Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model
Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model
复制标题
DOI：
10.1029/2022sw003146
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Haonan Wu;Xiyan Tan;Qiong Zhang;Whitney K. Huang;Xian Lu;Y. Nishimura;Yongliang Zhang
中科院分区：
文献类型：
--
作者：
Haonan Wu;Xiyan Tan;Qiong Zhang;Whitney K. Huang;Xian Lu;Y. Nishimura;Yongliang Zhang
本文应用多分辨率高斯过程模型（格点克里格）结合联合收割机卫星观测、地面观测和经验极光模型，对高纬度地区极光能量通量和平均能量进行同化。与简单的填充相比，同化连贯地结合了各种数据输入，从而导致不同数据集之间的连续转换。通过分配具有不同分辨率的多层基函数来实现多分辨率建模能力。高分辨率的拟合结果比低分辨率的拟合结果和经验模型捕捉到更多的中尺度（10-100 s km）结构，如极光弧。为了更好地协调不同的数据集，实施了两个预处理步骤，即卫星数据的时间内插和低保真数据的空间下采样。拟合的固有平滑效果会导致极光的不切实际的扩散，这可以通过后处理步骤来减轻：K最近邻（KNN）算法。KNN识别具有显著极光的区域的概率，从而消除具有低值的那些区域。因此，这种方法可以用来维持真实的中尺度极光结构，而没有边界问题。然后，我们运行由高分辨率和低分辨率极光同化驱动的热层电离层电动力学环流模型（TIEGCM），并比较总电子含量（TEC）。由数据同化驱动的TIEGCM产生的TEC比由经验极光驱动的TEC增强了102倍，高分辨率结果显示了中尺度结构。我们的研究显示了通过同化将现实的极光输入纳入电离层-热层模型以更好地理解中尺度现象的后果的价值。
We apply a multiresolution Gaussian process model (Lattice Kriging) to combine satellite observations, ground‐based observations, and an empirical auroral model, to produce the assimilation of auroral energy flux and mean energy over high‐latitude regions. Compared to a simple padding, the assimilation coherently combines various data inputs leading to continuous transitions between different datasets. The multiresolution modeling capability is achieved by allocating multiple layers of basis functions with different resolutions. Higher‐resolution fitting results capture more mesoscale (10–100 s km) structures such as auroral arcs, than the low‐resolution ones and the empirical model. To better reconcile different datasets, two preprocessing steps, temporal interpolation of satellite data and spatial down‐sampling of low‐fidelity data, are implemented. The inherent smoothing effect of the fitting, which causes an unrealistic spreading of the aurora, is mitigated by a post processing step: the K Nearest Neighbor (KNN) algorithm. KNN identifies the probability of a region with significant aurora and thereby eliminates those regions with low values. Thereby, this methodology can be used to maintain realistic and mesoscale auroral structures without boundary issues. We then run the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) driven by the high‐ and low‐resolution auroral assimilations and compare total electron contents (TECs). TIEGCM driven by data assimilation produces enhanced TECs by a factor of ∼2 than the one driven by the empirical aurora, and high‐resolution results show mesoscale structures. Our study shows the value of incorporating realistic auroral inputs via assimilation to drive ionosphere‐thermosphere models for better understanding the consequences of mesoscale phenomena.