Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates
Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates
复制标题
DOI：
10.1029/1999ja000262
复制
发表时间：
2000-05-01
影响因子：
2.8
通讯作者：
Pizzo, VJ
中科院分区：
文献类型：
--
作者：
Arge, CN;Pizzo, VJ
Wang-Sheeley模型是一个可以预测背景太阳风速和行星际磁场(IMF)极性的经验模型。我们对基本技术进行了一些修改，极大地提高了模型的性能和可靠性。首先，我们建立了一个连续的经验函数，它将源表面的磁膨胀因子与太阳风速联系起来。其次，我们使用径向气流的假设和一个简单的方案来解释它们之间的相互作用，从而将风从源表面传播到地球。第三，我们开发并应用了一种从Wilcox太阳天文台(WSO)识别和删除有问题的磁图的方法。第四，我们修正了WSO视线磁图中由太阳b角的年变化引起的极场强度调制效应。第五，我们探索了一些技术，以优化从WSO磁图构建每日更新的天气图。我们报告了一项全面的统计分析，将Wang-Sheeley模型的预测与以1996年5月太阳活动极小期为中心的3年时间内的风卫星数据集进行了比较。太阳风速的预测值和观测值在统计上具有显著的相关性(类似于0.4)，平均偏差为0.15。如果从比较中剔除具有较大数据差距的单个(6个月)周期，则正确地预测太阳风速在10-15%以内。国际货币基金组织的极性预测准确率类似于75%。本文介绍的太阳风预报技术可直接应用于空间天气研究和预报。
The Wang-Sheeley model is an empirical model that can predict the background solar wind speed and interplanetary magnetic field (IMF) polarity. We make a number of modifications to the basic technique that greatly improve the performance and reliability of the model. First, we establish a continuous empirical function that relates magnetic expansion factor to solar wind velocity at the source surface. Second, we propagate the wind from the source surface to the Earth using the assumption of radial streams and a simple scheme to account for their interactions. Third, we develop and apply a method for identifying and removing problematic magnetograms from the Wilcox Solar Observatory (WSO). Fourth, we correct WSO line-of-sight magnetograms for polar field strength modulation effects that result from the annual variation in the solar b angle. Fifth, we explore a number of techniques to optimize construction of daily updated synoptic maps from the WSO magnetograms. We report on a comprehensive statistical analysis comparing Wang-Sheeley model predictions with the WIND satellite data set during a 3-year period centered about the May 1996 solar minimum. The predicted and observed solar wind speeds have a statistically significant correlation (similar to 0.4) and an average fractional deviation of 0.15. When a single (6-month) period with large data gaps is excluded from the comparison, the solar wind speed is correctly predicted to within 10-15%. The IMF polarity is correctly predicted similar to 75% of the time. The solar wind prediction technique presented here has direct applications to space weather research and forecasting.