JPL/USC GAIM: On the impact of using COSMIC and ground-based GPS measurements to estimate ionospheric parameters

Details JPL/USC GAIM: On the impact of using COSMIC and ground-based GPS measurements to estimate ionospheric parameters JPL/USC GAIM: On the impact of using COSMIC and ground-based GPS measurements to estimate ionospheric parameters

Feb 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010jgra..11502307k&link_type=abstract

Journal of Geophysical Research, Volume 115, Issue A2, CiteID A02307

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9

Space Weather: Models, Geodesy And Gravity: Atmosphere Monitoring With Geodetic Techniques (6952), Ionosphere: Modeling And Forecasting, Ionosphere: Instruments And Techniques, Radio Science: Tomography And Imaging (7270, 8180)

Scientific paper

The University of Southern California (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed the Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for space weather applications. JPL/USC GAIM is a physics-based 3-D data assimilation model that uses both four-dimensional variational analysis and Kalman-filter techniques to solve for the ion and electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. Here we report on GAIM Kalman filter-based assimilation results using ground-based GPS and COSMIC-derived total electron count (TEC) measurements. We find that assimilating COSMIC measurements into GAIM improves critical ionospheric parameters such as NmF2 and HmF2. Assimilating COSMIC data produces higher-accuracy vertical electron density profile “shapes,” as verified by comparisons to independent electron density profiles measured at Arecibo, Jicamarca, and Millstone Hill incoherent scatter radar (ISR). We also find significant improvement in global vertical TEC (VTEC) maps when assimilating COSMIC measurements, verified by comparing GAIM output with VTEC measurements from the Jason ocean altimeter. For 3 days in June 2006, improvement in accuracy compared to ground-data-only assimilation is found to be 30%, 28%, and 44%, respectively.

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