An Improved Storm Cell Identification and Tracking (SCIT) Algorithm based on DBSCAN Clustering and JPDA Tracking Methods

Accurate storm cell identification and tracking is both a vital and challenging endeavor in severe weather operations. Correct identification and tracking of storms is a significant component of studying any type of meteorological phenomena, including lightning. A high accuracy method of identifying, differentiating, and tracking storm cells is proposed. The results of the proposed Storm Cell and Tracking (SCIT) algorithm are applied to a new lighting association algorithm for examining lightning trends in various storm cells. The new cell identification method utilizes a density-based unsupervised clustering algorithm which requires no a priori knowledge of the number of existing cells. Storm cells are identified and stored according to the entire area of the storm cell, which is contrary to the current method of maintaining just a centroid point. Knowing nearly the exact region a storm cell occupies is very advantageous in associating meteorological phenomena such as lightning with the appropriate storm cell. As a result, studies on spatial lightning correlation can be performed with a much greater accuracy. In addition to improved storm cell identification, a superior tracking and association algorithm is presented. As previously mentioned, storm cell areas are determined and tracked rather than centroid locations. A scheme of joint probabilistic data association (JPDA) problems is formed to associate storm cells. A traditional combinatorial optimization algorithm is performed on a particle representation of storm cells. This, in turn, produces a cost matrix which reflects the overall probability of assignment between storms. Lastly, two iterations of a modified Hungarian Algorithm, capable of making assignments that reflect splitting and merging cells, produce the final storm cell associations. Overall, storm cells are identified and tracked with a much higher degree of fidelity that the currently implemented SCIT algorithm. Lightning association is then performed by ensuring that each detected lightning strike is located within a storm cell’s identified area. This is much more accurate than the current method of associating each lightning strike with the closest storm cell centroid, especially for irregularly shaped and closely spaced storms. The result is much more accurate lightning association and tracking of lightning trends.