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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
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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.
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
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).
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]