In The Mesoscale Convective System Tracking And Prediction Of Multiple Clouds

Mesoscale Convective Systems (MCS) are main cause of formation for heavy rainfall and severe convective weather. Nowadays, the research on its development and evolution is an important topic for meteorological theory and operation. The cross correlation tracking technique, which is proposed in20th century70s, is still using by major meteorological agency for automatic tracking and forecasting of MCS. With the rapid development of satellite remote sensing technology, satellite data obtained by professional meteorologists is becoming increasingly detailed and comprehensive. And also with the increasingly powerful computer hardware, it is possible that more intelligent and accurate algorithms for automatic tracking and forecasting appear.Firstly, the existing algorithms of satellite meteorology are analyzed. Secondly, the theories and methods of moving object tracking and forecasting for pattern recognition field are comprehensively analyzed. Finally, on the basis of project requirements and magnanimous experiment analysis, a new automatic MCS tracking and forecasting algorithm based on optical flow algorithm and Kalman filter theory is proposed. A pyramid model was used in the computing process of optical flow algorithm, which greatly saved computing time to meet the requirements of the project. A tracking system based on uniform sampling is also designed by making full use of the cloud track motion field computed by optical flow method. By introducing discrete Kalman filter into the tracking system, a good result of forecasting the next state of MCS could be got, which achieved a short-term forecasting of satellite cloud images. By analysis and comparison with experimental results of the traditional meteorological method, it is proved that the tracking and forecasting experimental algorithm for MCS studied by this paper has a more accurate forecasting result.