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Atmosphere 2018, 9(7), 254; https://doi.org/10.3390/atmos9070254

Efficient Formulation and Implementation of Data Assimilation Methods

1
Applied Math and Computational Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, Colombia
2
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA
3
Department of Computer Science, Willamette University, 900 State Street, Salem, OR 97301, USA
*
Author to whom correspondence should be addressed.
Received: 3 July 2018 / Accepted: 4 July 2018 / Published: 6 July 2018
(This article belongs to the Special Issue Efficient Formulation and Implementation of Data Assimilation Methods)
Full-Text   |   PDF [196 KB, uploaded 6 July 2018]

Abstract

This Special Issue presents efficient formulations and implementations of sequential and variational data assimilation methods. The methods address three important issues in the context of operational data assimilation: efficient implementation of localization methods, sampling methods for approaching posterior ensembles under non-linear model errors, and adjoint-free formulations of four dimensional variational methods. View Full-Text
Keywords: ensemble Kalman filter; posterior ensemble; modified Cholesky decomposition; sampling methods; empirical orthogonal functions; Gaussian mixture models ensemble Kalman filter; posterior ensemble; modified Cholesky decomposition; sampling methods; empirical orthogonal functions; Gaussian mixture models
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Nino-Ruiz, E.D.; Sandu, A.; Cheng, H. Efficient Formulation and Implementation of Data Assimilation Methods. Atmosphere 2018, 9, 254.

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