3-D Storm Automatic Identification Based on Mathematical Morphology

The strom identification, tracking, and forecasting method is one of the important nowcasting techniques. Accurate storm identification is a prerequisite for successful storm tracking and forecasting. Storm identification faces two difficulties： one is false merger and the other is failure to isolate adjacent storms within 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 seven 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. Both 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 an erosion process to mitigate the 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 operations, this approach is able to mitigate the false merger problem as well as maintain the internal structure of sub-storms when isolating storms within a cluster of storms.

Automatic Identification of Storm Cells Using Doppler Radars

Three storm automatic identification algorithms for Doppler radar are discussed. The WSR-88D Build 7.0 （B7SI） tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build 3D storms, and when storms are merging, splitting, or clustered closely, the detection errors become larger. The B9SI algorithm is part of the Build 9.0 Radar Products Generator of the WSR-88D system. It uses multiple thresholds of reflectivity, newly designs the techniques of cell nucleus extraction and closestorms processing, and therefore is capable of identifying embedded cells in multi-cellular storms. The strong area components at a long distance are saved as 2D storms. However, the B9SI cannot give information on the convection strength of storm, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. To overcome this limitation, the CSI （Convective Storm Identification） algorithm is designed in this paper. By using the fuzzy logic technique, and under the condition that the levels of the seven reflectivity thresholds of B9SI are lowered, the CSI processes the radar base data and the output of B9SI to obtain the convection index of storm. Finally, the CSI is verified with the case of a supercell occurring in Guangzhou on 11 August 2004. The computational and analysis results show that the two rises of convection index matched well with a merging growth and strong convergent growth of the supercell, and the index was 0.744 when the supercell was the strongest, and then decreased. Correspondingly, the height of the maximum reflectivity, detected by the radar also reduced, and heavy rain also occurred in a large-scale area.