A hybrid grid/particle filter for Lagrangian data assimilation. II: Application to a model vortex flow

摘要

We apply our hybrid filter to a regularised vortex model of a co-rotating vortex pair. To illustrate the main advantages of our formulation over existing filters, we compare our method to the perturbed observation Ensemble Kalman filter and a particlefilter with Gaussian resampling. Our numerical simulations show that both the hybrid and particle filters can track the true vortex positions even when tracer position data is assimilated infrequently into our model. In contrast, the Ensemble Kalman filter diverges in this parameter range as was recently observed in the more realistic shallow-water model simulations of Salman et al. We have found that our hybrid method can track the true system with as few as 20 members for the vortex model flow. The particle filter on the other hand requires an ensemble comprising in excess of 160 members. The hybrid filter, therefore, provides one solution to the filter divergence problem that has been identified in recent work on Lagrangian data assimilation.