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”描述了在几秒钟到几天内发生的太阳磁场的变化。它可能会损害空间和地面技术，尤其是电源，通信和地球观察系统。为了预测超过1小时的警告时间的空间天气，有必要准确预测太阳风，材料的持续流远离太阳填充太阳系。目前，对太阳表面的望远镜观察用于为太阳风的计算机模拟提供起始条件。这些模拟从太阳到地球一直传播条件，可以估计太空撞击。正在进行持续的努力来改善太阳风模拟，并对太阳附近的太阳风进行更准确的测量。但是，航天器还常规地对远离太阳的太阳风进行直接测量，这提供了目前不用于改善预测的有用的其他信息。陆地天气预测的经验表明，通过使用可用的观测值定期将计算机模拟回到现实中，可以实现预测能力的最大进步。该观察性的计算机模型被称为“数据同化”（DA），这是现代天气预测的核心。准确的天气预报提前时间已经发展了大约一天，这主要是由于DA的进步。鉴于这一成功，是时候完全探索太空天气的DA功能，尤其是太阳风。我们的小组最近在该领域进行了初步研究。拟议的工作将基于此基础来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进行了基于物理的操作预测模拟。这是将DA应用于太阳风的首次努力，其方式与地面数值预测相当。然而，太阳风与大气和其他地球物理系统的不同，以多种基本方式，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。除了改善空间天气的预测外，SWDA系统还将实现最先进的空间天气研究。测试SWDA系统的一种副产品是，我们将结合模型和观测值，以产生最准确的估计值，以归结为太阳风条件的日期，在太阳附近，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性结构与不同的太阳风条件有关，这是对太阳风形成的理论模型的直接观察测试。 SWDA还将用于确定理想情况下，我们将在太阳风中定位航天器，以便对太空天气预测进行最大的改进。这将为未来的空间天气任务设计设计。

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

Ensemble CME Modeling Constrained by Heliospheric Imager Observations

• DOI: 10.1029/2020av000214
• 发表时间: 2020-09
• 期刊: AGU Advances
• 影响因子: 8.4
• 作者: L. Barnard;M. J. Owens;C. Scott;C. A. D. Koning