A Global 3-D Kalman Filter for Ionospheric Data Assimilation in GAIM

Details A Global 3-D Kalman Filter for Ionospheric Data Assimilation in GAIM A Global 3-D Kalman Filter for Ionospheric Data Assimilation in GAIM

Dec 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001agufmsa32a0681s&link_type=abstract

American Geophysical Union, Fall Meeting 2001, abstract #SA32A-0681

Computer Science

Performance

2400 Ionosphere, 2447 Modeling And Forecasting, 2467 Plasma Temperature And Density, 2499 General Or Miscellaneous

Scientific paper

With the significant increase in the number of ionospheric observations that will become available over the next decade, data assimilation will provide a powerful technique towards an improved specification and forecasting of the global ionosphere, with an unprecedented accuracy and reliability. A university consortium composed of Utah State University (USU), the University of Colorado in Boulder (CU), the University of Texas in Dallas (UTD), and the University of Washington (UW) has been funded to construct a data assimilation system for the Earth's ionosphere called GAIM (Global Assimilation of Ionospheric Measurements). GAIM uses a Kalman filter for the specification and forecast of the global ionospheric and plasmaspheric Ne distribution. Currently the filter simultaneously assimilates three different data sets, including: (1) slant path Total Electron Content (TEC) measurements from GPS satellites and ground-based receivers; (2) bottomside electron density (Ne) profiles from several ground-based ionosondes; (3) in situ electron density measurements from two DMSP satellites. In this paper we will give a brief description of the mathematical framework, in which the assimilation problem is formulated and present 3-D reconstructions of the Ne distribution obtained from the Kalman filter. To evaluate the performance of the filter we will compare these results with independent observations.

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