Real-time Space Weather forecasting using satellite data and machine learning

随着社会对诸如电力网络、通信和全球定位系统等地面和天基技术的日益依赖，我们越来越容易受到太阳耀斑、日冕物质抛射和地球磁暴等形式的空间天气事件的干扰。虽然空间中有一些实时监测系统测量太阳风及其磁场，但预测空间天气对地面技术和观测地球必不可少的卫星的影响仍然是问题。目的有许多潜在的信息来源可用于预测空间天气事件，包括人为预报(例如，空间天气预测中心和英国气象局空间天气业务中心发布的报告)、磁场测量(来自全球地磁观测站网络)、太阳圆盘图像和来自地球静止卫星(例如太阳动力学观测站和太阳和日球层观测站)的数据。我们寻求将这些信息结合起来，以改进空间天气预报的实时自动化，提前1至72小时，帮助减少空间天气对地面和天基技术基础设施的危害。这将使用先进的机器学习技术来实现，包括非线性时间序列预测、贝叶斯建模、文本挖掘和图像处理。还将利用卫星数据(例如欧空局的群星飞行任务)建立地球磁场模型，以更好地了解空间天气影响的更广泛背景，例如南大西洋异常区域内卫星受到电子损坏的风险增加。

With society's increasing reliance on ground- and space-based technology such as electricity networks, communications and GPS, we are ever more exposed to the risk of disruption from space weather events in the form of solar flares, coronal mass ejections and geomagnetic storms. Although there are a number of real-time monitoring systems in space measuring the solar wind and its magnetic field, forecasting the space weather impacts on both ground-based technology and the satellites essential for observing the Earth, remains problematic.AimsThere are many potential sources of information which could be used to predict a space weather event including human-made forecasts (e.g. reports issued by the Space Weather Prediction Centre, and the Met Office Space Weather Operations Centre), magnetic field measurements (from the worldwide network of geomagnetic observatories), solar disc imagery and data from geostationary satellites (e.g. the Solar Dynamic Observatory, and the Solar and Heliospheric Observatory). We seek to combine such information to improve the real-time automation of forecasts of space weather between 1 to 72 hours ahead of time, aiding efforts to reduce the space weather hazard to ground- and space-based technological infrastructure. This will be achieved using advanced machine learning techniques including nonlinear time series forecasting, Bayesian modelling, text mining, and image processing. Models of the Earth's magnetic field, built using satellite data (e.g. ESA's Swarm mission) will also be used to better understand the wider context for space weather impacts, for example, the increased risk of electronic damage to satellites in the region of the South Atlantic Anomaly.