Solar wind data assimilation - maximising the accuracy of space-weather forecasting

太阳风数据同化 - 最大限度地提高空间天气预报的准确性

• 批准号: NE/S010033/1
• 负责人: Mathew Owens
• 金额: $ 45.6万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS010033%2F1
• 关键词: Solar; wind; data; assimilation; maximising

"Space weather" describes changes in the Sun's magnetic field which occur over seconds to days. It can damage space- and ground-based technologies, particularly power, communication and Earth-observation systems. In order to forecast space weather with more than about 1 hour of warning time, it is necessary to accurately forecast the solar wind, the continual flow of material away from the Sun which fills the solar system. At present, telescopic observations of the Sun's surface are used to provide the starting conditions for computer simulations of the solar wind. These simulations propagate conditions all the way from the Sun to Earth, where the space-weather impact can be estimated. There are ongoing efforts to improve solar wind simulations and to make more accurate measurements of the solar wind near the Sun. But spacecraft also routinely make direct measurements of the solar wind far from the Sun, which provide useful additional information that is not presently used to improve forecasts. Experience from terrestrial weather prediction shows that the biggest advance in forecasting ability can be achieved by using the available observations to regularly "nudge" the computer simulations back towards reality.This observational "nudging" of computer models is called "data assimilation" (DA), and it is at the heart of modern weather forecasting. Accurate weather forecast lead times have advanced about a day a decade, mainly due to advances in DA. Given this success, it is time to fully explore DA capabilities for space weather, in particular the solar wind. Our group has recently made preliminary studies in this area. The proposed work will build on this to develop and test the first ever solar wind data assimilation (SWDA) system using a physics-based, operational forecast simulation of the solar wind. This represents the first effort to apply DA to the solar wind in a manner comparable to terrestrial numerical weather prediction. The solar wind, however, differs from the atmosphere and other geophysical systems in a number of fundamental ways, thus adapting existing DA techniques will involve overcoming a number of scientific challenges. This will form the core science of the proposed work.In addition to improving space-weather forecasting, the SWDA system will enable cutting-edge space-weather research. One by-product of testing the SWDA system is that we will combine models and observations to produce the most accurate estimate to date of the solar wind conditions back near the Sun, where we are unable to directly make measurements. This will help us to understand which magnetic structures on the Sun are related to different solar wind conditions, serving as a direct observational test for theoretical models of solar wind formation. The SWDA will also be used to determine where, ideally, we would position spacecraft in the solar wind in order to make the biggest improvements to space-weather forecasting. This will inform the design of future space-weather mission design.

“太空天气”描述了太阳磁场在几秒钟到几天内发生的变化。它可以破坏空间和地面技术，特别是电力、通信和地球观测系统。为了预测超过1小时的预警时间的空间天气，有必要准确预测太阳风，即充满太阳系的远离太阳的物质的持续流动。目前，太阳表面的望远镜观测被用来为计算机模拟太阳风提供初始条件。这些模拟将条件从太阳一直传播到地球，在那里可以估计空间天气的影响。目前正在努力改进太阳风模拟，并对太阳附近的太阳风进行更准确的测量。但航天器也经常对远离太阳的太阳风进行直接测量，这提供了有用的额外信息，目前还没有用于改善预测。地面天气预报的经验显示，利用现有的观测数据，定期将计算机模拟的结果“推”回现实，可大大提高预报能力。这种对计算机模式的观测“推”称为“数据同化”，是现代天气预报的核心。准确的天气预报提前时间大约每十年提前一天，主要是由于DA的进步。鉴于这一成功，现在是时候充分探索空间天气，特别是太阳风的DA能力了。我们小组最近在这方面进行了初步研究。拟议的工作将在此基础上开发和测试有史以来第一个太阳风数据同化（SWDA）系统，该系统使用基于物理的太阳风业务预报模拟。这是第一次尝试将DA应用于太阳风，其方式可与陆地数值天气预报相媲美。然而，太阳风在许多基本方面与大气和其他地球物理系统不同，因此调整现有的DA技术将涉及克服许多科学挑战。这将构成拟议工作的核心科学，除了改进空间气象预报外，SWDA系统将使尖端空间气象研究成为可能。测试SWDA系统的一个副产品是，我们将结合联合收割机模型和观测结果，以产生迄今为止对太阳附近太阳风状况的最准确估计，在那里我们无法直接进行测量。这将有助于我们了解太阳上的哪些磁结构与不同的太阳风条件有关，为太阳风形成的理论模型提供直接的观测测试。SWDA还将用于确定在理想情况下，我们将在太阳风中放置航天器，以便最大限度地改善空间天气预报。这将为未来空间气象使命的设计提供信息。

项目成果

Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data

• DOI: 10.1029/2021sw002841
• 发表时间: 2021-07
• 期刊: Space Weather
• 作者: L. Barnard;M. Owens;C. Scott;M. Lockwood;C. A. de Koning;T. Amerstorfer;J. Hinterreiter;C. Moestl;J. Davies;P. Riley

Improving CME modelling with data assimilation of Heliospheric Imager observations into the HUXt solar wind numerical model.

通过将日光层成像仪观测数据同化到 HUXt 太阳风数值模型中，改进 CME 建模。

• DOI: 10.5194/egusphere-egu21-192
• 发表时间: 2021
• 作者: Barnard L

Using the "Ghost Front" to Predict the Arrival Time and Speed of CMEs at Venus and Earth

使用“幽灵前线”预测日冕物质抛射到达金星和地球的时间和速度

• DOI: 10.3847/1538-4357/aba95a
• 发表时间: 2020
• 期刊: Astrophysical Journal
• 影响因子: 4.9
• 作者: Chi Yutian;Scott Christopher;Shen Chenglong;Owens Mathew;Lang Matthew;Xu Mengjiao;Zhong Zhihui;Zhang Jie;Wang Yuming;Lockwood Mike
• 通讯作者: Lockwood Mike

Sensitivity of model estimates of CME propagation and arrival time to inner boundary conditions

CME 传播和到达时间的模型估计对内部边界条件的敏感性

• DOI: 10.1002/essoar.10512439.1
• 发表时间: 2022
• 作者: James L

Quantifying the uncertainty in CME kinematics derived from geometric modelling of Heliospheric Imager data

量化由日光层成像仪数据的几何建模得出的日冕物质抛射运动学的不确定性

• DOI: 10.1002/essoar.10507552.1
• 发表时间: 2021
• 作者: Barnard L