Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter

Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of rad...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lili Lei, Jeffrey S. Whitaker, Jeffrey L. Anderson, Zhemin Tan
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2020
Materias:	radiance observation ensemble Kalman filter adaptive localization Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/f9972f6990d14417a8571945b741d187
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f9972f6990d14417a8571945b741d187
record_format	dspace
spelling	oai:doaj.org-article:f9972f6990d14417a8571945b741d1872021-11-15T14:20:27ZAdaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter1942-246610.1029/2019MS001693https://doaj.org/article/f9972f6990d14417a8571945b741d1872020-08-01T00:00:00Zhttps://doi.org/10.1029/2019MS001693https://doaj.org/toc/1942-2466Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. An adaptive method is proposed to estimate an effective vertical localization independently for each assimilated channel of every satellite platform. It uses sample correlations between ensemble priors of observations and state variables from a cycling data assimilation to estimate the localization function that minimizes the sampling error. The estimated localization functions are approximated by three localization parameters: the localization width, maximum value, and vertical location of the radiance observations. Adaptively estimated localization parameters are used in assimilation experiments with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the National Oceanic and Atmospheric Administration (NOAA) operational ensemble Kalman filter (EnKF). Results show that using the adaptive localization width and vertical location for radiance observations is more beneficial than also including the maximum localization value. The experiment using the adaptively estimated localization width and vertical location performs better than the default Gaspari and Cohn (GC) experiment, and produces similar errors to the optimal GC experiment. The adaptive localization parameters can be computed during the assimilation procedure, so the computational cost needed to tune the optimal GC localization width is saved.Lili LeiJeffrey S. WhitakerJeffrey L. AndersonZhemin TanAmerican Geophysical Union (AGU)articleradiance observationensemble Kalman filteradaptive localizationPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 12, Iss 8, Pp n/a-n/a (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiance observation ensemble Kalman filter adaptive localization Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	radiance observation ensemble Kalman filter adaptive localization Physical geography GB3-5030 Oceanography GC1-1581 Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
description	Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results in complexities when localizing the impact of radiance observations. An adaptive method is proposed to estimate an effective vertical localization independently for each assimilated channel of every satellite platform. It uses sample correlations between ensemble priors of observations and state variables from a cycling data assimilation to estimate the localization function that minimizes the sampling error. The estimated localization functions are approximated by three localization parameters: the localization width, maximum value, and vertical location of the radiance observations. Adaptively estimated localization parameters are used in assimilation experiments with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the National Oceanic and Atmospheric Administration (NOAA) operational ensemble Kalman filter (EnKF). Results show that using the adaptive localization width and vertical location for radiance observations is more beneficial than also including the maximum localization value. The experiment using the adaptively estimated localization width and vertical location performs better than the default Gaspari and Cohn (GC) experiment, and produces similar errors to the optimal GC experiment. The adaptive localization parameters can be computed during the assimilation procedure, so the computational cost needed to tune the optimal GC localization width is saved.
format	article
author	Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan
author_facet	Lili Lei Jeffrey S. Whitaker Jeffrey L. Anderson Zhemin Tan
author_sort	Lili Lei
title	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
title_short	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
title_full	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
title_fullStr	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
title_full_unstemmed	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
title_sort	adaptive localization for satellite radiance observations in an ensemble kalman filter
publisher	American Geophysical Union (AGU)
publishDate	2020
url	https://doaj.org/article/f9972f6990d14417a8571945b741d187
work_keys_str_mv	AT lililei adaptivelocalizationforsatelliteradianceobservationsinanensemblekalmanfilter AT jeffreyswhitaker adaptivelocalizationforsatelliteradianceobservationsinanensemblekalmanfilter AT jeffreylanderson adaptivelocalizationforsatelliteradianceobservationsinanensemblekalmanfilter AT zhemintan adaptivelocalizationforsatelliteradianceobservationsinanensemblekalmanfilter
_version_	1718428412268249088

Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter

Ejemplares similares