Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning
Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning
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DOI：
10.1029/2020sw002603
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发表时间：
2020-10
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通讯作者：
A. Smith;I. J. Rae;C. Forsyth;D. Oliveira;M. Freeman;D. Jackson
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作者：
A. Smith;I. J. Rae;C. Forsyth;D. Oliveira;M. Freeman;D. Jackson
在这项研究中，我们调查了几种不同的机器学习模型的能力，以提供概率预测，即在L1处观测到的地球上游的星际激波是否会导致立即(突然开始，SC)或更持久的磁层活动(风暴突然开始，SSCS)。四个模型被测试，包括线性(Logistic回归)、非线性(朴素贝叶斯和高斯过程)和集成(随机森林)模型，并被证明提供了熟练和可靠的预测能力与Brier技能分数(BSS)的∼0.3和ROC0.8分。最强大的预测参数被发现是行星际磁场的范围。这些模型也提供了对SSC的巧妙预测，尽管可靠性不如对SSC的预测。返回的BSS和ROC得分分别为∼0.21和0.82。对于这些预测，最重要的参数被发现是观测到的最小BZ。对激波的简单参数化进行了测试，包括了与磁层指数及其在激波撞击期间的变化有关的附加特征，从而适度增加了可靠性。一些参数，如速度和密度，可能能够在较长的提前时间内更准确地预测，例如，从日球层图像。当输入限于速度和密度时，模型被发现在预测SSC方面表现良好，尽管可靠性低于以前(BSS∼0.16，ROC Score∼0.8)，最后，使用当前观测之外的假设极端数据对模型进行测试，显示出显著不同的外推。
In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ∼0.21 and 0.82, respectively. The most important parameter for these predictions was found to be the minimum observed BZ. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, for example, from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ∼ 0.16, ROC Scores ∼ 0.8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations.