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Real-Time Geomagnetic Neural Network Model

An unconstrained data-derived neural network model of geomagnetic quantities with real-time features. Outputs geomagnetic disturbance measures based on solar wind data, location, and auxiliary information. Versions available for various time resolutions and modification possibilities. Spatial resolution is arbitrary with efficient interpolation. Features rapid validation, prediction capabilities, and operation-friendly. Full implementation details included.

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Real-Time Geomagnetic Neural Network Model

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  1. Bob Weigel, Department of Computational and Data Sciences, rweigel@gmu.edu Model Description • An “unconstrained” data-derived model of geomagnetic quantities (neural network). • Has several useful real-time features. • Outputs several geomagnetic disturbance measures given • solar wind time history, • spatial location, and • auxiliary information.

  2. Model Overview • Now have versions for D = • B (15-min averages – PE optimized) • B (1-min averages – Ms optimized) • |dB/dt| (15-min average) • sB(15-min average) • Could be modified for 1-min or less. • New version allows dependence on • average of an auxiliary variable, e.g., disturbed times • longitude dependence • Could add seasonal easily • Spatial resolution: arbitrary spatial locations. Interpolation is used, best near location of station with long history of data. 15 minutes

  3. Model Features • Rapid validation - solar cycle validation takes < 1 hour. • Prediction capabilities well-documented and understood from previous analysis. • Operations friendly - can handle data drop-outs: fall back to climatology. In principle, could be modified to handle drop-outs of a given variable (e.g. satellite magnetometer fails, use only solar wind velocity and climatology).

  4. Implementation Details • Model Name: Weigel 2010 • How close to real time: real time • Latitude range of validity: All* • External requirements: CCMC delivery runs in Octave (mac/pc/linux). Delivered via svn checkout. Version exists for ANSI C. • Spatial resolution: arbitrary (interpolated) • Temporal resolution: 15-minute, but switch exists to re-sample to 1-minute. • Duration of runs without intervention: ? • References: Weigel et al., JGR, 2003 [http://dx.doi.org/10.1029/2002JA009627]

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