Statistics – Computation
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufmsm42e..04s&link_type=abstract
American Geophysical Union, Fall Meeting 2003, abstract #SM42E-04
Statistics
Computation
2415 Equatorial Ionosphere, 2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting
Scientific paper
Although data assimilation techniques have been successfully used by the meteorologists and oceanographers for several decades, the space physics community has been slow in implementing data assimilation techniques, primarily because of the lack of a sufficient number of measurements. However, this situation is changing rapidly for the ionosphere. Within ten years, it is anticipated that there will be millions of ionospheric measurements per day from a variety of sources, and these data will be available for assimilation into specification and forecast models. 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). The Utah State University GAIM model will use a physics-based ionosphere-plasmasphere-polar wind model and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The new physics-based ionosphere-plasmasphere-polar wind model (IPM/IPWM) includes 6 ion species (O2+, N2+, NO+, O+, H+ and He+) and covers the low and mid latitudes from 90 km to about 20,000 km altitude and the high latitudes from 90 km to 10,000 km altitude. The Kalman filter is based on a reduced state approximation combined with a segmentation of the model domain into geographical sectors. These approximations not only lead to a dramatic reduction in the computational requirements but also make the model more easily adaptable for global, regional, or local applications. Several real data types, including in situ electron density measurements from DMSP satellites, bottomside electron density profiles from the Air Force network of digisondes, GPS-TEC from a global network of more than 160 ground stations, and occultation line-of-sight TEC measurements from three low Earth orbiting satellites are currently assimilated into the model. In this paper we will briefly review the Kalman filter technique including the applied approximations and then present results from a 3-day model run using data from the four different data types.
Scherliess Ludger
Schunk Robert W.
Sojka Jan J.
Thompson Daniel C.
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