Morphing Ensemble Kalman Filters

TL;DR

This paper introduces a novel ensemble Kalman filter that incorporates morphing and registration techniques from image processing to better handle nonlinear problems with moving features, such as wildfire interfaces.

Contribution

It develops a morphing ensemble Kalman filter that automatically registers and transforms states, improving data assimilation for problems with dynamic, coherent features.

Findings

Effective handling of nonlinear problems with moving features

Automatic registration requires only gridded data

Operates on a transformed state with registration and residuals

Abstract

A new type of ensemble filter is proposed, which combines an ensemble Kalman filter (EnKF) with the ideas of morphing and registration from image processing. This results in filters suitable for nonlinear problems whose solutions exhibit moving coherent features, such as thin interfaces in wildfire modeling. The ensemble members are represented as the composition of one common state with a spatial transformation, called registration mapping, plus a residual. A fully automatic registration method is used that requires only gridded data, so the features in the model state do not need to be identified by the user. The morphing EnKF operates on a transformed state consisting of the registration mapping and the residual. Essentially, the morphing EnKF uses intermediate states obtained by morphing instead of linear combinations of the states.