A Gray-Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning
A Gray-Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning
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DOI：
10.1029/2019ja027684
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发表时间：
2020-11-01
影响因子：
2.8
通讯作者：
Toth, G.
中科院分区：
文献类型：
--
作者：
Camporeale, E.;Cash, M. D.;Toth, G.
我们提出了一种新的算法，预测的概率，地面磁场的水平分量的时间导数dB/dt超过一个指定的阈值在一个给定的位置。该量提供了与地磁感应电流（GIC）物理相关的重要信息，GIC是与空间天气事件引起的地球磁场突变相关的电流。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，我们结合联合收割机的密歇根大学的地球空间模型，是在国家海洋和大气管理局（NOAA）空间天气预报中心，与一个增强的分类树的合奏。我们讨论的问题，重新校准的决策树的输出，以获得可靠的概率。该模型的性能评估的概率预测的典型指标：检测概率和错误检测，真技能统计，海德克技能得分，和接收器操作特征曲线。我们表明，ML增强算法一致地提高了所有考虑的指标。
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.