3D Convective Storm Identification, Tracking, and Forecasting—An Enhanced TITAN Algorithm

Abstract

Abstract Storm identification, tracking, and forecasting make up an essential part of weather radar and severe weather surveillance operations. Existing nowcasting algorithms using radar data can be generally classified into two categories: centroid and cross-correlation tracking. Thunderstorm Identification, Tracking, and Nowcasting (TITAN) is a widely used centroid-type nowcasting algorithm based on this paradigm. The TITAN algorithm can effectively identify, track, and forecast individual convective storm cells, but TITAN tends to provide incorrect identification, tracking, and forecasting in cases where there are dense cells whose shape changes rapidly or where clusters of storm cells occur frequently. Aiming to improve the performance of TITAN in such scenarios, an enhanced TITAN (ETITAN) algorithm is presented. The ETITAN algorithm provides enhancements to the original TITAN algorithm in three aspects. First, in order to handle the false merger problem when two storm cells are adjacent, and to isolate individual storm cells from a cluster of storms, ETITAN uses a multithreshold identification method based on mathematical morphology. Second, in the tracking phase, ETITAN proposes a dynamic constraint-based combinatorial optimization method to track storms. Finally, ETITAN uses the motion vector field calculated by the cross-correlation method to forecast the position of the individual isolated storm cells. Thus, ETITAN combines aspects of the two general classes of nowcasting algorithms, that is, cross-correlation and centroid-type methods, to improve nowcasting performance. Results of experiments presented in this paper show the performance improvements of the ETITAN algorithm.