3D STORM AUTOMATIC IDENTIFICATION BASED ON MATHEMATICAL MORPHOLOGY

The storm identification, tracking and forecasting method is one of the important nowcasting techniques and accurate storm identification is the prerequisite of successful storm tracking and forecasting. Storm identification faces two difficulties: one is false merger and the other is to isolate adjacent storms in a cluster of storms. The TITAN（Thunderstorm Identification, Tracking, Analysis, and Nowcasting） algorithm is apt to identify adjacent storm cells as one storm because it uses a single reflectivity threshold. The SCIT（Storm Cell Identification and Tracking） algorithm uses 7 reflectivity thresholds and therefore is capable of isolating adjacent storm cells, but it discards the results identified by the lower threshold, leading to the loss of the internal structure information of storms. This strategy of SCIT may erroneously miss initiating storms with low reflectivity. Both the TITAN and SCIT have the problem of failing to satisfactorily identify false merger. To overcome these shortcomings, this paper proposes a novel approach based on mathematical morphology. The approach first applies the single threshold identification followed by implementing a special erosion process using dynamic convolution mask to resolve false merger problem. During multi-threshold identification stages, dilation operation is performed against the storm cells which are just obtained by the higher threshold identification, until the storm edges touch each other or touch the edges of the previous storms identified by the lower threshold. The results of experiment show that by combining the strengths of the dilation and erosion operation, this approach is able to successfully recognize false merger as well as to keep the internal structure of sub-storms when isolating storms from a cluster of storms.