Using solar wind low-energy energetic ion enhancements to forecast large geomagnetic storms

Details Using solar wind low-energy energetic ion enhancements to forecast large geomagnetic storms Using solar wind low-energy energetic ion enhancements to forecast large geomagnetic storms

Dec 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufmsm31b1108s&link_type=abstract

American Geophysical Union, Fall Meeting 2003, abstract #SM31B-1108

Computer Science

Performance

2111 Ejecta, Driver Gases, And Magnetic Clouds, 2114 Energetic Particles, Heliospheric (7514), 2134 Interplanetary Magnetic Fields, 2139 Interplanetary Shocks, 2788 Storms And Substorms

Scientific paper

A forecasting method is developed based on the relationship between energetic ion enhancements and geomagnetic storms. This relationship was investigated in previous paper, Smith et al 2003 (hereafter referred to as Paper 1). An excellent correlation was found between large geomagnetic storms (storms with Kp equal to or greater than 7) and large energetic ion enhancements (those with high maximum flux values). An energetic Ion enhancement is hereafter referred to as an EIE. However there were many more large EIEs than large storms. Therefore other characteristics were sought in Paper 1 to help discriminate the EIEs likely to be followed by large storms. A forecasting method is developed here using the characteristics found useful in Paper 1. These are the nature of the IP driver (whether transient or co-rotating high speed stream) and whether the EIE was part of enhanced activity. An additional parameter, the magnitude of the total magnetic field is introduced here. The forecasting technique is developed using EIE events between February 1998 through December 2000 found primarily in ACE data. The prediction results obtained by this technique are presented for various maximum flux thresholds, together with the performance statistics. For a flux threshold that was exceeded by 60 of the 115 events found in this time interval, this technique would have predicted 24 of the 30 large storms that occurred in this time period. By including the other criteria, 83% of those events would have been correctly identified. The warning times obtained are also presented.

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