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

使用卫星数据和机器学习进行实时空间天气预报

• 批准号: 2607345
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2607345
• 关键词: Real; time; Space; Weather; forecasting

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.

随着社会越来越依赖地面技术和空间技术，例如电力网络，通信和GP，我们越来越受到太阳天气事件的破坏风险，以太阳能燃烧，冠状大规模弹射和地磁风暴的形式。 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世界各地的地磁观测器网络），太阳盘图像和来自地静止卫星的数据（例如，太阳能动态天文台以及太阳能和地球层观测值）。我们试图将这些信息结合起来，以提前1至72个小时的空间天气预测的实时自动化，以帮助减少对地面和空间基于空间的技术基础设施的危害。这将使用高级机器学习技术（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）来实现。地球磁场的模型，使用卫星数据（例如ESA的群体任务）构建的模型也将用于更好地了解太空天气影响的更广泛背景，例如，南大西洋异常地区对卫星的电子损害风险增加。