Efficient Formulation and Implementation of Data Assimilation Methods

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 approachi...

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Main Authors:	Elias D. Nino-Ruiz, Adrian Sandu, Haiyan Cheng
Format:	Article
Language:	English
Published:	MDPI AG 2018-07-01
Series:	Atmosphere
Subjects:	ensemble Kalman filter posterior ensemble modified Cholesky decomposition sampling methods empirical orthogonal functions Gaussian mixture models
Online Access:	http://www.mdpi.com/2073-4433/9/7/254

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spelling	doaj-ef96b2cf0ac04acfb5e11031d943071c2020-11-24T21:58:29ZengMDPI AGAtmosphere2073-44332018-07-019725410.3390/atmos9070254atmos9070254Efficient Formulation and Implementation of Data Assimilation MethodsElias D. Nino-Ruiz0Adrian Sandu1Haiyan Cheng2Applied Math and Computational Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, ColombiaDepartment of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USADepartment of Computer Science, Willamette University, 900 State Street, Salem, OR 97301, USAThis 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.http://www.mdpi.com/2073-4433/9/7/254ensemble Kalman filterposterior ensemblemodified Cholesky decompositionsampling methodsempirical orthogonal functionsGaussian mixture models
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Elias D. Nino-Ruiz Adrian Sandu Haiyan Cheng
spellingShingle	Elias D. Nino-Ruiz Adrian Sandu Haiyan Cheng Efficient Formulation and Implementation of Data Assimilation Methods Atmosphere ensemble Kalman filter posterior ensemble modified Cholesky decomposition sampling methods empirical orthogonal functions Gaussian mixture models
author_facet	Elias D. Nino-Ruiz Adrian Sandu Haiyan Cheng
author_sort	Elias D. Nino-Ruiz
title	Efficient Formulation and Implementation of Data Assimilation Methods
title_short	Efficient Formulation and Implementation of Data Assimilation Methods
title_full	Efficient Formulation and Implementation of Data Assimilation Methods
title_fullStr	Efficient Formulation and Implementation of Data Assimilation Methods
title_full_unstemmed	Efficient Formulation and Implementation of Data Assimilation Methods
title_sort	efficient formulation and implementation of data assimilation methods
publisher	MDPI AG
series	Atmosphere
issn	2073-4433
publishDate	2018-07-01
description	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.
topic	ensemble Kalman filter posterior ensemble modified Cholesky decomposition sampling methods empirical orthogonal functions Gaussian mixture models
url	http://www.mdpi.com/2073-4433/9/7/254
work_keys_str_mv	AT eliasdninoruiz efficientformulationandimplementationofdataassimilationmethods AT adriansandu efficientformulationandimplementationofdataassimilationmethods AT haiyancheng efficientformulationandimplementationofdataassimilationmethods
_version_	1725851724636225536

Efficient Formulation and Implementation of Data Assimilation Methods

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