气候变化领域集成服务门户(CCPortal): Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter

CCPortal

DOI	10.1029/2019MS001693
	Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter
	Lei L.; Whitaker J.S.; Anderson J.L.; Tan Z.
发表日期	2020
ISSN	19422466
卷号	12 期号:8
英文摘要	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. © 2020 The Authors.
英文关键词	adaptive localization; ensemble Kalman filter; radiance observation
语种	英语
scopus关键词	Location; Parameter estimation; Satellites; Tracking (position); Adaptive localizations; Assimilation procedure; Ensemble Kalman Filter; Global forecast systems; Localization functions; Localization parameters; National centers for environmental predictions; National Oceanic and Atmospheric Administration; Kalman filters; error analysis; experimental study; Kalman filter; NOAA satellite; observational method; parameter estimation; prediction; radiance; sampling; satellite data
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156671
作者单位	Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China; School of Atmospheric Sciences, Nanjing University, Nanjing, China; NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, CO, United States; National Center for Atmospheric Research, Boulder, CO, United States
推荐引用方式 GB/T 7714	Lei L.,Whitaker J.S.,Anderson J.L.,et al. Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter[J],2020,12(8).
APA	Lei L.,Whitaker J.S.,Anderson J.L.,&Tan Z..(2020).Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter.Journal of Advances in Modeling Earth Systems,12(8).
MLA	Lei L.,et al."Adaptive Localization for Satellite Radiance Observations in an Ensemble Kalman Filter".Journal of Advances in Modeling Earth Systems 12.8(2020).