Data‐Driven Empirical Conductance Relations During Auroral Precipitation Using Incoherent Scatter Radar and All Sky Imagers

Abstract

AbstractWe 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, https://doi.org/10.1029/2004RS003042), but now incorporating the Fang et al. (2010, https://doi.org/10.1029/2010GL045406) ionization model. The main results of this investigation are the power law equations that describe the median, 90th, and 10th percentile Hall and Pedersen conductance as a function of energy flux and average energy. These power law fits are performed for different auroral morphology including all events, discrete, diffuse, and pulsating auroral events. The median Pedersen conductance is found to be in good agreement with past empirical conductance specifications by Robinson et al. (1987, https://doi.org/10.1029/JA092iA03p02565); however, the median Hall conductance from the PFISR observations is found to be larger than the empirical Hall conductance formulas by Robinson et al. (1987, https://doi.org/10.1029/JA092iA03p02565). Pulsating aurora is found to be the most frequently occurring auroral morphology. Furthermore, pulsating aurora has an important contribution to Hall conductance since it has higher average energies than discrete aurora. The results from this investigation are applicable to space weather models and may enable better agreement between model‐data comparisons.