The use of semi-Lagrangian formulations in numerical weather predication models (NWP) allows for an increase in time step size. Use of this method can increase performance of these models. However, on parallel architectures, communication between processors can become a huge bottleneck, limiting speedup. Furthermore, the communication pattern is dependent on the application's execution. We discus a novel strategy, called Halo on Demand, which dynamically drives the communication between the processors by examining the content of the data at runtime in order to reduce communication costs. With an extensive performance analysis of the execution of the model we show that our strategy can decrease communication time and thus decrease total execution time.