Mapping high‐latitude ionospheric electrodynamics with SuperDARN and AMPERE

Authors:
Open Access
Publisher website
Google Scholar
Abstract
An assimilative procedure for mapping high‐latitude ionospheric electrodynamics is developed for use with plasma drift observations from the Super Dural Auroral Radar Network (SuperDARN) and magnetic perturbation observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). This procedure incorporates the observations and their errors, as well as two background models and their error covariances (estimated through empirical orthogonal function analysis) to infer complete distributions of electrostatic potential and vector magnetic potential in the high‐latitude ionosphere. The assimilative technique also enables objective error analysis of the results. Various methods of specifying height‐integrated ionospheric conductivity, which is required by the procedure, are implemented and evaluated quantitatively. The benefits of using both SuperDARN and AMPERE data to solve for both electrostatic and vector magnetic potentials, rather than using the data sets independently or solving for just electrostatic potential, are demonstrated. Specifically, solving for vector magnetic potential improves the specification of field‐aligned currents (FACs), and using both data sets together improves the specification of features in regions lacking one type of data (SuperDARN or AMPERE). Additionally, using the data sets together results in a better correspondence between large‐scale features in the electrostatic potential distribution and those in the FAC distribution, as compared to using SuperDARN data alone to infer electrostatic potential and AMPERE data alone to infer FACs. Finally, the estimated uncertainty in the results decreases by typically ∼20% when both data sets rather than just one are included.
Funding Information
  • U.S.
  • Canada
  • U.K.
  • France
  • JAPAN
  • Italy
  • South Africa
  • Australia
  • NSF (AGS-1135446)
  • AFOSR Award (1210429)
  • NASA (NNX13AD64G)
  • NSF award (AGS-1025089)
  • National Science Foundation

This publication has 53 references indexed in Scilit: