Statistics – Applications
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufmsm51b0514s&link_type=abstract
American Geophysical Union, Fall Meeting 2003, abstract #SM51B-0514
Statistics
Applications
2447 Modeling And Forecasting, 2467 Plasma Temperature And Density, 2753 Numerical Modeling
Scientific paper
GAIM uses 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. Some of the data that are assimilated include in situ electron density measurements from the DMSP satellites, bottomside electron density profiles from a network of digisondes, GPS-TEC data from a network of more than 300 stations, and occultation data. GAIM provides specifications and forecasts on a spatial grid that can be global, regional, or local (25 x 25 km). The primary GAIM output is in the form of 3-dimensional electron density distributions from 90 km to the geosynchronous altitude (35,000 km). GAIM also provides auxiliary parameters (NmF2, hmF2, NmE, hmE, slant and vertical TEC) and global distributions of the self-consistent ionospheric drivers (neutral winds and densities, magnetospheric and dynamo electric fields, and particle precipitation patterns). In its specification mode, GAIM provides quantitative estimates for the accuracy of the reconstructed ionospheric densities. In addition to the physics-based, Kalman filter model, we have also developed a Gauss-Markov Kalman filter model and an approximate (semi-physics-based) Kalman filter model. A beta version of the Gauss-Markov model has been running continuously and autonomously since January 1, 2003. This is a regional (mainly over the U.S.), high-resolution, data assimilation model that can assimilate GPS-TEC data from up to 400 of the NOAA CORS ground-based receivers. A global Gauss-Markov Kalman Filter model has been running continuously since July 1, 2003, and this model can assimilate four data types, including Ne profiles from digisondes, in situ satellite densities, GPS-TECs from 300 stations, and occultation data. The status of the models and the relevant scientific applications will be presented.
Anderson Dale N.
Codrescu Mihail
Fuller-Rowell Tim J.
Hairston Marc
Heelis Roderick A.
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