The background covariance matrix establishes the transition from the data‐ to model‐driven regions in the ionospheric data assimilation algorithms. To construct the background covariance matrix, the information about the spatial ionospheric correlations of model errors is required. This paper focuses on the horizontal component of the covariance matrix. It is the first study that presents global maps of zonal and meridional ionospheric correlation lengths derived for IRI‐2016 model errors. The model errors were calculated using 20 years of GPS total electron content (TEC) values from the Jet Propulsion Laboratory Global Ionospheric Maps (GIMs) for different seasons, geomagnetic conditions, and universal times. The correlation lengths derived from IRI model errors were analyzed and compared to correlation lengths derived from day‐to‐day ionospheric variability calculated from GIM. It was found that the global distributions of the zonal and meridional correlation lengths between the two approaches are very different and that the correlation lengths derived from day‐to‐day TEC variability cannot be used as a proxy for the construction of covariance matrix for ionospheric data assimilation. A new method is proposed for the modeling of azimuthal distribution of the correlation distances that considers the nonisotropic nature of the distribution of correlations around the reference point.

中文翻译：

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电离层水平相关距离：估计，分析和对电离层数据同化的影响

背景协方差矩阵建立了电离层数据同化算法中从数据驱动区域到模型驱动区域的过渡。为了构造背景协方差矩阵，需要有关模型误差的空间电离层相关性的信息。本文关注协方差矩阵的水平分量。这是第一项提出针对IRI-2016模型误差得出的纬向和经向电离层相关长度全球图的研究。使用来自喷气推进实验室全球电离层图（GIM）的20年GPS总电子含量（TEC）值计算不同季节，地磁条件和世界时间的模型误差。分析了从IRI模型误差得出的相关长度，并将其与从GIM计算得出的日常电离层变异性得出的相关长度进行了比较。发现两种方法之间纬向和经向相关长度的整体分布非常不同，并且从日常TEC变异性得出的相关长度不能用作构建电离层数据协方差矩阵的代理同化。针对相关距离的方位角分布，提出了一种新方法，该方法考虑了参考点周围相关性分布的非各向同性性质。发现两种方法之间纬向和经向相关长度的整体分布非常不同，并且从日常TEC变异性得出的相关长度不能用作构建电离层数据协方差矩阵的代理同化。针对相关距离的方位角分布，提出了一种新方法，该方法考虑了参考点周围相关性分布的非各向同性性质。发现两种方法之间纬向和经向相关长度的整体分布非常不同，并且从日常TEC变异性得出的相关长度不能用作构建电离层数据协方差矩阵的代理同化。针对相关距离的方位角分布，提出了一种新方法，该方法考虑了参考点周围相关性分布的非各向同性性质。