Particle Routing in Distributed Particle Filters for Large-Scale Spatial Temporal Systems

Particle filters are important techniques to support data assimilation for large-scale spatial temporal simulation systems. Distributed particle filters improve the performance of particle filtering by distributing particles to multiple processing units (PUs). While different resampling algorithms have been developed for distributed particle filters, less research has been conducted to investigate how to route particles among the PUs after resampling in effective and efficient manners. This paper develops particle routing policies in distributed particle filters with both the centralized resampling and the distributed resampling. The developed routing policies are evaluated from the aspects of the communication cost and the data assimilation accuracy based on an application of data assimilation for large-scale wildfire spread simulations.