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.

中文翻译：

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使用梯度增强回归来改善环境太阳风模型预测

研究周围的太阳风是从太阳发出的持续的压力驱动的等离子体流，是空间天气研究的重要组成部分。行星际空间中的周围太阳风流动决定了太阳风暴在到达地球之前是如何通过太阳圈演化的，特别是在太阳最低峰期间，它们本身就是地球磁场活动的驱动力。因此，准确预测周围的太阳风对提高太空天气意识至关重要。在这里，我们提出了一种机器学习方法，其中使用来自日冕电磁铁模型的解来输出地球附近的太阳风状况。在全面的验证分析中，将结果与观察值和现有模型进行比较，新模型在几乎所有指标上均优于现有模型。此外，这种方法提供了一个新的视角来讨论不同输入数据对环境太阳风建模的作用，以及这将告诉我们有关底层物理过程的信息。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，可以预测地球上的环境太阳风。