We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field dB/dt exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to geomagnetically induced currents (GICs), which are electric currents associated with sudden changes in the Earth's magnetic field due to space weather events. The model follows a “gray‐box” approach by combining the output of a physics‐based model with machine learning. Specifically, we combine the University of Michigan's Geospace model that is operational at the National Oceanic and Atmospheric Administration (NOAA) Space Weather Prediction Center, with a boosted ensemble of classification trees. We discuss the problem of recalibrating the output of the decision tree to obtain reliable probabilities. The performance of the model is assessed by typical metrics for probabilistic forecasts: Probability of Detection and False Detection, True Skill Statistic, Heidke Skill Score, and Receiver Operating Characteristic curve. We show that the ML‐enhanced algorithm consistently improves all the metrics considered.

中文翻译：

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区域地面磁扰动概率估计的灰箱模型：通过机器学习增强NOAA操作地理空间模型

我们提出了一种新颖的算法，可以预测地磁场水平分量d B / d t的时间导数的概率在给定位置超过指定的阈值。该数量提供了与地磁感应电流（GIC）物理相关的重要信息，地磁感应电流是与由于太空天气事件导致的地球磁场突然变化相关的电流。该模型遵循“灰箱”方法，将基于物理的模型的输出与机器学习相结合。具体来说，我们将密歇根大学在国家海洋与大气管理局（NOAA）太空天气预报中心运行的地理空间模型与分类树的增强组合在一起。我们讨论了重新校准决策树的输出以获得可靠概率的问题。该模型的性能通过概率预测的典型指标进行评估：检测和错误检测的概率，真实技能统计信息，海德克技能得分和接收者操作特征曲线。我们证明了ML增强算法可以持续改善所有考虑的指标。