Description: Radiation hazards constitute a serious risk to human and robotic space operations beyond Low-Earth orbit. Primary contributors to space radiation include Solar Particle Events (SPEs) associated with Coronal Mass Ejections (CMEs). Because the mechanisms that produce coronal mass ejections (CME) are exceedingly complex, no reliable deterministic methods for predicting eruptions are yet available, and the most successful approaches are phenomenological and probabilistic in nature. But predicting the eruption is only part of the problem. In order to forecast the time, location, flux, and the energy spectrum of a Solar Particle Event (in order to better model its effect on specific hardware and instruments, for example) we must also understand the intervening plasma environment, including the steady-state magnetic configuration, as well as the dynamic, eruption driven configurations that provide for the time dependent transport and diffusive acceleration of solar energetic particles. Progress has been made in the understanding of the solar atmosphere due to the increased availability of observational data and the development of analytical and numerical models of the solar wind. One aspect of this development is the construction of complex three-dimensional (3D) models, which can be validated with observations and further refined to improve the comparison. In order to improve SPE forecasts Michigan Aerospace Corporation (MAC) and the University of Michigan's department of Atmospheric, Oceanic, and Space Science (AOSS) intend to cooperate on this STTR project, which seeks, over Phase 1 and Phase 2, to 1) Use data-driven statistical models to forecast the likelihood of solar eruptions; 2) Couple these predictions with eruption generation models in the context of the Space Weather Modeling Framework (SWMF) to forecast the likely time, location, flux, and energy spectrum of Solar Energetic Particles.

Benefits: This technology will use data-driven statistical models to forecast the likelihood of solar eruptions and couple these predictions with eruption generation models in the context of the Space Weather Modeling Framework (SWMF) to forecast the likely time, location, flux, and energy spectrum of Solar Energetic Particles. In principle, this technology could be developed by NASA into a commercial space weather forecasting product, available on a subscription (or other) basis to interested parties.

This technology will use data-driven statistical models to forecast the likelihood of solar eruptions and couple these predictions with eruption generation models in the context of the Space Weather Modeling Framework (SWMF) to forecast the likely time, location, flux, and energy spectrum of Solar Energetic Particles. In principle, because the SWMF is publicly available, the forecasting technology described in this proposal could be developed by a into a commercial space weather forecasting product by a private company, available on a subscription (or other) basis to interested parties.