A Study Of Forecasting Convective Initiation And Statistical Evaluation Based On Satellite Data

Convective weather is one of the severe weathers in China and often poses serious threats on the production and life of human beings. Recently, it became a hot topic on monitoring and forecasting convective initiation(CI). CI is defined as the first occurrence of a≥35dBZ radar echo from a cumuliform cloud. CI is the beginning symbol of the strong convection weather event. Thus, the accurate forecasting of CI is important for convective weather warning. This study mainly contains two parts.The first is the CI forecasting algorithm, which is as follows: (1)Fusing visible imagery with high spatial resolution and rich texture detail to infrared imagery, based on the theory of CL multiple wavelet fusion, which can improve the spatial resolution of infrared imagery, and increase the physical information. (2)Tracking the moving trend of the same pixel in different satellite imagery of 15-min time period by the cross correlation algorithm. The goal is to assess the temporal trend in cloud-top temperature. (3)Eight predictors are used to forecast CI which include IR cloud-top brightness temperatures, IR multispectral channel differences, and IR cloud-top temperature/multispectral temporal trend. Cumulus cloud pixels for which≥7 of the 8 CI indicators are satisfied are labeled as having high CI potential. The result shows that CI may be forecasted 30-45 min in advance through this method.The second is a statistical evaluation of forecasting CI. Four measures of foresting skill which are probability of detection(POD), false-alarm ratio(FAR), threat score(TS), and Heidke skill score(HSS), and principal component analysis(PCA) are used to evaluate the CI forecast products. The goal of the statistical analysis is to confirm the accuracy of the algorithm, and that whether each CI indicator is important to forecast CI. The results show that it is reliable to use this method.