4.2
Data assimilation models for ionosphere specifications and forecasts

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Tuesday, 25 January 2011: 8:45 AM
Data assimilation models for ionosphere specifications and forecasts
4C-3 (Washington State Convention Center)
Robert W. Schunk, Utah State Univ., Logan, UT; and L. Scherliess, J. J. Sojka, D. C. Thompson, and L. Zhu

In recent years, we have developed, and are continuing to develop, four physics-based data assimilation models of the ionosphere under a program called Global Assimilation of Ionospheric Measurements (GAIM). One of our data assimilation models became an operational model at the Air Force Weather Agency (AFWA) in 2006. This model is a Gauss-Markov Kalman Filter model (GAIM-GM) and it uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside from 90 to 1400 km. It takes account of five ion species (NO+, O2+, N2+, O+, H+), but the main output of the model is a 3-dimensional electron density distribution at user specified times. The second data assimilation model uses a physics-based ionosphere-plasmasphere model and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. This full physics model (GAIM-FP) is global, takes account of six ion species (NO+, O2+, N2+, O+, H+, He+), covers the altitude range of from 90 to 30,000 km, and is more sophisticated than the Gauss-Markov model. Hence, it should provide more reliable specifications in data poor regions and during severe weather disturbances. Both of these GAIM models assimilate bottom-side Ne profiles from 100 ionosondes, slant TEC from 1000 ground GPS/TEC stations, in situ Ne from 4 DMSP satellites, occultation data from multiple satellites, and line-of-sight UV emissions measured by satellites. Quality control algorithms for all of the data types are provided as an integral part of the GAIM models and these models take account of latent data (up to 3 hours). The third data assimilation model is a physics-based ensemble Kalman filter for high-latitude ionosphere dynamics and electrodynamics. This model assimilates cross-track ion velocities from the 4 DMSP satellites, line-of-sight ion velocities from the SuperDarn radar system, and magnetic perturbations from 100 ground magnetometers and from the Iridium constellation of satellites. The fourth data assimilation model is an ensemble Kalman filter model for the global thermosphere. The characteristics and uses of these models for space weather applications will be discussed.