A Parallel Implementation of the Ensemble Kalman Filter Based on Modified Cholesky Decomposition

TL;DR

This paper presents a highly efficient parallel implementation of the ensemble Kalman filter using modified Cholesky decomposition, enabling large-scale data assimilation with significant speedup and improved accuracy.

Contribution

The paper introduces a novel parallel approach for the ensemble Kalman filter based on modified Cholesky decomposition, reducing computational time and enhancing accuracy in large-scale models.

Findings

Outperforms local ensemble transform Kalman filter (LETKF) in accuracy.

Achieves up to 400 times speedup over serial implementation.

Maintains similar computational time to parallel LETKF without covariance estimation.

Abstract

This paper discusses an efficient parallel implementation of the ensemble Kalman filter based on the modified Cholesky decomposition. The proposed implementation starts with decomposing the domain into sub-domains. In each sub-domain a sparse estimation of the inverse background error covariance matrix is computed via a modified Cholesky decomposition; the estimates are computed concurrently on separate processors. The sparsity of this estimator is dictated by the conditional independence of model components for some radius of influence. Then, the assimilation step is carried out in parallel without the need of inter-processor communication. Once the local analysis states are computed, the analysis sub-domains are mapped back onto the global domain to obtain the analysis ensemble. Computational experiments are performed using the Atmospheric General Circulation Model (SPEEDY) with the T-63 resolution on the Blueridge cluster at Virginia Tech. The number of processors used in the experiments ranges from 96 to 2,048. The proposed implementation outperforms in terms of accuracy the well-known local ensemble transform Kalman filter (LETKF) for all the model variables. The computational time of the proposed implementation is similar to that of the parallel LETKF method (where no covariance estimation is performed). Finally, for the largest number of processors, the proposed parallel implementation is 400 times faster than the serial version of the proposed method.