An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation

The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiang Xing, Bainian Liu, Weimin Zhang, Jianping Wu, Xiaoqun Cao, Qunbo Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ensemble data assimilation spurious correlations adaptive covariance localization Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/eea0b9df0d654284901d645c457d31e5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:eea0b9df0d654284901d645c457d31e5
record_format	dspace
spelling	oai:doaj.org-article:eea0b9df0d654284901d645c457d31e52021-11-25T18:03:42ZAn Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation10.3390/jmse91111562077-1312https://doaj.org/article/eea0b9df0d654284901d645c457d31e52021-10-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1156https://doaj.org/toc/2077-1312The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.Xiang XingBainian LiuWeimin ZhangJianping WuXiaoqun CaoQunbo HuangMDPI AGarticleensemble data assimilationspurious correlationsadaptive covariance localizationNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1156, p 1156 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ensemble data assimilation spurious correlations adaptive covariance localization Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	ensemble data assimilation spurious correlations adaptive covariance localization Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Xiang Xing Bainian Liu Weimin Zhang Jianping Wu Xiaoqun Cao Qunbo Huang An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
description	The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.
format	article
author	Xiang Xing Bainian Liu Weimin Zhang Jianping Wu Xiaoqun Cao Qunbo Huang
author_facet	Xiang Xing Bainian Liu Weimin Zhang Jianping Wu Xiaoqun Cao Qunbo Huang
author_sort	Xiang Xing
title	An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
title_short	An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
title_full	An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
title_fullStr	An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
title_full_unstemmed	An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation
title_sort	investigation of adaptive radius for the covariance localization in ensemble data assimilation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/eea0b9df0d654284901d645c457d31e5
work_keys_str_mv	AT xiangxing aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT bainianliu aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT weiminzhang aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT jianpingwu aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT xiaoquncao aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT qunbohuang aninvestigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT xiangxing investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT bainianliu investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT weiminzhang investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT jianpingwu investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT xiaoquncao investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation AT qunbohuang investigationofadaptiveradiusforthecovariancelocalizationinensembledataassimilation
_version_	1718411716148068352

An Investigation of Adaptive Radius for the Covariance Localization in Ensemble Data Assimilation

Ejemplares similares