Title: Projected Data Assimilation.
Abstract: 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 PF-AUS, a new implementation of Assimilation in the Unstable Subspace (AUS) for Particle Filters. The PF-AUS implementation assimilates highly informative but low-dimensional observations. 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 numerically implement PF-AUS with the optimal proposal and compare to the standard optimal proposal and to the Local Ensemble Transform Kalman Filter.