DataSheet1_Forecasting changes of the magnetic field in the United Kingdom from L1 Lagrange solar wind measurements.PDF

Extreme space weather events can have large impacts on ground-based infrastructure important to technology-based societies. Machine learning techniques based on interplanetary observations have proven successful as a tool for forecasting global geomagnetic indices, however, few studies have examined local ground magnetic field perturbations. Nowcast and forecast models which predict the magnitude of the horizontal geomagnetic field, |BH|, and its time derivative, dBHdt, at ground level would be valuable for assessing the potential space weather hazard. We attempt to predict the variation of the magnetic field at the three United Kingdom observatories (Eskdalemuir, Hartland and Lerwick) driven by L1 solar wind parameters. The horizontal magnetic field component and its time derivative are predicted from solar wind plasma and interplanetary magnetic field observations using Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network-LSTM models. A 5-fold grid search cross-validation is used for tuning the hyperparameters in each model. Forecasts were made with 5, 15 and 30-min lead times. Models were trained and validated with geomagnetic storm-only data from 1997 to 2016; their outputs were evaluated with the 7–9th September 2017 storms. The forecast models are only able to predict the directly driven parts of geomagnetic storms (not the substorms) and LSTM models generally perform best. We find the |BH| 15- and 30-min forecasts at Lerwick and Eskdalemuir have some predictive power. The 5-min |BH| forecast as well as all the dBHdt models for Eskdalemuir and all the Hartland models were found to have little or no predictive power. This suggests that the machine learning models have better forecasting power at higher latitude (closer to the auroral zones), where the ground magnetic variation field is larger and during storm onset, which is directly driven by changes in the solar wind.

极端空间天气事件可对科技依赖型社会至关重要的地面基础设施造成严重影响。基于行星际观测的机器学习技术已被证实可有效用于全球地磁指数的预报，但目前鲜有研究针对局地地面磁场扰动展开探讨。可预报地面水平地磁场强度|BH|及其时间导数dBHdt的临近预报与预报模型，将对评估潜在空间天气灾害具有重要价值。本研究尝试基于L1太阳风参数，对英国三座观测台（埃斯代尔缪尔、哈特兰与勒威克）的磁场变化进行预报。本研究利用长短期记忆网络（Long Short-Term Memory, LSTM）与混合卷积神经网络-LSTM模型，基于太阳风等离子体及行星际磁场观测数据，对水平磁场分量及其时间导数进行预报。本研究采用5折网格搜索交叉验证对各模型的超参数进行调优。预报提前时效设置为5、15及30分钟。模型使用1997年至2016年的仅包含地磁暴事件的数据进行训练与验证，并以2017年9月7日至9日的地磁暴事件对模型输出开展评估。预报模型仅能预测地磁暴的直接驱动分量（而非亚暴分量），且长短期记忆网络模型整体表现最优。研究发现，在勒威克与埃斯代尔缪尔观测台，|BH|的15分钟和30分钟预报具备一定预测能力。埃斯代尔缪尔观测台的5分钟|BH|预报，以及所有针对哈特兰观测台的dBHdt模型，几乎不具备或完全不具备预测能力。这表明，在更靠近极光带的高纬度地区，以及受太阳风变化直接驱动的暴源起始阶段，当地面磁场变化幅度更大时，机器学习模型的预报能力更佳。