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.
