This paper presents a Bayesian model for the multiple target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and targets state. The events include target births, deaths, splits, and merges. The target state contains both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values we can include high level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective systems tracking.