A Physics-Based Kalman Filter for the Ionosphere in GAIM

Details A Physics-Based Kalman Filter for the Ionosphere in GAIM A Physics-Based Kalman Filter for the Ionosphere in GAIM

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agufmsa22a..09s&link_type=abstract

American Geophysical Union, Fall Meeting 2002, abstract #SA22A-09

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2427 Ionosphere/Atmosphere Interactions (0335), 2447 Modeling And Forecasting, 2467 Plasma Temperature And Density, 2736 Magnetosphere/Ionosphere Interactions

Scientific paper

A physics-based data assimilation model of the ionosphere is under development as the central part of a DoD MURI funded program called GAIM (Global Assimilation of Ionospheric Measurements). With the significant increase in the number of ionospheric observations that will become available over the next decade, this model will provide a powerful tool towards an improved specification and forecasting of the global ionosphere, with an unprecedented accuracy and reliability. The goal of this effort will be the development of an operational ionospheric assimilation model that will provide specifications and forecasts on spatial grids that can be global, regional, or local (50 km x 50 km). The specification/forecast will be in the form of 3-dimensional electron density distributions from 90 km to geosynchronous altitudes (35,000 km). In GAIM, the data assimilation is performed by a Kalman filter using a new physics-based ionosphere/plasmasphere model (IPM). This model includes 6 ion species (O2+, N2+, NO+, O+, H+, and He+) and currently covers the low and mid-latitudes from 90 km to about 20,000 km altitude. As a practical implementation the Kalman filter in GAIM is based on approximations of the state error covariance matrix, employing a reduction of the model dimension and a linearization of the physical model for the propagation of the error covariance matrix. These approximations lead to a dramatic reduction in the computational requirements. In this paper, we will give an update on the status of the Kalman filter development and present results from a global assimilation run. In this test three different data types were considered, including bottomside electron density profiles obtained from several digisondes, slant TEC from a network of ground-based GPS receivers, and in situ electron density measurements from DMSP satellites.

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