Utah State University Global Assimilation of Ionospheric Measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation
Authors:
Open Access
- 16 November 2006
- journal article
- ionosphere and-upper-atmosphere
- Published by American Geophysical Union (AGU) in Journal of Geophysical Research
- Vol. 111 (A11)
Abstract
The Utah State University Gauss‐Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics‐based model of the ionosphere and a Gauss‐Markov Kalman filter 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 for 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 total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line‐of‐sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real‐time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real‐time operations, the model ran continuously and automatically and produced three‐dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss‐Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.Keywords
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