Computer Science – Artificial Intelligence
Scientific paper
Sep 1998
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998phdt........24c&link_type=abstract
Thesis (PHD). RICE UNIVERSITY , Source DAI-B 59/03, p. 1175, Sep 1998, 122 pages.
Computer Science
Artificial Intelligence
Magnetosphere, Neural Network, Artificial Intelligence
Scientific paper
The Rice Magnetospheric Specification and Forecast Model (MSFM) will be upgraded to remove the need for the ground based parameters Kp and Dst through the use of solar wind driven analytical and artificial neural network (ANN) models that specify and predict these parameters. The ground based parameters require long processing times before they are made available to the U.S. Air Force 55 th Space Weather Squadron and the installed version of the MSFM. This delay hinders the usefulness of the specification of the magnetosphere and subsequent forecasts. By replacing these parameters with values specified by solar wind driven models this processing time can be reduced and the MSFM can achieve a true forecast. The effects of a higher time resolution quasi-Kp index from the MEB inversion and solar wind ANN algorithms on the MSFM equatorial particle fluxes will also be investigated.
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