Other
Scientific paper
Dec 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006agufmsa41b1420s&link_type=abstract
American Geophysical Union, Fall Meeting 2006, abstract #SA41B-1420
Other
2415 Equatorial Ionosphere, 2437 Ionospheric Dynamics, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting, 2475 Polar Cap Ionosphere
Scientific paper
As part of the Global Assimilation of Ionospheric Measurements (USU-GAIM) program, we developed two Kalman filter data assimilation models of the global ionosphere, including Gauss-Markov (GM) and Full Physics (FP) models. Our Gauss-Markov 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 a multi-ion global model that covers the E-region, F-region, and topside from 90 to 1400 km. The GM model has both regional and global capabilities and the output of the model is a 3-dimensional Ne distribution at specified times. With the GM model the ionospheric densities obtained from the IFM constitute a background ionospheric density field on which perturbations are superimposed based on the available data sources and their errors. The density perturbations and the associated errors evolve over time via a statistical Gauss-Markov process. Our full physics data assimilation model is more sophisticated than the Gauss-Markov model. This model is based on a physics-based ionosphere-plasmasphere-polar wind model, which includes six ion species (NO+, O2+, N2+, O+, H+, and He+) and covers the low-mid latitudes from 90 to 20,000 km. In addition to the global Ne distribution, the full physics model also provides global distributions of the self-consistent drivers (neutral winds and composition, electric fields, and particle precipitation). Both models can assimilate in situ electron densities from 4 DMSP satellites, bottomside Ne profiles from 30 ionosondes, slant TECs from up to 1000 ground GPS/TEC receivers, occultation data and line-of-sight UV emissions. The USU-GAIM models are now being widely used, but as with all physics-based data assimilation models, care must be exercised because these models have both strengths and limitations. Some of the important issues relate to the quality, amount and distribution of the data, the ability to obtain reliable data errors, representation errors, and the physics contained in the underlying model. These and other issues related to the data assimilation models will be discussed.
Scherliess Ludger
Schunk Robert W.
Sojka Jan J.
Thompson Daniel C.
Zhu Lijun
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