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Improved modeling and prediction of total atmospheric refractivity by assimilation of angle of arrival and total electron content measurements from an array of GPS receivers

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Monday, 24 January 2011
Improved modeling and prediction of total atmospheric refractivity by assimilation of angle of arrival and total electron content measurements from an array of GPS receivers
Bonnie Valant-Spaight, Propagation Research Associates, Inc., Marietta, GA; and G. M. Hall, A. J. Mannucci, A. Komjathy, B. D. Wilson, and M. A. Dumett

Poster PDF (908.6 kB)

The Total Atmospheric Effects Mitigation (TAEM) system measures atmospheric effects on GPS signals and then assimilates those measurements into both tropospheric and ionospheric models in order to determine current and forecast atmospheric states. TAEM hardware includes multiple ground-based, dual-frequency GPS receivers in an array. Use of multiple receivers extends performance to elevations below 10 degrees, where multipath effects typically corrupt GPS signal quality. Measurements of the difference between observed and expected angle of arrival are assimilated into the Weather Research and Forecasting (WRF) model, improving the model's fidelity of temperature, pressure, and humidity, while measurements of total electron content are assimilated into the Jet Propulsion Laboratory/University of Southern California's Global Assimilative Ionospheric Model (GAIM), improving that model's fidelity of ionospheric electron densities. Such joint assimilation has the potential to improve the characterization and prediction of the local refractivity field from the earth's surface up to the upper atmosphere, which would lead to improved atmospheric corrections to electromagnetic signals of interest.

We present results from a field test of the TAEM system performed in January, 2010. Although the ionosphere was quiet during this period due to solar minimum, benefits are still apparent from using the recently developed nested-grid capability of GAIM to create a higher resolution representation of the ionosphere in the local area of the TAEM hardware. In addition, we show how knowledge of the tropospheric state affects the value of ionospheric corrections to RF signals.