Wavelet Ensemble Kalman Filters

TL;DR

This paper introduces a wavelet-based ensemble Kalman filter that employs diagonal covariance approximation in wavelet space for adaptive localization, improving data assimilation efficiency in spatial models.

Contribution

The paper proposes a novel wavelet ensemble Kalman filter with diagonal covariance approximation for adaptive localization, enhancing data assimilation methods.

Findings

Demonstrated efficiency on a spatial model example.

Achieved adaptive localization through wavelet space covariance approximation.

Improved data assimilation performance with the new method.

Abstract

We present a new type of the EnKF for data assimilation in spatial models that uses diagonal approximation of the state covariance in the wavelet space to achieve adaptive localization. The efficiency of the new method is demonstrated on an example.