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.

太阳的大气层以太阳风的形式流入行星际空间。太阳爆发导致这种流动的巨大变化，可能对地球，经济和社会造成巨大的破坏。如果能对严重的空间天气发出预警，这种干扰就可以减少，而该项目的主要目的是通过预报来改进这种预警系统。空间天气预报是一个相对较新的领域，英国气象局有一个专门的单位为各种组织提供预报和警报。这些预测是基于望远镜对太阳及其扩展大气（日冕）的测量。这些数据不断监测可能影响地球的大型喷发事件。这些数据还驱动了太阳风的大规模计算模型，预测了地球的状况。该项目的任务是提供一个新的系统，该系统将与现有系统一起运行，并提供某些关键改进，拟议的项目称为空间气象经验包（SWEEP）。SWEEP从观测太阳和日冕的多个望远镜中获取数据，并使用新方法创建日冕磁场，日冕密度和日冕物质抛射（CME）的多个三维地图。因此，存在基于不同类型的数据和方法的多个独立的信息流。这些多股流被用来驱动一个高效的太阳风模型，在地球上给出多个预测。SWEEP将把这些多个预测与最近对地球附近太阳风的测量结果进行比较，计算出一个“技能分数”，这是衡量每个预测表现的一个指标。在不同的条件下，某些预测流可能比其他预测流表现得更好。然后，技能分数用于将各个预测流联合收割机组合成最终的主预测。这种方法具有模块化的优点。如果一台望远镜无法收集数据，其他预报流将继续运行--因此该系统非常强大。SWEEP使用高效的太阳风模型--比当前模型快几个数量级。这允许一种“集合”的预测方法。我们可以运行这个模型几百次，并对每一次运行都在太阳附近对输入模型的数据进行随机的微小变化。这导致了一个大的集合，或一个集合，在地球附近的预测。最后的输出可以作为最可能的预测，并估计预测的不确定性。与地球天气预报不同，有几十个或几百个气象站直接对地球大气进行采样，观测太阳和日冕的望远镜很少而且昂贵。太阳大气层的性质必须从望远镜的遥感数据中间接推断出来。我们可以从这些数据中提取的任何新信息都是有价值的，并且可以在不花费建造新望远镜的成本的情况下提高当前的预测性能。SWEEP利用了几种新的日冕分析工具和模型，其中一些以前从未用于预测。SWEEP将是一个完全自动化的系统，这是对现有系统的又一次改进。模块化的基础还允许未来的发展-随着未来几年新望远镜的上线，它将直接添加其他预测流，或者将新开发的数据分析方法或模型纳入其中。这个最初的2.5年项目致力于将现有工具调整和组合成功能齐全的SWEEP。到项目结束时，它将在气象局全面运作。在未来几十年里，我们希望SWEEP将成为一个关键的预报工具，帮助保护社会免受空间天气的损害。

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