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

该项目将通过汇集空间和等离子体物理、数据同化、不确定性量化和网络基础设施方面的专家，启动下一代空间天气建模框架（NextGen SWMF）的开发。空间天气是由太阳活动造成的，它会影响地球的空间环境，破坏我们的技术系统，并使飞行员和宇航员暴露在有害的辐射中。极端事件可能会破坏电网，需要几个月的恢复时间，造成约2万亿美元的损失。及时可靠的空间天气预报可以避免或减轻许多影响。下一代空间天气建模框架将采用从太阳表面到地球表面的计算模型，结合观测数据的同化，提供最佳概率空间天气预报。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太空天气。该项目将集中于预测由日冕物质抛射产生的主要空间天气事件。目前的空间天气预报采用第一性原理和/或经验模型。虽然这些提供了有用的信息，但它们的准确性、可靠性和预测窗口需要重大改进。数据同化具有显著提高模式性能的潜力，就像在地面天气预报中成功完成的那样。然而，为了考虑到卫星观测的稀疏性，必须采用不同的数据同化方法。下一代SWMF模型将从太阳开始，进行一系列模拟，这些模拟跨越了不确定的观测和模型参数。使用实时和过去的观察，该模型将战略性地向下选择到一个高性能子集。接下来，通过改变不确定参数对下选择的集合进行扩展，并继续模拟到下一个数据同化点。最后的集合将提供空间天气影响的概率预报。寻找以最小不确定性产生最佳预测的最优算法是一项复杂且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得比实时更快，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使用传统的多核cpu外，还使用图形处理单元（gpu）。该项目的主要产品将是具有量化不确定性和数据同化的密歇根日地模型（MSTEM-QUDA）。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。它由美国国家科学基金会天文科学部、大气与地球空间科学部、数学科学部和物理部提供支持。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。

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.