期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2020
卷号：12
期号：8
页码：1-15
DOI：10.1029/2019MS001693
出版社：John Wiley & Sons, Ltd.
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. Plain Language Abstract Assimilation of satellite radiance observations is essential for numerical weather predictions, especially where conventional observations are limited. Satellite radiances effectively measure integrated quantities over an atmospheric column, but it is not straightforward to define the vertical location of such a nonlocal observation. This results in complexities for localizing the impact of radiance observations; however, localization is crucial to effectively assimilate satellite radiances. This study investigated an adaptive localization approach to estimate an effective vertical localization independently for each assimilated channel of every satellite platform. Three localization parameters, including the localization width, maximum value, and vertical location of the radiance observations, were examined. It is demonstrated that using the adaptive localization width and vertical location for radiance observations is more beneficial than also including the maximum localization value. The adaptive localization method performs similarly to the optimal Gaspri and Cohn (GC) function, but the adaptive localization has significant computational cost advantages because it does not require intensive tuning of the localization width like the GC localization function.
