SMEI Observations and Space Weather Forecasting

Details SMEI Observations and Space Weather Forecasting SMEI Observations and Space Weather Forecasting

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006agufmsh33a0394w&link_type=abstract

American Geophysical Union, Fall Meeting 2006, abstract #SH33A-0394

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2101 Coronal Mass Ejections (7513), 2111 Ejecta, Driver Gases, And Magnetic Clouds, 2788 Magnetic Storms And Substorms (7954), 7513 Coronal Mass Ejections (2101), 7974 Solar Effects

Scientific paper

CMEs are a primary cause of severe space weather at Earth because they trigger geomagnetic storms that damage space and ground-based assets. The Solar Mass Ejection Imager (SMEI) is a U. S. Air Force experiment whose primary purpose is to demonstrate the ability to track CMEs from near the Sun to Earth, thus providing a new capability for forecasting storms. SMEI has accomplished this objective by detecting geoeffective CMEs at elongations of 20-30 deg, equivalent to ~1/3 of the distance from the Sun to Earth. Depending on the speed of the CME front, these distances correspond to advance-warning times of 10 hours to 2 days. We have performed several studies of SMEI's capability to detect and track geoeffective CMEs and, therefore, its capability as a forecasting tool. In one case, we examined the sources of moderate (peak Dst < - 60nT) and intense (peak Dst < - 100nT) geomagnetic storms during a 2-year interval. SMEI observed associated Earthward-directed CMEs for about 85% of both kinds of storms. For the intense storms, the mean lags between the first SMEI observation of the CME and 1) Earth arrival of the associated shock and 2) onset of the storm were 18.6 and 29.25 hours, respectively. In another study, we used distance-time plots of LASCO halo CMEs associated both with SMEI transients and ACE shocks at 1 AU to compare the predicted and actual arrival times. The mean range between these values was 10.6 hr., representing an approximate 30% improvement in the accuracy of predicting CME arrival times compared to published results using LASCO data only. We also report on developing metrics and other tools as a means for transitioning SMEI results for forecasting use, and on using SMEI in combination with STEREO data to improve forecasting skill.

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