Geomagnetic Storm and Substorm Predictions with the Real-Time WINDMI Model

Details Geomagnetic Storm and Substorm Predictions with the Real-Time WINDMI Model Geomagnetic Storm and Substorm Predictions with the Real-Time WINDMI Model

Dec 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007agufmsm34a..04m&link_type=abstract

American Geophysical Union, Fall Meeting 2007, abstract #SM34A-04

Statistics

Computation

2788 Magnetic Storms And Substorms (7954), 7959 Models

Scientific paper

The Real-Time WINDMI model is an implementation of WINDMI, a low dimensional, plasma physics-based, nonlinear dynamical model of the coupled magnetosphere-ionosphere system. The system of nonlinear ordinary differential equations, which describes energy transfer into, and between dominant components of the nightside magnetosphere and ionosphere, is solved numerically to determine the state of each component. The model characterizes the energy stored in the ring current and the region 1 field-aligned current which are compared to the Dst and AL indices. Solar wind parameter measurements are available from the ACE satellite in real-time. These quantities are automatically downloaded every 10 minutes and used to derive the input solar wind driving voltage to the model. This allows the computation of model Dst and AL values by Real-Time WINDMI about 1-2 hours before index data is available at the Kyoto WDC Quicklook website. Model results are shown on the website (http://orion.ph.utexas.edu/~windmi/realtime/) and there is also an email alert system which sends a notification when Dst activity is predicted below -50 nT or AL activity below -500 nT. When data is available the model parameters are optimized every hour using a genetic algorithm, which has already been implemented for WINDMI. The model has captured about 13 storm and/or substorm events in the past 1.5 years it has been running. For these events, the Real-Time WINDMI output is studied for the rectified driving voltage compared to the Siscoe et al. voltage as input. The events the model did not capture are also investigated. The work is supported by NSF grant ATM-0638480.

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