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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.
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Upper Atmosphere Radiance Data Assimilation: A Feasibility Study for GOLD Far Ultraviolet Observations
Abstract Far ultraviolet observations of Earth's dayglow from the National Aeronautics and Space Administration (NASA) Global‐scale Observations of the Limb and Disk (GOLD) mission presents an unparalleled opportunity for upper atmosphere radiance data assimilation. Assimilation of the Lyman‐Birge‐Hopfield (LBH) band emissions can be formulated in a similar fashion to lower atmosphere radiance data assimilation approaches. To provide a proof‐of‐concept for such an approach, this paper presents assimilation experiments of simulated LBH emission data using an ensemble filter measurement update step implemented with National Oceanic and Atmospheric Administration (NOAA)'s Whole Atmosphere Model (WAM) and National Center for Atmospheric Research (NCAR)'s Global Airglow (GLOW) model. Primary findings from observing system simulation experiments (OSSEs), wherein “truth” atmospheric conditions simulated by NCAR's Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) are used to generate synthetic GOLD data, are as follows: (1) Assimilation of GOLD LBH disk emission data can reduce the bias in model temperature specification (ensemble mean) by 60% under both geomagnetically quiet conditions and disturbed conditions. (2) The reduction in model uncertainty (ensemble spread) as a result of assimilation is about 20% in the lower thermosphere and 30% in the upper thermosphere for both conditions. These OSSEs demonstrate the potential for far ultraviolet radiance data assimilation to dramatically reduce the model biases in thermospheric temperature specification and to extend the utility of GOLD observations by helping to resolve the altitude‐dependent global‐scale response of the thermosphere to geomagnetic storms.
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- Award ID(s):
- 1848544
- PAR ID:
- 10372438
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 124
- Issue:
- 10
- ISSN:
- 2169-9380
- Format(s):
- Medium: X Size: p. 8154-8164
- Size(s):
- p. 8154-8164
- Sponsoring Org:
- National Science Foundation
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