SA31C-01:
Scientific Progress in Understanding Thermosphere-Ionosphere Dynamics Enabled by Ground-based Networks of Instrumentation
Wednesday, 17 December 2014: 8:00 AM
Jonathan J Makela1, John W Meriwether2, Daniel James Fisher3, Brian Joseph Harding3, Aaron J Ridley4, Gregory Duane Earle5, Marco Ciocca6, Michael Castelaz7, Ricardo Buriti8, Marco A Milla9, Luis Navarro9, Gary Bust10 and Farzad Kamalabadi3, (1)University of Illinois, Urbana, IL, United States, (2)Clemson University, Clemson, SC, United States, (3)University of Illinois at Urbana Champaign, Urbana, IL, United States, (4)Univ Michigan, Ann Arbor, MI, United States, (5)Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, (6)Eastern Kentucky University, Richmond, KY, United States, (7)Pisgah Astronomical Research Institute, Rosman, NC, United States, (8)UFCG Federal University of Campina Grande, Campina Grande, Brazil, (9)Instituto Geofísico del Perú, Jicamarca Radio Observatory, Lima, Peru, (10)John Hopkins University-Applied Physics Laboratory, Laurel, MD, United States
Abstract:
The recent increase in the number of ground-based instruments, many in coordinated arrays or collocated clusters, has led to exciting new insights into the temporal and spatial dynamics of the thermosphere-ionosphere system. However, these multi-instrument, multi-site deployments necessitate the development of new analysis algorithms and operational strategies to optimize their scientific output. Additionally, as we look to expand these networks in the future, questions of sensor density comes to the forefront. Here, we review some recent examples of progress in understanding thermosphere-ionosphere dynamics enabled by networks of ground-based optical instrumentation (specifically optical imagers and Fabry-Perot interferometers) and discuss the future of these types of projects. In many cases, the results from these optical networks are augmented by other arrays of instruments, such as dual-frequency receivers and radar systems, as well as assimilative and numerical models.
