Physics
Scientific paper
May 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agusmsa23a..11s&link_type=abstract
American Geophysical Union, Spring Meeting 2005, abstract #SA23A-11
Physics
2400 Ionosphere, 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. In the current application of the model the ionospheric plasma densities at F region heights obtained from our GAIM model will be compared with observed plasma densities from the Arecibo incoherent scatter radar. The emphasis of this comparison is on the observed and modeled day-to-day variability over Arecibo and its spatial extend as specified by the global and regional GAIM model. The comparison will cover several periods of Arecibo observations with both small and large day-to-day variability.
Scherliess Ludger
Schunk Robert W.
Sojka Jan J.
Thompson Daniel C.
Zhu Lijun
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