Sun-to-Earth CME Modeling with Data Assimilation and Uncertainty Quantification

Supported by the Space Weather with Quantified Uncertainty (SWQU) NSF program, we have been developing the Next Generation Space Weather Modeling Framework at the University of Michigan for three years. The main goal of the project is to provide useful probabilistic forecast of major space weather events about 24 hours before the geospace impact occurs. We are using the first-principles models in the Space Weather Modeling Framework (SWMF) in combination with uncertainty quantification and data assimilation. Using the advanced MaxPro experimental design and fully automated Python scripts, we have performed thousands of solar wind background and coronal mass ejection (CME) simulations with the solar corona, inner heliosphere and eruptive event generator based on the Gibson-Low fluxrope (EEGGL) models of the SWMF. Our CME initiation model is at the surface of the Sun, so the CME can interact with the background solar wind and the magnetic field of the erupting active region. Based on these simulations, we have performed the uncertainty quantification analysis using the Bayesian inversion formula and a newly defined distance metric adapted to solar simulations. One important finding is that the physically meaningful range of the background solar wind model parameters depends on the solar cycle. We have identified the three most important parameters that impact the background solar wind model and two more parameters (the strength and helicity of the magnetic field of the fluxrope) that impact the CME eruption model. The reduced dimensionality of the parameter space enables reducing the size of the ensemble. Data assimilation provides further opportunity to improve the predictions. We are using in-situ observations at L1 prior to the CME to constrain the background solar wind and coronal white-light image observations right after the eruption to find the optimal flux rope parameters. We find that the CME arrival time error is significantly reduced to less than 5 hours by the data assimilation based on three events. Using an ensemble of simulations also provides a likely range for the various quantities of interest, including arrival time, solar wind speed and density and the BZ component of the magnetic field. The main product of the project, the Michigan Sun-to-Earth Model with Quantified Uncertainty and Data Assimilation (MSTEM-QUDA) is available as an open-source distribution at https://github.com/MSTEM-QUDA