89th American Meteorological Society Annual Meeting

Tuesday, 13 January 2009: 11:45 AM
Ionosphere Data Assimilation Models for Specifications and Forecasts
Room 126B (Phoenix Convention Center)
Robert W. Schunk, Utah State University, Logan, UT; and L. Scherliess, J. J. Sojka, D. C. Thompson, and L. Zhu
Ionosphere weather can have detrimental effects on a variety of civilian and military systems and operations. It can affect over-the-horizon (OTH) radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the FAA's Wide-Area Augmentation System (WAAS). In an effort to mitigate and/or forecast the adverse effects of the ionosphere on these systems, we developed two physics-based data assimilation models of the ionosphere under a program called Global Assimilation of Ionospheric Measurements (GAIM). One of the data assimilation models is now in operational use at the Air Force Weather Agency (AFWA) in Omaha, Nebraska. This model is a Gauss-Markov Kalman Filter (GAIM-GM) model, 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 ionosphere 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-polar wind 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), which is global and covers the altitude range of from 90 to 30,000 km, is more sophisticated than the Gauss-Markov model, and hence, should provide more reliable specifications in data poor regions and during severe weather disturbances. The GAIM-FP model is scheduled for operational use in 2012. Both of these GAIM models assimilate bottom-side Ne profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ Ne from four DMSP satellites, line-of-sight UV emissions measured by satellites, and occultation data. 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 current status, validation efforts, and future plans for the USU GAIM models will be discussed.

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