Specification of Ionospheric Dynamics at Low- and Mid-Latitude Using the Global Assimilation of Ionospheric Measurements (GAIM) Model

Physics

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2415 Equatorial Ionosphere, 2437 Ionospheric Dynamics, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting

Scientific paper

It is well known that the ionosphere-plasmasphere-thermosphere system at low and middle latitudes is strongly coupled, and therefore, a study of ionospheric dynamics must take into account the interaction between the different domains. As shown by meteorologists and oceanographers, a powerful way of modeling complex systems is with the use of data assimilation models. At USU, we have developed two GAIM data assimilation models with different complexity and both provide global and regional specifications of the 3-dimensional ionosphere-plasmasphere plasma densities. One of these models is our Full Physics-Based Kalman filter data assimilation model, which is based on a physics-based model for the ionosphere-plasmasphere system, a diverse array of data sources, and an ensemble Kalman filter data assimilation technique. This model covers the ionosphere-plasmasphere system from 90 to 30,000 km altitude and includes 6 ion species (NO+, N2+, O2+, O+, He+, H+). The strength of this model is that in addition to the global and regional 3-D ionosphere electron density distribution it also self-consistently determines the corresponding ionospheric drivers, including the thermospheric neutral winds and composition and the electric fields. The model can assimilate a variety of different data types, including GPS/TEC from up to 1000 ground receivers, in situ Ne from several DMSP satellites, bottomside Ne profiles from tens of ionosondes, and radio occultation data from the six COSMIC satellites. We have used this model to study the dynamics of the low- and mid-latitude ionosphere on a case-by-case basis to determine the various driving forces and to study their temporal and spatial variability. We will present examples of the ionospheric and driver variability obtained from our model and compare the results with independent data.

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