50% compared to the direct neural network method without data decomposition. It is found that the combination of wavelet decomposition and BP neural network can achieve the best prediction accuracy of typhoon wind speed.