The USU-GAIM-FP data assimilation model for ionospheric specifications and forecasts

Physics-based data assimilation models have been used in meteorology and oceanography for several decades and are now becoming prevalent for specifications and forecasts of the ionosphere. This increased use of ionospheric data assimilation models coincides with the increase in data suitable for assimilation. At USU we have developed several different data assimilation models, including the Global Assimilation on Ionospheric Measurements Gauss-Markov (GAIM-GM) and the Full Physics (GAIM-FP) models. Both models assimilate a variety of different data types, including ground-based GPS/TEC, occultation, bottomside electron density profiles from ionosondes, in-situ electron densities, and space-based UV radiance measurements and provide specifications and forecasts on a spatial grid that can be global, regional, or local. While GAIM-GM is a simpler model that uses a statistical process in the Kalman filter, GAIM-FP is based on a more sophisticated Ensemble Kalman filter technique together with a physics-based ionosphere-plasmasphere model (IPM). The primary GAIM-FP output is in the form of 3-dimensional electron density distributions from 90 km to near geosynchronous altitude but also provides auxiliary information about the global distributions of the self-consistent ionospheric drivers (neutral winds and densities, electric fields). The GAIM-FP model has recently been updated and extended to include the ionospheric D-region and to incorporate bubble information obtained from the SSUSI instruments. Furthermore, additional data types have been added to the list of possible observations that can be assimilated. This list includes slant TEC observations form satellite-to-satellite and satellite-to-ground radio beacons as well as radio occultation data and in situ plasma density observations from generic satellites.