Assimilative Mapping of Auroral Electron Energy Flux Using SSUSI Lyman‐Birge‐Hopfield (LBH) Emissions. Issue 3 (24th March 2022)

• Record Type: Journal Article
• Authors: Li, J.; Matsuo, T.; Kilcommons, L. M.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-24
• Subjects: Magnetospheric physics -- Periodicals; Space environment -- Periodicals; Cosmic physics -- Periodicals; Planets -- Atmospheres -- Periodicals; Heliosphere (Astrophysics) -- Periodicals; Geophysics -- Periodicals; 523.01
• DOI: 10.1029/2021JA029739
• Languages: English
• ISSNs: 2169-9380

Abstract

Abstract: Far ultraviolet (FUV) imaging of the aurora from space provides great insight into the dynamic coupling of the atmosphere, ionosphere, and magnetosphere on global scales. To gain a quantitative understanding of these coupling processes, the global distribution of auroral energy flux is required, but the inversion of FUV emission to derive precipitating auroral particles' energy flux is not straightforward. Furthermore, the spatial coverage of FUV imaging from Low Earth Orbit (LEO) altitudes is often insufficient to achieve global mapping of this important parameter. This study seeks to fill these gaps left by the current geospace observing system using a combination of data assimilation and machine learning techniques. Specifically, this paper presents a new data‐driven modeling approach to create instantaneous, global assimilative mappings of auroral electron total energy flux from Lyman‐Birge‐Hopfield (LBH) emission data from the Defense Meteorological System Program (DMSP) Special Sensor Ultraviolet Spectrographic Imager (SSUSI). We take a two‐step approach; the creation of assimilative maps of LBH emission using optimal interpolation, followed by the conversion to energy flux using a neural network model trained with conjunction observations of in‐situ auroral particles and LBH emission from the DMSP Special Sensor J and SSUSI instruments. The paper demonstrates the feasibility of this approach with a model prototype built with DMSP data from 17 February 2014 to 23 February 2014. This study serves as a blueprint for a future comprehensive data‐driven model of auroral energy flux that is complementary to traditional inversion techniques to take advantage of FUV imaging from LEO platforms for global assimilative mapping of auroral energy flux. Plain Language Summary: When energetic protons and electrons interact with the nitrogen gas molecules of the Earth's atmosphere at high latitudes, light emissions including ultraviolet emissions in the Lyman‐Birge‐Hopfield (LBH) band are created. Our goal is to make global maps of the energy flux of these particles using images of these LBH emissions observed by the Defense Meteorological System Program (DMSP) Low Earth Orbiting (LEO) satellites. This is a challenging task because ultraviolet imagers onboard LEO satellites can only provide partial coverage of the global high‐latitude region and emissions are indirect measurements of the energy flux. To address these problems, we first determine a small set of global patterns from lots of DMSP data that efficiently explain how these LBH emissions vary over time. By using these global patterns, we then make global maps of LBH emissions for a particular time from instantaneous LBH emission observations. We finally relate global LBH emission maps to the energy flux using a neural network model trained with data from another DMSP instrument that measures the energy flux of precipitating particles as well as LBH emission data. Our study serves as a blueprint for a future comprehensive data‐driven modeling of auroral energy flux from ultraviolet imagers onboard LEO satellites. Key Points: An end‐to‐end data‐driven modeling approach for assimilative mapping of auroral electron energy flux using Special Sensor Ultraviolet Spectrographic Imager (SSUSI) Lyman‐Birge‐Hopfield (LBH) emissions is developed A neural network model to predict electron flux from LBH emissions is learned using 1 week of Defense Meteorological System Program F16, F17, and F18 SSUSI and Special Sensor J data A blueprint is presented of a novel approach that combines multiple machine learning methods and is scalable to larger Low Earth Orbit Far ultraviolet imaging data