A New Forecasting Index for Solar Wind Velocity Based on EIT 284 Å Observations

Details A New Forecasting Index for Solar Wind Velocity Based on EIT 284 Å Observations A New Forecasting Index for Solar Wind Velocity Based on EIT 284 Å Observations

Jul 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008soph..250..159l&link_type=abstract

Solar Physics, Volume 250, Issue 1, pp.159-170

Physics

10

Sun: Euv Emission, Sun: Solar Wind

Scientific paper

Various solar wind forecasting methods have been developed during the past decade, such as the Wang Sheeley model and the Hakamada Akasofu Fry Version 2 (HAFv2) model. Also, considerable correlation has been found between the solar wind speed v and the coronal hole (CH) area A M on the visible side of the Sun, showing quantitative improvement of forecasting accuracy in low CME activity periods ( e.g., Vršnak, Temmer, and Veronig, Solar Phys. 240, 315, 2007a). Properties of lower layers of the solar atmosphere are good indications of the subsequent interplanetary and geomagnetic activities. We analyze the SOHO/EIT 284 Å images and construct a new forecasting factor (Pch) from the brightness of the solar EUV emission, and a good correlation is found between the Pch factor and the 3-day-lag solar wind velocity ( v) probed by the ACE spacecraft. The main difference between the Pch and A M factor is that Pch does not depend on the CH-boundary estimate and can reflect both the area and brightness of CH. A simple method of forecasting the solar wind speed near Earth in low CME activity periods is presented. Between Pch and v from 21 November until 26 December 2003, the linear correlation coefficient is R=0.89. For comparison we also analyze the data in the same period (DOY 25 125, 2005) as Vršnak, Temmer, and Veronig ( Solar Phys. 240, 315, 2007a), who used the CH areas A M for predicting the solar wind parameters. In this period the correlation coefficient between Pch and v is R=0.70, whereas for A M and v the correlation coefficient is R=0.62. The average relative difference between the calculated and the observed values is overline{|δ|}≈ 12.15% . Furthermore, for the ten peaks during the analysis period, Pch and v show a correlation coefficient of R=0.78, and the average relative difference between the calculated and the observed peak values is overline{|δ|}≈ 5.83% . Moreover, the Pch factor can eliminate personal bias in the forecasting process, which existed in the method using CH area as input parameter, because CH area depends on the CH-boundary estimate but Pch does not. Until now the CH-boundary could not be easily determined since no quantitative criteria can be used to precisely locate CHs from observations, which led to differences in forecasting accuracy.

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