Physics
Scientific paper
Dec 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004agufmsa33a1143s&link_type=abstract
American Geophysical Union, Fall Meeting 2004, abstract #SA33A-1143
Physics
2415 Equatorial Ionosphere, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting
Scientific paper
Physics-based data assimilation models of the ionosphere were developed at Utah State University as the central part of a DoD MURI funded program called GAIM (Global Assimilation of Ionospheric Measurements). Recently, the Air Force Weather Agency (AFWA) has selected one of the USU GAIM models for its operational use and the same model will also be implemented at the Community Coordinated Modeling Center (CCMC) for scientific studies. The selected model is based on a physics-based model of the ionosphere and a Gauss-Markov Kalman Filter (GMKF) as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts of five ion species and covers the E-region, F-region and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration the GMKF assimilates slant TEC from a variable number of ground GPS sites, bottom-side Ne profiles from a variable number of ionosondes, in situ Ne from four DMSP satellites, and nighttime line-of-sight UV radiances measured by satellites. To test the GMKF for real-time operations and to validate its ionospheric density specifications, three month-long validation periods covering a variety of different geophysical conditions have been identified. During these three periods the model ran continuously and automatically and produced 3-dimensional global electron density distributions in 15 minute increments. The results of this validation study, with an emphasis on a comparison with independent observations, will be presented.
Scherliess Ludger
Schunk Robert W.
Sojka Jan J.
Thompson Daniel C.
Zhu Lijun
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