Among the many reasons for load imbalance in weather forecasting models, the dynamic imbalance caused by localized variations on the state of the atmosphere is the hardest one to handle. As an example, active thunderstorms may substantially increase load at a certain timestep with respect to previous timesteps in an unpredictable manner - after all, tracking storms is one of the reasons for running a weather forecasting model. In this paper, we present a comparative analysis of different load balancing algorithms to deal with this kind of load imbalance. We analyze the impact of these strategies on computation and communication and the effects caused by the frequency at which the load balancer is invoked on execution time. This is done with-out any code modification, employing the concept of processor virtualization, which basically means that the domain is over-decomposed and the unit of rebalance is a sub-domain. With this approach, we were able to reduce the execution time of a full, real-world weather model.