HR: 17:20h
AN: SH14B-06    [Abstracts]
TI: New Forecasting Factor for Solar Wind Velocity From EIT Observations
AU: Luo, B
EM: bxluo@hotmail.com
AF: Center for Space Science and Applied Research Chinese Academy of Sciences, #1 Nanertiao Zhongguancun Haidian, Beijing, 100080, China
AU: * Liu, S
EM: liusq@earth.sepc.ac.cn
AF: Center for Space Science and Applied Research Chinese Academy of Sciences, #1 Nanertiao Zhongguancun Haidian, Beijing, 100080, China
AU: Zhong, Q
AF: Center for Space Science and Applied Research Chinese Academy of Sciences, #1 Nanertiao Zhongguancun Haidian, Beijing, 100080, China
AU: Gong, J
AF: Center for Space Science and Applied Research Chinese Academy of Sciences, #1 Nanertiao Zhongguancun Haidian, Beijing, 100080, China
AB: Various solar wind velocity forecasting methods at 1AU have been developed during the last decade, such as Wang-sheeley model and Hakamada-Akasofu-Fry Version 2 (HAFv2) model. Some authors have found that Coronal hole(CH) areas can be used to forecast the solar wind velocity with better results in low CME activity periods(e.g. Vršnak et.al.). The property of the solar surface is a good indication of the following interplanetary and geomagnetic activities. We analyzed all EIT284Å images in almost the whole solar cycle 23 and developed a new forecasting factor(Pch) from the brightness of the solar Extreme Ultraviolet Images. and a good relationship was found between the Pch and solar wind velocity V three days later probed by ACE spacecraft. A simple method of forecasting the solar wind speed near earth in low CME activity periods is presented. For Pch and solar wind velocity, the linear correlation coefficients is R = 0.89 from 21 September until 26 December. For comparison we also analysed the same period data as Vršnak(2007) who using the coronal hole areas A_M as input parameters for predicting solar wind velocity. The linear least-squares fit of Pch with the 3-day lag solar wind velocity showed a correlation coefficient R = 0.70, which is better than the result using A_M(R = 0.62). The solar wind speed could be expressed as V (km s^-1) = 337 + 0.00868 × Pch. The average of relative difference between the calculated and the observed values amounts to |δ̄| ≈ 12.15%. Furthermore, for the ten peaks during the analysis period, A_M and V just showed a correlation coefficient R = 0.32, much worse than using Pch factor which showed R = 0.75. Moreover, the Pch factor exterminated personal bias in the forecasting process, which existed in the method using A_M as input parameters because the coronal hole boundary can not be easily determined since no quantitative criteria can be used to precisely locate coronal holes from observation. Finally, the expression of V by Pch is analysed, which showed the variation of background solar wind speed during the whole solar cycle 23.
DE: 0520 Data analysis: algorithms and implementation
DE: 7509 Corona
DE: 7511 Coronal holes
SC: SPA-Solar and Heliospheric Physics [SH]
MN: 2007 Fall Meeting