SWQU: NextGen Space Weather Modeling Framework Using Data, Physics and Uncertainty Quantification

SWQU：使用数据、物理和不确定性量化的下一代空间天气建模框架

• 批准号: 2027555
• 负责人: Gabor Toth
• 金额: $ 286万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027555&HistoricalAwards=false
• 关键词: SWQU; NextGen; Space; Weather; Modeling

This project will initiate development of the NextGen Space Weather Modeling Framework (NextGen SWMF) by bringing together experts in space and plasma physics, data assimilation, uncertainty quantification and cyberinfrastructure. Space weather results from solar activity that can impact the space environment of the Earth and damage our technological systems as well as expose pilots and astronauts to harmful radiation. Extreme events could knock out the power grid with a recovery time of months and cause about $2 trillion damage. Much of the impacts can be avoided or mitigated by timely and reliable space weather forecast. The NextGen Space Weather Modeling Framework will employ computational models from the surface of the Sun to the surface of Earth in combination with assimilation of observational data to provide optimal probabilistic space weather forecasting. The model will run efficiently on the next generation of supercomputers to predict space weather about one day or more before the impact occurs. The project will concentrate on forecasting major space weather events generated by coronal mass ejections.Current space weather prediction employs first-principles and/or empirical models. While these provide useful information, their accuracy, reliability and forecast window need major improvements. Data assimilation has the potential to significantly improve model performance, as has been successfully done in terrestrial weather forecast. However, to allow for the sparsity of satellite observations, different data assimilation methods have to be employed. NextGen SWMF model will start from the Sun with an ensemble of simulations that span the uncertain observational and model parameters. Using real time and past observations, the model will strategically down-select to a high performing subset. Next, the down-selected ensemble will be extended by varying uncertain parameters and the simulation continued to the next data assimilation point. The final ensemble will provide a probabilistic forecast of the space weather impacts. Finding the optimal algorithm that produces the best prediction with minimal uncertainty is a complex and very challenging task that requires developing, implementing and perfecting novel data assimilation and uncertainty quantification methods. To make these ensemble simulations run faster than real time, the most expensive parts of the model need to run efficiently on the current and future supercomputers, which employ graphical processing units (GPUs) in addition to the traditional multi-core CPUs. The main product of this project will be the Michigan Sun-To-Earth Model with Quantified Uncertainty and Data Assimilation (MSTEM-QUDA).This award is made as a part of the joint NSF-NASA pilot program on Next Generation Software for Data-driven Models of Space Weather with Quantified Uncertainties (SWQU). It is supported by NSF Divisions of Astronomical Sciences, Atmospheric and Geospace Sciences, Mathematical Sciences, and Physics. All software developed as a result of this award will be made available by the awardee free of charge for non-commercial use; the software license will permit modification and redistribution of the software free of charge for non-commercial use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将通过汇集空间和等离子体物理学、数据同化、不确定性量化和网络基础设施方面的专家，启动下一代空间气象建模框架的开发工作。 太空天气是太阳活动造成的，它会影响地球的太空环境，损害我们的技术系统，并使飞行员和宇航员暴露在有害辐射中。极端事件可能会在数月的恢复时间内摧毁电网，并造成约2万亿美元的损失。通过及时和可靠的空间气象预报，可以避免或减轻许多影响。 下一代空间气象建模框架将采用从太阳表面到地球表面的计算模型，并结合观测数据的同化，以提供最佳概率空间气象预报。该模型将在下一代超级计算机上高效运行，以预测撞击发生前一天或更长时间的空间天气。该项目将侧重于预测由日冕物质抛射产生的重大空间气象事件。虽然这些数据提供了有用的信息，但其准确性、可靠性和预报窗口需要重大改进。数据同化有可能大大提高模式的性能，正如在陆地天气预报中所成功做到的那样。然而，考虑到卫星观测的稀疏性，必须采用不同的数据同化方法。NextGen SWMF模型将从太阳开始，模拟集合涵盖不确定的观测和模型参数。使用真实的时间和过去的观察，模型将策略性地向下选择到高性能子集。接下来，向下选择的集合将通过改变不确定参数来扩展，并且模拟继续到下一个数据同化点。最后的集合将提供空间气象影响的概率预报。寻找最佳算法，以最小的不确定性产生最好的预测是一个复杂的和非常具有挑战性的任务，需要开发，实施和完善新的数据同化和不确定性量化方法。为了使这些集成模拟运行速度超过真实的时间，模型中最昂贵的部分需要在当前和未来的超级计算机上高效运行，这些超级计算机除了传统的多核CPU之外还使用图形处理单元（GPU）。该项目的主要产品将是密歇根州的太阳到地球模型与量化的不确定性和数据同化（MSTEM-QUDA）。该奖项是美国国家科学基金会-美国宇航局联合试点计划的一部分，用于下一代软件数据驱动模型的空间天气与量化的不确定性（SWQU）。 它得到了NSF天文科学、大气和地球空间科学、数学科学和物理学部门的支持。 所有因该奖项而开发的软件将由获奖者免费提供用于非商业用途;软件许可证将允许免费修改和重新分发软件用于非商业用途。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Three‐Dimensional Structure of the Corona During WHPI Campaign Rotations CR‐2219 and CR‐2223

WHPI 活动轮换 CR-2219 和 CR-2223 期间日冕的三维结构

• DOI: 10.1029/2022ja030406
• 发表时间: 2022
• 期刊: Journal of Geophysical Research: Space Physics
• 作者: Lloveras, D. G.;Vásquez, A. M.;Nuevo, F. A.;Frazin, R. A.;Manchester, W.;Sachdeva, N.;Van der Holst, B.;Lamy, P.;Gilardy, H.
• 通讯作者: Gilardy, H.

New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines

• DOI: 10.1029/2021sw002928
• 发表时间: 2022-07
• 期刊: Space Weather
• 作者: Daniel Iong;Yang Chen;G. Tóth;S. Zou;Tuija Pulkkinen;Jiaen Ren;E. Camporeale;T. Gombosi

Simulating Solar Maximum Conditions Using the Alfvén Wave Solar Atmosphere Model (AWSoM)

使用阿尔文波太阳大气模型 (AWSoM) 模拟太阳极大值条件

• DOI: 10.3847/1538-4357/ac307c
• 发表时间: 2021
• 期刊: The Astrophysical Journal
• 作者: Sachdeva, Nishtha;Tóth, Gábor;Manchester, Ward B.;van der Holst, Bart;Huang, Zhenguang;Sokolov, Igor V.;Zhao, Lulu;Shidi, Qusai Al;Chen, Yuxi;Gombosi, Tamas I.
• 通讯作者: Gombosi, Tamas I.

Tomography of the Solar Corona with the Metis Coronagraph I: Predictive Simulations with Visible-Light Images

使用 Metis Coronagraph I 进行日冕层析成像：使用可见光图像进行预测模拟

• DOI: 10.1007/s11207-022-02047-9
• 发表时间: 2022
• 期刊: Solar Physics
• 影响因子: 2.8
• 作者: Vásquez, Alberto M.;Nuevo, Federico A.;Frassati, Federica;Bemporad, Alessandro;Frazin, Richard A.;Romoli, Marco;Sachdeva, Nishtha;Manchester, Ward B.
• 通讯作者: Manchester, Ward B.

Magnetohydrodynamic with Adaptively Embedded Particle-in-Cell model: MHD-AEPIC

• DOI: 10.1016/j.jcp.2021.110656
• 发表时间: 2021-08
• 期刊: J. Comput. Phys.
• 作者: Y. Shou;V. Tenishev;Yuxi Chen;G. Tóth;N. Ganushkina