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.

中文翻译：

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集合卡尔曼滤波器中卫星辐射观测的自适应定位

本地化对于有效吸收集合卡尔曼滤波器中的卫星辐射至关重要。但是，对于辐射度观测的垂直位置和与模型网格点变量的距离并没有很好地定义，这在确定辐射度观测的影响时会导致复杂性。提出了一种自适应方法来估计每个卫星平台的每个同化信道的有效垂直定位。它使用观测先验集合与循环数据同化中的状态变量之间的样本相关性来估计定位函数，以最大程度地减少采样误差。估计的定位函数由三个定位参数近似：辐射观测的定位宽度，最大值和垂直位置。自适应估计的定位参数用于与国家环境预测中心（NCEP）全球预报系统（GFS）模型和国家海洋与大气管理局（NOAA）操作集合卡尔曼滤波器（EnKF）的同化实验中。结果表明，使用自适应定位宽度和垂直位置进行辐射观测比包含最大定位值更有利。使用自适应估计的定位宽度和垂直位置进行的实验要比默认的Gaspari和Cohn（GC）实验更好，并且会产生与最佳GC实验相似的误差。可以在同化过程中计算自适应定位参数，因此节省了调整最佳GC定位宽度所需的计算成本。