HR: 08:05h
AN: NG31C-01 INVITED     [Abstracts]
TI: Complexity in Magnetospheric Dynamics: from Modeling to forecasting
AU: * Ukhorskiy, A
EM: ukhorskiy@jhuapl.edu
AF: JHU/APL, 11100 Johns Hopkins Rd, Laurel, MD 20723 United States
AU: Sitnov, M
EM: sitnov@umd.edu
AF: University of Maryland, University of Maryland, College Park, MD 20742 United States
AU: Sharma, S
EM: ssh@astro.umd.edu
AF: University of Maryland, University of Maryland, College Park, MD 20742 United States
AU: Anderson, B
EM: brian.anderson@jhuapl.edu
AF: JHU/APL, 11100 Johns Hopkins Rd, Laurel, MD 20723 United States
AU: Ohtani, S
EM: shin.ohtani@jhuapl.edu
AF: JHU/APL, 11100 Johns Hopkins Rd, Laurel, MD 20723 United States
AU: Lui, A
EM: anthoni.lui@jhuapl.edu
AF: JHU/APL, 11100 Johns Hopkins Rd, Laurel, MD 20723 United States
AB: The solar wind-magnetosphere coupling exhibits complex behavior involving both global coherent and multi-scale dynamical features. Early attempts to explain its complexity in terms of low-dimensional dynamical chaos failed to reproduce its multi-scale constituent. More recent cellular automata models effectively reproduce the observed power-law spectra lacking the description of coherent dynamical features. The input-output analysis of various magnetospheric time series suggests a new data-derived description of the system. Based on the combination of nonlinear dynamical methods and statistical physics approach it reveals both global coherent and multi-scale features of solar wind - magnetosphere coupling. This data-derived approach yields an efficient forecasting model of magnetospheric dynamics during storms and substorms. It provides deterministic predictions of global component of magnetospheric dynamics and probabilistic predictions of its multi-scale features.
DE: 2722 Forecasting
DE: 2784 Solar wind/magnetosphere interactions
DE: 3220 Nonlinear dynamics
DE: 3240 Chaos
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting