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).

太空天气描述了近地空间中条件的可变性。太空天气会影响社会的主要方式之一是通过在地面上的电力网络和管道中产生异常电流（称为地磁诱导的电流或GICS）。这些GIC可以加速系统的衰老，或更严重地导致组件（例如电源变压器）的立即失败。这项研究将在理解和预测我们何时有遭受大型基因遭受大型基因的风险时取得飞跃。GICS是由地球磁场的快速变化驱动的，并且在近地球空间中有一系列现象是负责的，但最重要的是磁层替代物。在实体过程中，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等离子体和磁场能量中的磁场夜边，该区域被称为磁尾。存储能量，直到系统达到稳定性的极限，此时，通过磁重新连接的过程，能量再次爆炸。这导致可观察到的现象，例如极光。但是，这个过程可能会带来可怕的空间天气后果，导致极端的电离层电流，并为卫星和其他基础设施带来风险，但即使是我们最复杂的方法也很难预测何时发生。当磁场变化和预测磁场可变性是一个非常困难的问题时，当零星和本地化的过程中，在一个不足的过程中，它是一个不错的替代方法。基本问题之一是系统的规模。所涉及的过程是零星和局部化的，他们可以运行的域是巨大的。该团契的目的是了解磁层变得不稳定的过程和不稳定性，并使用它来产生尖端的，物理启发的太空天气预报模型。我将准确，稳健地处理大量数据，从地球上使用“大数据”技术来表征和预测情况。我将开发贝叶斯蒙特卡洛方法来估计其空间和时间尺度并确定因果关系。然后，我将使用这种理解来生成何时和何处发生何时何地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，以创建一个以物理风格的预测地磁扰动模型。这对于提供何时面临危险GIC的风险的精确和可靠的预测是必要的。由物理启发的过程将确保对极端条件的模型推断比“黑匣子”外推更可靠。在此奖学金的过程中，我将与世界领先的等离子体稳定性（MSSL）和Magnetotail Dynamilics（Michigan）合作，利用尖端的全球模型（Michigan）来了解状态机器的机器学习模型。然后，我将创建强大而可靠的模型，以使利益相关者（Met Office）受益。

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

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.

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

Extreme Birkeland Currents Are More Likely During Geomagnetic Storms on the Dayside of the Earth

在地球白天的地磁风暴期间更有可能出现极端伯克兰电流

• DOI: 10.1029/2023ja031946
• 发表时间: 2023
• 期刊: Space Physics
• 作者: Coxon J