HR: 11:20h
AN: NG22A-05 INVITED     [Abstracts]
TI: Space Weather Forecasting: Integrated Model Based on Nonlinear Dynamics and Statistical Physics
AU: * Sharma, A
EM: ssh@umd.edu
AF: University of Maryland, Department of Astronomy, College Park, MD 20742 United States
AU: Ukhorskiy, A Y
EM: aleksandr.ukhorskiy@jhuapl.edu
AF: Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723 United States
AU: Chen, J
EM: chenjian@astro.umd.edu
AF: University of Maryland, Department of Astronomy, College Park, MD 20742 United States
AB: The magnetosphere is an open system driven by the turbulent solar wind and exhibits complex behavior with global and multiscale characteristics. The multiscale behavior is characterized by power law distributions and arise due to, in part, the turbulence in the solar wind. On the other hand the overarching global dynamical behavior originate mainly from the internal dynamics and is evident in processes such as plasmoid formation and release. The global nature of the magnetosphere is characterized by low-dimensionality and is evident in the numerical simulations using global MHD models. The recognition of the capability of nonlinear dynamical models to capture the inherent features in the data forms the basis for the data-derived models of the coupled solar wind - magnetosphere system. The models of the global behavior use the dynamical trajectories in the reconstructed phase space and a mean field approach based on averages over nearest neighbors has been used for space weather forecasting. The multiscale aspects may not be predicted based on dynamical considerations. However the deviations from the mean field forecasts are closely related to the driver, i.e., the solar wind, and a Bayesian approach is used to compute the conditional probabilities from the solar wind and magnetospheric data. The probability density functions are computed using the leading eigenvalues from a principal component analysis. The predictions of the global features, based on nonlinear dynamical modeling, and the likelihood of the deviations from them, based on statistical physics considerations, provide an integrated space weather forecasting technique. The data from different phases of the solar cycle, and the corresponding levels of geospace disturbances are used to yield accurate predictions. Phase transitions, which exhibit global behavior (first order) and scale invariance (second order), provide a framework for the global and multiscale phenomena underlying the space weather forecasts.
DE: 2722 Forecasting
DE: 2784 Solar wind/magnetosphere interactions
DE: 2788 Storms and substorms
DE: 3210 Modeling
DE: 3220 Nonlinear dynamics
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting