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An event-based approach to validating solar wind speed predictions: high-speed enhancements in the Wang-Sheeley-Arge model

机译:基于事件的方法来验证太阳风速预测:Wang-Sheeley-Arge模型中的高速增强

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摘要

One of the primary goals of the Center for Integrated Space Weather Modeling (CISM) effort is to assess and improve prediction of the solar wind conditions in near‐Earth space, arising from both quasi‐steady and transient structures. We compare 8 years of L1 in situ observations to predictions of the solar wind speed made by the Wang‐Sheeley‐Arge (WSA) empirical model. The mean‐square error (MSE) between the observed and model predictions is used to reach a number of useful conclusions: there is no systematic lag in the WSA predictions, the MSE is found to be highest at solar minimum and lowest during the rise to solar maximum, and the optimal lead time for 1 AU solar wind speed predictions is found to be 3 days. However, MSE is shown to frequently be an inadequate “figure of merit” for assessing solar wind speed predictions. A complementary, event‐based analysis technique is developed in which high‐speed enhancements (HSEs) are systematically selected and associated from observed and model time series. WSA model is validated using comparisons of the number of hit, missed, and false HSEs, along with the timing and speed magnitude errors between the forecasted and observed events. Morphological differences between the different HSE populations are investigated to aid interpretation of the results and improvements to the model. Finally, by defining discrete events in the time series, model predictions from above and below the ecliptic plane can be used to estimate an uncertainty in the predicted HSE arrival times.
机译:集成空间天气建模中心(CISM)的工作的主要目标之一是评估并改善对准稳定结构和瞬态结构引起的近地空间太阳风状况的预测。我们将8年的L1原位观测结果与Wang-Sheeley-Arge(WSA)经验模型对太阳风速的预测进行了比较。观测值和模型预测值之间的均方误差(MSE)用于得出许多有用的结论:WSA预测值没有系统性的滞后,发现MSE值在太阳最低时最高,而在上升时最低。太阳最大,预测1 AU太阳风速的最佳提前​​期为3天。但是,对于评估太阳风速的预测,MSE通常显示为不足的“品质因数”。开发了一种基于事件的补充分析技术,其中从观察到的时间序列和模型时间序列中系统地选择了高速增强(HSE)并将其关联。通过比较命中,未命中和错误的HSE数量,以及预测事件和观察到事件之间的时间和速度幅度误差,对WSA模型进行验证。研究了不同HSE种群之间的形态差异,以帮助解释结果和改进模型。最后,通过在时间序列中定义离散事件,可以使用黄道平面上方和下方的模型预测来估计HSE到达时间的不确定性。

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