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

Abstract

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 AM on the visible side of the Sun, showing quantitative improvement of forecasting accuracy in low CME activity periods (e.g., Vrsnak, 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 A 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 AM 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 Vrsnak, Temmer, and Veronig (Solar Phys.240, 315, 2007a), who used the CH areas AM for predicting the solar wind parameters. In this period the correlation coefficient between Pch and v is R=0.70, whereas for AM and v the correlation coefficient is R=0.62. The average relative difference between the calculated and the observed values is \(\overline{|\delta|}\approx 12.15\%\) . 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 \(\overline{|\delta|}\approx 5.83\%\) . 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.