Assimilative Mapping of Auroral Electron Energy Flux using SSUSI Lyman-Birge-Hopfield (LBH) Emissions Journal Article uri icon

Overview

abstract

  • Far ultraviolet (FUV) imaging of the aurora from space provides great; insight into dynamic coupling of the atmosphere, ionosphere and; magnetosphere on global scales. To gain 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; SSJ and SUSSI instruments. The paper demonstrates the feasibility of; this approach with a model prototype built with DMSP data from February; 17-23 2014. This study serves as a blueprint for a future comprehensive; data-driven modeling 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.

publication date

  • April 13, 2021

has restriction

  • hybrid

Date in CU Experts

  • April 29, 2021 6:02 AM

Full Author List

  • Li J; Matsuo T; Kilcommons L

author count

  • 3

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