论文标题
地磁风暴和k $ _ {\ textrm {p}} $ index的概率预测
Probabilistic Prediction of Geomagnetic Storms and the K$_{\textrm{p}}$ Index
论文作者
论文摘要
通常使用摘要指数来描述地磁活动，以总结太空天气影响的可能性以及在参数化空间天气模型时。地磁索引$ \ text {k} _ \ text {p} $尤其是用于这些目的的。当前的最新预测模型提供了确定性的$ \ text {k} _ \ text {p} $使用多种方法进行预测 - 包括经验衍生的功能，基于物理的模型和神经网络 - 但不提供与预测相关的不确定性估计。本文提供了一种示例方法，以生成3小时的$ \ text {k} _ \ text {p} $以不确定性界限进行预测，从此提供了概率的地磁风暴预测。具体来说，我们已经使用了两层体系结构来分别预测Storm（$ \ text {k} _ \ text {p} \ geq 5^ - $）和非稳定案例。由于太阳能风向模型的预测瞬态驱动活动的发作的能力受到限制，因此我们还使用太阳能X射线通量引入了模型变体，以评估包括太阳活动代理在内的简单模型是否可以改善与L1-to-Arthth Ex-Ad-Efrath Expagation Time更长的地磁风暴活动的预测。通过比较这些模型的性能，我们表明，包括有关太阳辐照度的操作信息的信息可以增强预测模型捕获地磁风暴发作的能力，并且可以实现这一点，同时还可以实现概率预测。
Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index $\text{K}_\text{p}$ in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic $\text{K}_\text{p}$ predictions using a variety of methods -- including empirically-derived functions, physics-based models, and neural networks -- but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead $\text{K}_\text{p}$ prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm ($\text{K}_\text{p}\geq 5^-$) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.
