Constraining the plasmasphere dynamics with multiple data sets and data assimilation

The Earth's plasmasphere is a region of dense plasma, originating inthe ionosphere, extending nearly to geostationary orbit. The preciseextent of the plasmasphere is dynamic, particularly duringgeomagnetic active conditions. Knowing the exact distribution ofplasma in the plasmasphere is important as an input to coupledmagnetospheric models. In particular, density gradients inside theplasmasphere and at the plasmapause, are important in controllingwaves which are responsible for the growth and decay of the radiationbelts. At the most basic level the plasmasphere can be described interms of plasma exchange with the ionosphere and convection due to animposed electric field. At that level plasmasphere modeling isrelatively simple. However there is currently insufficient knowledgeof the drivers, particularly the electric field, to model theplasmasphere boundaries at the most accurate level to providesufficient quality inputs to wave and radiation belt models. One solution to this problem is to use a data assimilationapproach. Data assimilation wraps a feedback loop around theplasmasphere model in which free, ideally unknown, model parametersare adjusted to maximize the agreement between the model andobservations. There are many possible implementations of this feedbackloop. We use the Ensemble Kalman Filter in which a statisticalensemble of models tracks the observations through lineartransformations. In previous work we have used either ground-basedobservations from the PLASMON project (funded by the European SeventhFramework Program), or a small number of space-based observations. Thenext step is to use a larger number of data sources, including avariety of ground-based and space-based observations as well as otherknowledge contains in empirical models. We will discuss our approachto incorporating disparate data sets and demonstrate some assimilationresults which combine different data sources.