Thunderstorm Potential Prediction Based on Back Propagation Neural Network
Chen Yongwe · Journal of Arid Meteorology · 2013
In order to use neural networks to solve common nonlinear problem in lightning potential trend prediction,the correlation coefficients were calculated between forty-six convective parameters and thunderstorms occurring from June to August in 2008 in Nanjing.In these convective parameters,seven convective factors among them had better relationship with thunderstorms occurrence,including TT,SI,SWEAT,Tlfc,CIN,DCI and PW indexes,then these seven convective parameters were selected as the input factors of the neural network model which contained seven input layers,twelve hidden layers and one output layer.On the basis of back propagation neural network model built by the data of 2008,the thunderstorm potential trend from June to August in 2009 in Nanjing were predicted including the thunderstorm days and non-thunderstorm days.According to the score standard,the POD,FAR,CSI,PDFD and FOM of the model were 74.5%,9.5%,74.5% 2.9% and 19.1%,respectively,which indicated that this back propagation neural network model had better forecast accuracy and its performance was steady,it can be well applied in thunderstorm potential trend prediction.