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A Parallel Implementation of the Ensemble Kalman Filter Based on Modified Cholesky Decomposition by Nino, Elias D.; Sandu, Adrian; Deng, Xinwei is a scholarly article available to read on EtoBox.

What is A Parallel Implementation of the Ensemble Kalman Filter Based on Modified Cholesky Decomposition about?

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

Author

Nino, Elias D.; Sandu, Adrian; Deng, Xinwei

Published

2016

Language

EN