Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions
Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions
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DOI：
10.1029/2020sw002673
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发表时间：
2020-06
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通讯作者：
R. Bailey;M. Reiss;C. Arge;C. Möstl;C. Henney;M. Owens;U. Amerstorfer;T. Amerstorfer;A. Weiss;J. Hinterreiter
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作者：
R. Bailey;M. Reiss;C. Arge;C. Möstl;C. Henney;M. Owens;U. Amerstorfer;T. Amerstorfer;A. Weiss;J. Hinterreiter
研究太阳周围的太阳风是空间气象研究的重要组成部分。太阳风是太阳发出的一种持续的压力驱动等离子体流。行星际空间中的环境太阳风流动决定了太阳风暴在到达地球之前是如何在日光层中演变的，特别是在太阳活动最低时期，太阳风暴本身就是地球磁场活动的驱动因素。因此，准确预报环境太阳风流对提高空间气象意识是十分必要的。在这里，我们提出了一种机器学习方法，其中使用太阳日冕磁场模型的解来输出地球附近的太阳风条件。在综合验证分析中，将结果与观测值和现有模型进行比较，新模型在几乎所有衡量标准上都优于现有模型。此外，这种方法提供了一个新的视角来讨论不同的输入数据对环境太阳风模拟的作用，以及这告诉我们关于潜在的物理过程的什么。这里讨论的最终模型代表了一种非常快速、经过充分验证的开源方法来预测地球上的环境太阳风。
Studying the ambient solar wind, a continuous pressure‐driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth's magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here, we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well‐validated and open‐source approach to the forecasting of ambient solar wind at Earth.