An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average \(42.46\%\) and \(31.89\%\) root mean squared error (RMSE) reduction during our test period, September 2017.

准确估计电离层变量（例如总电子含量 (TEC)）对于许多空间天气、通信和卫星大地测量应用非常重要。经验和基于物理的模型通常用于确定这些应用中的 TEC。然而，众所周知，由于模型结构简化、输入的粗略采样以及对校准周期的依赖性等各种原因，这些模型无法再现所有电离层变化。基于贝叶斯的数据同化 (DA) 技术通常用于提高这些模型的性能，但它们的计算成本相当大。在这项研究中，首先，我们回顾了可用于高层大气数据同化的 DA 技术。然后，我们将提出一个基于经验分解的数据同化（DDA），基于主成分分析和集成卡尔曼滤波器。DDA 大大降低了以前 DA 实现的计算复杂性。它的性能通过更新经验 NeQuick 的经验正交函数和使用快速全球电离层图 (GIM) TEC 产品作为观察的基于物理的 TIEGCM 模型来证明。分别称为“DDA-NeQuick”和“DDA-TIEGCM”的新模型随后用于预测第二天的 TEC 值。TEC 预测与最终 GIM TEC 产品（11 天后可用）的比较代表平均值 它的性能通过更新经验 NeQuick 的经验正交函数和使用快速全球电离层图 (GIM) TEC 产品作为观察的基于物理的 TIEGCM 模型来证明。分别称为“DDA-NeQuick”和“DDA-TIEGCM”的新模型随后用于预测第二天的 TEC 值。TEC 预测与最终 GIM TEC 产品（11 天后可用）的比较代表平均值 它的性能通过更新经验 NeQuick 的经验正交函数和使用快速全球电离层图 (GIM) TEC 产品作为观察的基于物理的 TIEGCM 模型来证明。分别称为“DDA-NeQuick”和“DDA-TIEGCM”的新模型随后用于预测第二天的 TEC 值。TEC 预测与最终 GIM TEC 产品（11 天后可用）的比较代表平均值\(42.46\%\)和\(31.89\%\)均方根误差 (RMSE) 在 2017 年 9 月测试期间减少。