A mollified Ensemble Kalman filter

TL;DR

This paper introduces a mollified ensemble Kalman filter that smooths analysis increments to reduce high frequency artifacts, using a hybrid approach inspired by nudging and incremental analysis updates, demonstrated on an extended Lorenz-96 model.

Contribution

It proposes a novel mollified ensemble Kalman filter combining nudging and IAU techniques, with a new slow-fast Lorenz-96 model extension for testing.

Findings

Reduces high frequency adjustment artifacts in data assimilation.

Demonstrates effectiveness on an extended Lorenz-96 model.

Provides a hybrid approach inspired by existing smoothing techniques.

Abstract

It is well recognized that discontinuous analysis increments of sequential data assimilation systems, such as ensemble Kalman filters, might lead to spurious high frequency adjustment processes in the model dynamics. Various methods have been devised to continuously spread out the analysis increments over a fixed time interval centered about analysis time. Among these techniques are nudging and incremental analysis updates (IAU). Here we propose another alternative, which may be viewed as a hybrid of nudging and IAU and which arises naturally from a recently proposed continuous formulation of the ensemble Kalman analysis step. A new slow-fast extension of the popular Lorenz-96 model is introduced to demonstrate the properties of the proposed mollified ensemble Kalman filter.