本文提出一种月尺度西北太平洋热带气旋生成频数(Western North Pacific Tropical Cyclone Frequency，WNPTCF)预测的新方法。该方法利用全球次表层海温(Subsurface Sea Temperature Anomaly，SubSSTA)和中国气象局国家气候中心发布的130项监测指数，构建了既考虑热力强迫因子、又考虑大气动力因子，既考虑同期海洋强迫，又考虑前期海洋和大气影响的集成预测模型。利用该预测模型对2011-2020年6-10月逐月WNPTCF进行独立样本检验预测，准确率达70％以上，说明该预测模型对WNPTCF的逐月演变预测的效果良好。该预测模型对ENSO信号较强年份的WNPTCF预测效果要好于ENSO信号不强的年份，原因在于在ENSO信号不强的年份，SubSSTA可预报性较低，非线性变率大，海洋对WNPTC的强迫作用弱。
A new statistical model for predicting month-to-month evolution of tropical cyclones in the western North Pacific
In this paper,a new method for predicting the monthly Western North Pacific Tropical Cyclone Frequency(WNPTCF)was proposed.Based on the global Subsurface Sea Temperature Anomaly(SubSSTA)and 130 monitoring indexes released by the National Climate Center of China Meteorological Administration,this method constructs an integrated statistical prediction model that considers both thermal forcing factor and atmospheric dynamic factor,and both simultaneous oceanic forcing factor and the influence of previous ocean and atmosphere.The independent sample test prediction of monthly WNPTCF from June to October from 2011 to 2020 was carried out by using this prediction model,and the accuracy rate reached more than 70％,indicating that this prediction model has good prediction effect on the monthly evolution of WNPTCF.The prediction effect of this prediction model for WNPTCF in years with strong ENSO signal is better than that in years with weak ENSO signal.The reason is that in years with weak ENSO signal,the predictability of SubSSTA is low,the nonlinear variability is large,and the forcing effect of ocean on WNPTC is weak.
tropical cyclonemonthlyfrequency of formationstatistical prediction
万方数据
维普
