Next Generation, Physics-Inspired AI for Space Weather Forecasting

用于空间天气预报的下一代物理启发人工智能

• 批准号: NE/W009129/1
• 负责人: Andrew Smith
• 金额: $ 66.3万
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FW009129%2F1
• 关键词: Next; Generation; Physics; Inspired; AI

Space weather describes the variability of conditions in near-Earth space. One of the primary ways in which space weather can impact society is through the generation of anomalous currents (termed Geomagnetically Induced Currents, or GICs) in power networks and pipelines on the ground. These GICs can accelerate the ageing of systems, or more critically lead to the immediate failure of components such as power transformers. This research will take a leap forward in understanding and predicting when we are at risk of suffering large GICs on the ground.GICs are driven by rapid changes in the Earth's magnetic field, and there are a range of phenomena in near-Earth space that are responsible, but one of the most important is the magnetospheric substorm. During a substorm, interactions between the magnetic field of the Earth and the incident solar wind results in the transfer of energy. This additional energy is principally stored in plasma and magnetic field energy on the nightside of a planet in a region known as the magnetotail. Energy is stored until the system reaches the limit of stability, at which point the energy is explosively released, again through the process of magnetic reconnection. This leads to observable phenomena such as the aurora. However, this process can have dire space weather consequences, causing extreme ionospheric currents and posing risks to satellites and other infrastructure, yet even our most sophisticated methods struggle to predict when it will occur.Understanding and forecasting magnetic field variability is a hugely difficult problem when the myriad of sporadic and localised processes at the start of a magnetosphere substorm are poorly understood. One of the fundamental issues is the scale of the system. The processes involved are sporadic and localised, and the domain in which they could operate is huge. The aim of this fellowship is to understand the processes and instabilities by which the magnetosphere becomes unstable, and use this to generate cutting-edge, physics-inspired space weather forecasting models.I will accurately and robustly process huge volumes of data from several missions at the Earth using 'big data' techniques to characterize and predict the conditions under which the substorm is likely to occur. I will develop Bayesian Monte Carlo methods to estimate their spatial and temporal scales and determine causality. I will then use this understanding to generate cutting-edge machine learning models of when and where substorms will occur, as well as the properties and location of the auroral oval. I will then put this together to create a physics-inspired model of forecasting geomagnetic perturbations. This is necessary to provide precise and reliable predictions of when regions are at risk of dangerous GICs. The physics-inspired process will ensure that the model extrapolations to extreme conditions are more reliable than 'black box' extrapolations.During the course of this fellowship I will collaborate with world leading experts on plasma stability (MSSL) and magnetotail dynamics (Michigan), utilizing cutting edge global models (Michigan) to inform state-of-the-art machine learning models. I will then create robust and reliable models for the benefit of stakeholders (Met Office).

空间天气描述了近地空间条件的变化。太空天气影响社会的主要方式之一是在地面电力网络和管道中产生异常电流（称为地磁感应电流，或 GIC）。这些 GIC 会加速系统的老化，或更严重的是导致电力变压器等组件立即失效。这项研究将在理解和预测我们何时面临地面上遭受大型 GIC 的风险方面迈出一大步。GIC 是由地球磁场的快速变化驱动的，近地空间中存在一系列现象，负责任的，但最重要的之一是磁层亚暴。在亚暴期间，地球磁场与入射太阳风之间的相互作用导致能量转移。这种额外的能量主要存储在行星背面磁尾区域的等离子体和磁场能量中。能量被存储，直到系统达到稳定极限，此时能量再次通过磁重联过程爆炸性释放。这导致了诸如极光之类的可观测现象。然而，这个过程可能会产生可怕的空间天气后果，导致极端的电离层电流并对卫星和其他基础设施构成风险，但即使我们最复杂的方法也很难预测它何时发生。当磁场变化发生时，理解和预测磁场变化是一个非常困难的问题。人们对磁层亚暴开始时的无数零星和局部过程知之甚少。基本问题之一是系统的规模。所涉及的流程是零星的和本地化的，并且它们可以操作的领域是巨大的。该奖学金的目的是了解磁层变得不稳定的过程和不稳定性，并利用它来生成尖端的、受物理启发的空间天气预报模型。我将准确而稳健地处理来自多个任务的大量数据地球利用“大数据”技术来描述和预测亚暴可能发生的条件。我将开发贝叶斯蒙特卡罗方法来估计它们的空间和时间尺度并确定因果关系。然后，我将利用这种理解来生成关于亚暴何时何地发生以及极光椭圆形的属性和位置的尖端机器学习模型。然后，我将把它们放在一起，创建一个预测地磁扰动的物理启发模型。这对于准确可靠地预测区域何时面临危险的 GIC 风险是必要的。受物理启发的过程将确保对极端条件的模型外推比“黑匣子”外推更可靠。在本研究期间，我将与等离子体稳定性（MSSL）和磁尾动力学（密歇根州）方面的世界领先专家合作，利用尖端的全球模型（密歇根州）来为最先进的机器学习模型提供信息。然后，我将创建强大且可靠的模型，以造福利益相关者（气象局）。

项目成果

Sudden Commencements and Geomagnetically Induced Currents in New Zealand: Correlations and Dependance

新西兰的突然开始和地磁感应电流：相关性和依赖性

• DOI: 10.1029/2023sw003731
• 发表时间: 2024
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: Smith A

Extreme Value Analysis of Ground Magnetometer Observations at Valentia Observatory, Ireland

爱尔兰瓦伦蒂亚天文台地面磁力计观测的极值分析

• DOI: 10.1029/2023sw003565
• 发表时间: 2023
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: Fogg A

Using machine learning to diagnose relativistic electron distributions in the Van Allen radiation belts

使用机器学习来诊断范艾伦辐射带中的相对论电子分布

• DOI: 10.1093/rasti/rzad035
• 发表时间: 2023
• 期刊: RAS Techniques and Instruments
• 作者: Killey S

MIST reunited

迷雾重聚

• DOI: 10.1093/astrogeo/atad022
• 发表时间: 2023
• 期刊: Astronomy & Geophysics
• 影响因子: 0.8
• 作者: Woodfield E

Automatic Encoding of Unlabeled Two Dimensional Data Enabling Similarity Searches: Electron Diffusion Regions and Auroral Arcs

• DOI: 10.1029/2023ja032096
• 发表时间: 2024-01-01
• 期刊: JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS
• 影响因子: 2.8
• 作者: Smith，A. W.;Rae，I. J.;Koul，A.
• 通讯作者: Koul，A.