Constraining the plasmasphere dynamics with multiple data sets and data assimilation

Tuesday, 16 December 2014
Anders Moller Jorgensen1, Janos Lichtenberger2, Balazs Heilig3, Massimo Vellante4, Jan Reda5, Reiner H W Friedel6, Michael G Henderson6, Daniel M Ober7, Athanasios Boudouridis8, Eftyhia Zesta9, Peter J Chi10, Junghee Cho11 and Roxanne M Katus12, (1)New Mexico Institute of Mining and Technology, Elec, Socorro, NM, United States, (2)Eotvos University, Budapest, Hungary, (3)Geological and Geophysical Institute of Hungary, Budapest, Hungary, (4)University of L'Aquila, L'Aquila, Italy, (5)Institute of Geophysics Polish Academy of Sciences, Warszawa, Poland, (6)Los Alamos National Laboratory, Los Alamos, NM, United States, (7)Air Force Research Laboratory, Kirtland AFB, NM, United States, (8)Space Science Institute, Boulder, CO, United States, (9)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (10)University of California Los Angeles, Los Angeles, CA, United States, (11)Chungbuk National University, Department of Astronomy and Space Science, Cheongju, South Korea, (12)University of Michigan, Ann Arbor, MI, United States
The Earth's plasmasphere is a region of dense plasma, originating in
the ionosphere, extending nearly to geostationary orbit. The precise
extent of the plasmasphere is dynamic, particularly during
geomagnetic active conditions. Knowing the exact distribution of
plasma in the plasmasphere is important as an input to coupled
magnetospheric models. In particular, density gradients inside the
plasmasphere and at the plasmapause, are important in controlling
waves which are responsible for the growth and decay of the radiation
belts. At the most basic level the plasmasphere can be described in
terms of plasma exchange with the ionosphere and convection due to an
imposed electric field. At that level plasmasphere modeling is
relatively simple. However there is currently insufficient knowledge
of the drivers, particularly the electric field, to model the
plasmasphere boundaries at the most accurate level to provide
sufficient quality inputs to wave and radiation belt models.

One solution to this problem is to use a data assimilation
approach. Data assimilation wraps a feedback loop around the
plasmasphere model in which free, ideally unknown, model parameters
are adjusted to maximize the agreement between the model and
observations. There are many possible implementations of this feedback
loop. We use the Ensemble Kalman Filter in which a statistical
ensemble of models tracks the observations through linear
transformations. In previous work we have used either ground-based
observations from the PLASMON project (funded by the European Seventh
Framework Program), or a small number of space-based observations. The
next step is to use a larger number of data sources, including a
variety of ground-based and space-based observations as well as other
knowledge contains in empirical models. We will discuss our approach
to incorporating disparate data sets and demonstrate some assimilation
results which combine different data sources.