SWEEP: Space Weather Empirical Ensemble Package

SWEEP：空间天气经验系综包

• 批准号: ST/V00235X/1
• 负责人: Huw Morgan
• 金额: $ 59.27万
• 依托单位: Aberystwyth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FV00235X%2F1
• 关键词: SWEEP; Space; Weather; Empirical; Ensemble

The Sun's atmosphere flows into interplanetary space as the solar wind. Solar eruptions cause large variations in this flow that can cause huge disruption to Earth, economy and society. This disruption can be reduced given advance warning of severe space weather, and the main purpose of this project is to improve this warning system through forecasts.Space weather forecasting is a relatively new field, and the UK Met Office has a dedicated unit providing forecasts and alerts for various organisations. The forecasts are based on telescope measurements of the Sun and it's extended atmosphere (the corona). These data are continually monitored for large eruptive events which may impact Earth. The data also drive large-scale computational models of the solar wind, giving a forecast of conditions at Earth. This project is tasked with providing a new system that will run alongside the current system, and provide certain key improvements.The proposed project is called Space Weather Empirical Ensemble Package (SWEEP). SWEEP takes in data from multiple telescopes observing the Sun and the corona, and uses novel methods to create multiple three-dimensional maps of the coronal magnetic field, the coronal density, and of coronal mass ejections (CMEs). Thus there are multiple, independent streams of information based on different types of data and methods. These multiple streams are used to drive a highly-efficient model of the solar wind, giving multiple forecasts at Earth. SWEEP will compare these multiple forecasts against recent measurements of the solar wind near Earth, calculating a 'skill score', which is a measure of each forecast's performance. Certain forecast streams may perform better than others under different conditions. The skill scores are then used to combine the individual forecast streams into a final main forecast. This approach has the advantage of being modular. If one telescope fails to collect data, the other forecast streams continue to operate - the system is therefore robust.SWEEP uses a highly-efficient solar wind model - several orders of magnitude faster than the current model. This allows an 'ensemble' approach to forecasting. We can run the model several hundred times, and for each instance apply random small variations on the data fed into the model near the Sun. This leads to a large set, or an ensemble, of forecasts near Earth. The final output can then be given as the most probable forecast, with an estimate of the forecast uncertainty. Unlike Earth weather forecasting, where there are dozens, or hundreds, of weather stations sampling Earth's atmosphere directly, telescopes that observe the Sun and corona are few and expensive. The properties of the Sun's atmosphere must be indirectly inferred from the telescope remote data. Any new information that we can extract from these data is valuable, and may improve current forecasting performance without the cost of building new telescopes. SWEEP exploits several new analysis tools and models of the solar corona, some of which have not previously been used for forecasting. SWEEP will be a fully automated system, which is another improvement from the current system. The modular basis also allows for future developments - it will be straightforward to add other forecast streams as new telescopes come online in future years, or to incorporate newly-developed data analysis methods or models.This initial 2.5 year project is dedicated to adapting and combining existing tools into a fully functional SWEEP. By the end of the project it will be fully operational at the Met Office. Over the next few decades, we hope that SWEEP will be a key forecasting tool that helps protect society from the damage of space weather.

太阳的大气作为太阳风流入星际空间。太阳爆发会导致这种流动的巨大变化，可能会对地球，经济和社会造成巨大破坏。鉴于对严重的太空天气的提前警告，可以减少这种干扰，而该项目的主要目的是通过预报来改善此警告系统。空间天气预报是一个相对较新的领域，英国大都会公司的专用部门为各种组织提供了预测和警报。预测是基于太阳的望远镜测量值及其扩展的大气（电晕）。对于可能影响地球的大型喷发事件，对这些数据进行了不断监测。数据还驱动了太阳风的大规模计算模型，从而预测了地球条件的预测。该项目的任务是提供一个将与当前系统一起运行的新系统，并提供某些关键的改进。拟议的项目称为Space Weather Adey经验合奏软件包（SWEEP）。扫荡从观察太阳和电晕的多个望远镜中获取数据，并使用新颖的方法创建了冠状磁场的多个三维图，冠状密度和冠状质量弹出（CMES）。因此，基于不同类型的数据和方法，有多个独立的信息流。这些多个流用于驱动太阳风的高效模型，从而在地球上提供了多个预测。扫描将将这些多个预测与地球附近太阳风的最新测量值进行比较，从而计算“技能得分”，这是对每个预测性能的衡量标准。在不同条件下，某些预测流的性能可能比其他预测流更好。然后，该技能分数用于将单个预测流组合到最终的主要预测中。这种方法具有模块化的优点。如果一个望远镜未能收集数据，另一个预测流仍在继续运行 - 因此，系统是强大的。Sheep使用高效的太阳风模型 - 比当前模型快几个数量级。这允许“合奏”方法进行预测。我们可以运行几百次模型，并且对于每个实例，对馈送到太阳附近的模型的数据进行随机小变化。这导致了地球附近的大型预测或合奏。然后可以将最终输出作为最可能的预测，并估计预测不确定性。与地球天气预报不同，直接有数十个或数百个气象站直接采样地球大气层，观察到太阳和电晕的望远镜很少而且很昂贵。必须从望远镜远程数据中间接推断出太阳大气的特性。我们可以从这些数据中提取的任何新信息都是有价值的，并且可以改善当前的预测性能，而无需构建新望远镜。扫描利用了太阳能电晕的几种新分析工具和模型，其中一些以前尚未用于预测。扫描将是一个完全自动化的系统，这是当前系统的另一个改进。模块化的基础还允许将来的发展 - 随着新望远镜在未来的几年在线或结合新开发的数据分析方法或模型，添加其他预测流是很简单的。本初始的2.5年项目专门用于调整现有工具并将现有工具组合到功能齐全的范围内。到项目结束时，它将在大都会办公室全面运作。在接下来的几十年中，我们希望扫描将成为有助于保护社会免受太空天气损害的关键预测工具。

项目成果

Constraints on Solar Wind Density and Velocity Based on Coronal Tomography and Parker Solar Probe Measurements

基于日冕层析成像和帕克太阳探测器测量的太阳风密度和速度约束

• DOI: 10.3847/1538-4357/ad1506
• 发表时间: 2024
• 期刊: The Astrophysical Journal
• 作者: Bunting K

Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

• DOI: 10.3389/fspas.2020.571186
• 发表时间: 2020-12
• 作者: M. Korsós;R. Erdélyi;Jiajia Liu;H. Morgan

A solar-cycle study of coronal rotation: large variations, rapid changes, and implications for solar wind models

日冕旋转的太阳周期研究：大变化、快速变化以及对太阳风模型的影响

• DOI: 10.48550/arxiv.2203.03447
• 发表时间: 2022
• 作者: Edwards L

Magnetic Helicity Flux Oscillations in the Atmospheres of Flaring and Nonflaring Active Regions

耀斑和非耀斑活跃区大气中的磁螺旋通量振荡

• DOI: 10.3847/1538-4357/ac7469
• 发表时间: 2022
• 期刊: The Astrophysical Journal
• 作者: Korsós M

An Empirical Relationship Between Coronal Density and Solar Wind Velocity in the Middle Corona With Applications to Space Weather

中日冕中日冕密度与太阳风速之间的经验关系及其在空间天气中的应用

• DOI: 10.1029/2023sw003448
• 发表时间: 2023
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: Bunting K