Particle filters for data assimilation based on reduced-order data models

We introduce a framework for data assimilation (DA) in which the data is split into multiple sets corresponding to low-rank projections of the state space. Algorithms are developed that assimilate some or all of the projected data, including an algorithm compatible with any generic DA method. The major application explored here is PROJ-PF, a projected particle filter. The PROJ-PF implementation assimilates highly informative but low-dimensional observations. The implementation considered here is based on using projections corresponding to assimilation in the unstable subspace (AUS). In the context of particle filtering, the projected approach mitigates the collapse of particle ensembles in high-dimensional DA problems, while preserving as much relevant information as possible, as the unstable and neutral modes correspond to the most uncertain model predictions. In particular, we formulate and implement numerically a projected optimal proposal particle filter (PROJ-OP-PF) and compare this with the standard optimal proposal and the ensemble transform Kalman filter.

Automated summary

This study introduces a framework for data assimilation that divides data into multiple low-rank projections for state space. Algorithms are developed to assimilate these projected data, and the major application explored is PROJ-PF for highly informative but low-dimensional observations. The implementation is based on projections in the unstable subspace, aiming to mitigate the collapse of particle ensembles in high-dimensional DA problems.