87th AMS Annual Meeting

Monday, 15 January 2007: 4:15 PM
A Thermosphere-Ionosphere Data Assimilation Model Component for a Seamless Ocean-Atmosphere Model
210A (Henry B. Gonzalez Convention Center)
Robert W. Schunk, Utah State University, Logan, UT; and L. Scherliess, D. C. Thompson, J. J. Sojka, and L. Zhu
We are developing a thermosphere-ionosphere data assimilation model that can be used as an upper atmospheric component for a seamless ocean-atmosphere model. The ionosphere data assimilation model can be either one of the USU GAIM models, e.g., either the Gauss Markov or Full Physics-Based Kalman filter model. The two models were developed as part of an effort called the Global Assimilation of Ionosphere Measurements (GAIM). Both models are capable of assimilating real-time (or near real -time) data from a variety of sources, including bottom-side Ne profiles from ionosondes, slant GPS/TEC from a network of stations, in situ Ne from DMSP satellites, line-of-sight UV emissions measured by satellites, and occultation data. Our Gauss-Markov (GM) data assimilation model uses a physics-based ionospheric model and a Kalman filter as a basis for assimilating the 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. The model has both regional and global capabilities and the output of the model is a 3-dimensional Ne distribution at specified times. Our full physics-based, Kalman filter, data assimilation model is more sophisticated than the Gauss-Markov model. This model is based on a physics-based model of the ionosphere-plasmasphere system and covers the altitude range from 9030,000 km. In addition to the global Ne distribution, the full physics model also provides global distributions of the self-consistent drivers (neutral winds & composition, electric fields, and particle precipitation). The thermosphere data assimilation model is being constructed from an already existing physics-based, global, thermosphere model using an ensemble Kalman filter technique. The thermosphere data assimilation model will be able to assimilate UV radiances from the SSUSI and SSULI instruments, satellite drag data, and inferred neutral parameters from incoherent scatter radars. The coupled thermosphere-ionosphere data assimilation model can also be coupled to the NCAR Whole Atmosphere Community Climate Model (WACCM) at 90 km, which will allow us to study the general effect that upward propagating gravity waves have on the thermosphere-ionosphere system for different solar cycle, seasonal, and geomagnetic activity conditions. Our data assimilation model can also be coupled to the Navy's troposphere weather model (NOGAPS) at 100 km, and this will yield a seamless ocean-atmosphere model. In addition, it will allow for rigorous studies of the effect that tropospheric weather disturbances have on the upper atmosphere. The status of this modeling effort will be reviewed.

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