SWQU: Improving Space Weather Predictions with Data-Driven Models of the Solar Atmosphere and Inner Heliosphere

SWQU：利用太阳大气层和内日光层的数据驱动模型改进空间天气预报

• 批准号: 2028154
• 负责人: Nikolai Pogorelov
• 金额: $ 79.96万
• 依托单位: University of Alabama in Huntsville
• 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=2028154&HistoricalAwards=false
• 关键词: SWQU; Improving; Space; Weather; Predictions

Solar wind, a stream of charged particles emitted from the Sun, is a key driver of space weather at Earth and throughout the solar system. Extreme space weather events occur when disturbances in the Sun’s atmosphere, called coronal mass ejections (CMEs), reach the Earth’s magnetosphere. Space weather phenomena can create conditions hazardous for humans and instruments in space and on the ground. Accurately forecasting space weather is thus increasingly important for our technology-dependent society and will be critical while planning and operating missions to the Moon and Mars. This project will develop a new generation of software capable of near real-time modeling from the Sun to Earth's orbit (inner heliosphere) and predicting intense space weather events. The tools developed by this project can not only dramatically improve the accuracy and performance of currently operational space weather models, but also allow the broader scientific community to experiment with and extend these tools to create new capabilities that could eventually be transformational for operational activity. This work will also provide a leap forward in the computation and simulation of complex, turbulent plasma systems and is expected to have impact in several areas, including space physics and astrophysics. The project team includes both early-career and senior researchers at U.S. universities, NASA centers, national labs, and in the private sector; support for the non-academic collaborating institutions is to be provided by NASA.The structuring of the solar wind into fast and slow streams is the source of recurrent geomagnetic activity. The largest geomagnetic storms are caused by CMEs propagating through and interacting with the solar wind. The connection of the interplanetary magnetic field to CME-related shocks and impulsive solar flares determines where solar energetic particles propagate. Therefore, data-driven modeling of stream interactions in the background solar wind, and CMEs propagating through it, is a necessary part of space weather forecasting. At present NOAA Space Weather Prediction Center forecasts the background solar wind and CME arrival times using empirically driven models. The goal of this project is to develop a data-driven, time-dependent model that will improve the current state of the art. The new model will consist of: 1) a surface flux transport model, 2) potential field solver, and 3) an MHD solar wind model. It will provide more accurate solutions and be scalable on massively parallel computing systems, including Graphic Processor Units. Products from this project will provide a leap forward in the computation and simulation of complex plasma systems involving multiple discontinuities. The developed software will also be useful for astrophysical problems possessing a distinct spherical geometry, including exoplanets, early sun, and sun-like stars. 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). 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.

太阳风，一种从太阳发射的带电粒子流，是地球和整个太阳系空间天气的关键驱动因素。当太阳大气中的扰动，即日冕物质抛射（cme）到达地球磁层时，就会发生极端的太空天气事件。空间天气现象会对空间和地面上的人类和仪器造成危险。因此，对于我们这个依赖技术的社会来说，准确预测太空天气变得越来越重要，在规划和执行月球和火星任务时也将至关重要。该项目将开发新一代软件，能够从太阳到地球轨道（内日球层）进行近实时建模，并预测强烈的太空天气事件。该项目开发的工具不仅可以极大地提高当前操作空间天气模型的准确性和性能，而且还允许更广泛的科学界对这些工具进行实验和扩展，以创造最终可能为操作活动带来变革的新能力。这项工作还将为复杂的湍流等离子体系统的计算和模拟提供一个飞跃，并有望在包括空间物理学和天体物理学在内的几个领域产生影响。项目团队包括美国大学、NASA中心、国家实验室和私营部门的早期和高级研究人员；对非学术合作机构的支持将由NASA提供。将太阳风分成快流和慢流的结构是周期性地磁活动的来源。最大的地磁风暴是由cme通过太阳风传播并与太阳风相互作用引起的。行星际磁场与日冕物质抛射相关的冲击和脉冲太阳耀斑的联系决定了太阳高能粒子的传播方向。因此，数据驱动的背景太阳风流相互作用模型，以及通过它传播的日冕物质抛射，是空间天气预报的必要组成部分。目前，NOAA空间天气预报中心利用经验驱动模型预测背景太阳风和日冕物质抛射到达时间。该项目的目标是开发一个数据驱动的、依赖于时间的模型，以改进当前的技术状态。新模型将包括：1)地表通量输运模型，2)势场求解器，3)MHD太阳风模型。它将提供更精确的解决方案，并可在大规模并行计算系统（包括图形处理器单元）上进行扩展。该项目的产品将在涉及多个不连续的复杂等离子体系统的计算和模拟方面提供一个飞跃。开发的软件还将对具有独特球面几何的天体物理问题有用，包括系外行星、早期太阳和类太阳恒星。该合同是NSF-NASA联合试点项目“具有量化不确定性的空间天气数据驱动模型的下一代软件”（SWQU）的一部分。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；软件许可证将允许修改和重新分发软件，免费用于非商业用途。该奖项反映了美国国家科学基金会的法定使命，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

An Empirically Driven MHD Model to Predict the Solar Wind at Parker Solar Probe and Solar Orbiter during the Current Solar Minimum

经验驱动的 MHD 模型可预测当前太阳极小期期间帕克太阳探测器和太阳轨道飞行器的太阳风

• 发表时间: 2020
• 期刊: abstract #SH021-08
• 作者: Kim, T. K.;Pogorelov, N.;Arge, C. N.;Jones-Mecholsky, S. I.
• 通讯作者: Jones-Mecholsky, S. I.

Can Fortran’s ‘do concurrent’ Replace Directives for Accelerated Computing?

Fortran 的“并发”能否取代加速计算指令？

• DOI: 10.1007/978-3-030-97759-7_1
• 发表时间: 2022
• 期刊: vol 13194. Springer,
• 作者: Stulajter, M. M.

Improving predictions of the background solar wind using coronal and solar wind observations as constraints

使用日冕和太阳风观测作为约束改进背景太阳风的预测

• 发表时间: 2022
• 期刊: WA. Bulletin of the AAS
• 作者: Arge, Charles;Henney, Carl;Jones, Shaela;Staeben, James;Leisner, Andrew;Uritsky, Vadim;Da Silva, Daniel;Schonfeld, Samuel
• 通讯作者: Schonfeld, Samuel

Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model

• DOI: 10.3847/1538-4357/ac73f3
• 发表时间: 2022-05
• 期刊: The Astrophysical Journal
• 作者: T. Singh;Tae K. Kim;N. Pogorelov;C. Arge

Predicting the Solar Wind Using Empirically-driven Data-constrained Heliospheric MHD Model

使用经验驱动的数据约束日光层 MHD 模型预测太阳风

• 发表时间: 2021
• 期刊: id. SM53B-08.
• 作者: Kim, T.;Pogorelov, N
• 通讯作者: Pogorelov, N