We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,
We present empirical conductance relations that are derived from incoherent scatter radar observations and correlated with all sky imager observations to identify the morphology of the aurora. We use 75,461 events collected using the Poker Flat Incoherent Scatter Radar (PFISR) with associated all sky imagers observations spanning the years 2012–2016. In addition to classifying these events based on auroral morphology, we estimated the Hall and Pedersen conductance and the differential number flux from which the energy flux and the average energy can be calculated. The differential number flux was estimated using the maximum entropy inversion method described in Semeter and Kamalabadi (2005,
- NSF-PAR ID:
- 10465319
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 128
- Issue:
- 9
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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