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

## TL;DR

This paper introduces a novel data assimilation framework using low-rank projections, specifically a projected Particle Filter, to improve high-dimensional state estimation by mitigating particle collapse.

## Contribution

The paper develops a projected Particle Filter based on low-rank projections, compatible with any DA method, and demonstrates its effectiveness in high-dimensional problems.

## Key findings

- Projected Particle Filter reduces particle collapse in high dimensions
- The method preserves relevant information from unstable modes
- Numerical comparisons show improved performance over standard filters

## 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 PROJ-PF, a projected Particle Filter. The PROJ-PF implementation assimilates highly informative but low-dimensional observations. The implementation considered here is based upon 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 numerically implement a projected Optimal Proposal Particle Filter (PROJ-OP-PF) and compare to the standard optimal proposal and to the Ensemble Transform Kalman Filter.