Geomagnetic storms resulting from high‐speed streams can have significant negative impacts on modern infrastructure due to complex interactions between the solar wind and geomagnetic field. One measure of the extent of this effect is the Kyoto Dst index. We present a method to predict Dst from data measured at the Lagrange 5 (L5) point, which allows for forecasts of solar wind development 4.5 days in advance of the stream reaching the Earth. Using the STEREO‐B satellite as a proxy, we map data measured near L5 to the near‐Earth environment and make a prediction of the Dst from this point using the Temerin‐Li Dst model enhanced from the original using a machine learning approach. We evaluate the method accuracy with both traditional point‐to‐point error measures and an event‐based validation approach. The results show that predictions using L5 data outperform a 27‐day solar wind persistence model in all validation measures but do not achieve a level similar to an L1 monitor. Offsets in timing and the rapidly changing development of B_z in comparison to B_x and B_y reduce the accuracy. Predictions of Dst from L5 have a root‐mean‐square error of 9 nT, which is double the error of 4 nT using measurements conducted near the Earth. The most useful application of L5 measurements is shown to be in predicting the minimum Dst for the next 4 days. This method is being implemented in a real‐time forecast setting using STEREO‐A as an L5 proxy and has implications for the usefulness of future L5 missions.

中文翻译：

---

使用L5的原位测量预测太阳最小期间的Dst

由于太阳风和地磁场之间复杂的相互作用，高速流引起的地磁风暴可能对现代基础设施产生重大负面影响。衡量这种影响程度的一种方法是京都D s t指数。我们提出了一种根据拉格朗日5号（L5）点测得的数据预测D s t的方法，该方法可以在水流到达地球之前4.5天预测太阳风的发展。使用STEREO-B卫星作为代理，我们将L5附近测得的数据映射到近地环境，并使用Temerin-Li D s t对此点进行D s t的预测。使用机器学习方法从原始模型中增强模型。我们使用传统的点对点误差测量和基于事件的验证方法来评估方法的准确性。结果表明，使用L5数据进行的预测在所有验证措施中均优于27天的太阳风持久性模型，但未达到与L1监测器相似的水平。与B _x和B _y相比，时序偏移和B _z的快速变化发展会降低精度。D s t的预测L5的均方根误差为9 nT，这是在地球附近进行测量时误差4 nT的两倍。研究表明，L5测量最有用的应用是预测接下来4天的最小D s t。该方法正在使用STEREO-A作为L5代理的实时预测环境中实施，这对未来L5任务的有用性产生了影响。