Data Assimilation of High‐Latitude Electric Fields: Extension of a Multi‐Resolution Gaussian Process Model (Lattice Kriging) to Vector Fields. Issue 1 (18th January 2022)

• Record Type: Journal Article
• Authors: Wu, Haonan; Lu, Xian
• Is Part Of: Space weather. Volume 20:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-18
• Subjects: data assimilation -- multi‐resolution -- electric fields -- Gaussian process model -- SuperDARN -- PFISR; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2021SW002880
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: We develop a new methodology for the multi‐resolution assimilation of electric fields by extending a Gaussian process model (Lattice Kriging) used for scalar field originally to vector field. This method takes the background empirical model as "a priori" knowledge and fuses real observations under the Gaussian process framework. The comparison of assimilated results under two different background models and three different resolutions suggests that (a) the new method significantly reduces fitting errors compared with the global spherical harmonic fitting (SHF) because it uses range‐limited basis functions ideal for the local fitting and (b) the fitting resolution, determined by the number of basis functions, is adjustable and higher resolution leads to smaller errors, indicating that more structures in the data are captured. We also test the sensitivity of the fitting results to the total amount of input data: (a) as the data amount increases, the fitting results deviate from the background model and become more determined by data and (b) the impacts of data can reach remote regions with no data available. The assimilation also better captures short‐period variations in local PFISR measurements than the SHF and maintains a coherent pattern with the surrounding. The multi‐resolution Lattice Kriging is examined via attributing basis functions into multiple levels with different resolutions (fine level is located in the region with observations). Such multi‐resolution fitting has the smallest error and shortest computation time, making the regional high‐resolution modeling efficient. Our method can be modified to achieve the multi‐resolution assimilation for other vector fields from unevenly distributed observations. Plain Language Summary: Earth's upper atmosphere is vulnerable to energy injection from the Sun via the channel of solar wind‐magnetosphere‐ionosphere‐thermosphere coupling. Geomagnetic storms induce dramatic changes in ion motions, electron densities, and composition, which further drive neutral atmosphere into a highly disturbed condition. Such disturbances in neutrals, ions, and electrons lead to an important component of space weather, affecting satellite drag, ionospheric scintillation, radio propagation, and GPS navigation. It is therefore critical to better understand space weather and underlying processes. The high‐latitude electric field, largely imposed from the magnetosphere, controls electrodynamic and dynamic processes in the ionosphere‐thermosphere. It is also an important driver for numerical models to simulate and predict space weather. Empirical models of electric field are unrealistically smooth and miss localized feature. Data assimilation technique helps to fuse data information and solves this issue. Previous techniques rely on the global harmonics fitting, which is not friendly in local fitting. In this paper, we introduce a Gaussian process model (Lattice Kriging) and propose a new methodology based on its extension to vector fields. This new method is robust and significantly decreases fitting errors, providing a useful tool for the research aiming to perform regional high‐resolution and multi‐resolution data assimilation upon unevenly distributed observations. Key Points: The multi‐resolution Gaussian process model has been successfully adapted to assimilate electric fields from line‐of‐sight measurements This method captures finer scale variations in regional data than background models and decreases fitting errors with increasing resolutions The multi‐level basis functions enable the regional fine‐scale modeling and achieve the best fitting performance with reduced computation