Short-term automatic forecast algorithm of severe convective cloud identification using FY-2 IR images
FY-2红外云图中强对流云团的短时自动预报算法

The movement of clouds is qualitative analyzed by forecasters with satellite images currently,which is,however,lack of objectivity and quantitativity.In this paper,based on the stationary satellite infrared(IR) channel(10.3—11.3 μm) images of FY-2C and FY-2D with the time resolution of 15 minutes,brightness temperature(BT) and area threshold are selected to identify the severe convective cloud(SCC).We then use the SCC matching algorithm of maximum correlation to track the shorttime automatic prediction of SCC systematically.The experiment results show that the tracking method proposed in this work has higher matching accuracy and efficiency compared with the traditional cross-correlation approach.The cloud center of gravity(CG) extrapolation is markedly superior to the minimum temperature,and the mean temperature,area and roundness all have better indications to the cloud split and merge.Tested by contingency table,the automatic identification and tracking technology has high prediction accuracy and timeliness.In addition,the research of this paper provides a scientific basis for the objective and quantitative application of satellite images to SCC short-time prediction in operation.