Multiresolution Data Assimilation for Auroral Energy Flux and Mean Energy Using DMSP SSUSI, THEMIS ASI, and An Empirical Model

Abstract

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.

Understanding Strong Neutral Vertical Winds and Ionospheric Responses to the 2015 St. Patrick's Day Storm Using TIEGCM Driven by Data‐Assimilated Aurora and Electric Fields

“…As shown in Figure 9 of Wu et al. (2022), the Lattice Kriging model can coherently combine the three data sets (empirical aurora model, SSUSI, and THEMIS/ASIs) with a smooth boundary transition and largely keep mesoscale features such as aurora arcs shown in the observations. The auroral image form Yakutat, Alaska (57°–61°N, 130°–140°W), is padded to a box of (65°–71°N, 140°–160°W) near Poker Flat, partially guided by the raw aurora images which illustrate that the temporal variations of aurora at these two locations are decently correlated (Figure 4).…”

“…The Lattice Kriging modeling has been recently adopted for the data assimilation of aurora (Wu et al., 2022) and extended for the assimilation of electric fields (Wu & Lu, 2022). It has been shown to largely capture the temporal and spatial variability of real data.…”

“…The detailed mathematical formulation and principles can be found in Wu and Lu (2022) and Wu et al. (2022).…”

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

“…Furthermore, empirical ionospheric models, such as the International Reference Ionosphere (IRI, Bilitza, 2001; Bilitza et al., 2017) and the NeQuick model (Nava et al., 2008; Radicella, 2009), have also been extensively employed in the development of ionospheric data assimilation systems. Examples include the Ionospheric Data Assimilation Three/Four‐Dimensional (IDA3D/4D) (Bust et al., 2004, 2007), the Global Ionospheric Specification (GIS) (Lin et al., 2015, 2017), the United States/North American TEC (Fuller‐Rowell et al., 2006; Spencer et al., 2004), the Multi‐Instrument Data Analysis System (MIDAS) (Mitchell & Spencer, 2003; Spencer & Mitchell, 2007), as well as various global/regional ionospheric data assimilation systems driven by multiple data sources (e.g., Aa et al., 2018, 2022; Forsythe et al., 2020, 2021; Galkin et al., 2012; Mengist et al., 2019; Reid et al., 2023; Ssessanga et al., 2019; H. Wu et al., 2022; Yue et al., 2012, 2014). The empirical model‐based data assimilation system has the merits of low computational cost and can be used for ionospheric imaging with potential near‐real‐time capabilities, although it could be somewhat limited by the issue of under‐performance in regions with scarce data.…”