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首页> 外文期刊>Atmospheric Measurement Techniques >Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance
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Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance

机译：使用非平稳背景模型误差协方差的无线电掩星和地面GPS数据的电离层同化

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摘要

Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations. We present a data assimilation model for the ionosphere, based on the Gauss-Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model, to assimilate two different types of slant total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. The objective of this study is to investigate impacts of stationary (location-independent) and non-stationary (location-dependent) classes of the background model error covariance on the quality of assimilation analyses. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing system simulation experiments suggest that assimilation of slant TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. Our new ionospheric data assimilation model that employs the location-dependent background model error covariance outperforms the earlier assimilation model with the location-independent background model error covariance, and can reconstruct the 3-D ionospheric electron density distribution satisfactorily from both ground-and space-based GPS observations.
机译：电离层数据同化是一种从各种类型的观测值重建电离层电子密度的3-D分布的有效方法。我们以国际参考电离层（IRI）为背景模型，基于高斯-马尔可夫卡尔曼滤波器，为电离层提供了一个数据同化模型，以同化来自地面GPS的两种不同类型的倾斜总电子含量（TEC）观测值天基FORMOSAT-3 / COSMIC（F3 / C）无线电掩星。背景模型误差和观测误差的协方差模型在数据同化中起重要作用。这项研究的目的是调查背景模型误差协方差的平稳（与位置无关）和非平稳（与位置无关）类别对同化分析质量的影响。位置相关的相关性是使用从IRI输出的整体计算出的经验正交函数来建模的，而位置无关的相关性是使用高斯函数进行建模的。观测系统仿真实验表明，由位置相关的背景模型误差协方差促进的倾斜TEC数据的同化可产生更高质量的同化分析。美国大陆上真实地面GPS和F3 / C无线电掩星观测的同化结果显示为TEC和电子密度剖面。还介绍了使用Millstone Hill非相干散射雷达数据进行的验证以及与Abel反演结果的比较。我们新的电离层数据同化模型采用了位置相关的背景模型误差协方差，优于早期的同化模型与位置无关的背景模型误差协方差，并且可以从地面和空间条件下令人满意地重建3D电离层电子密度分布。基于GPS的观测。

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