Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF
Abstract
The Space Weather Modelling Framework (SWMF) offers efficient and flexible Sun-to-Earth simulations based on coupled first principles and/or empirical models. This encompasses computing the quiet solar wind, generating a coronal mass ejection (CME) through a flux rope, propagating the CME through the heliosphere, and calculating the magnetospheric impact via geospace models. However, these predictions are affected by uncertainty in many model parameters and inputs and therefore require systematic uncertainty quantification and Data Assimilation (DA). In this presentation, as part of the NextGen SWMF project funded by NSF, we focus on DA for observations and simulations of CME events. Prior work on simulations of background solar wind and apriori sensitivity analysis helps us identify the most influential parameters that impact the background velocity and density. Subsequently, by incorporating observation data we construct posterior distribution for these parameters through Approximate Bayesian Computation (ABC), which bypasses the difficulty of evaluating the intractable likelihood of the complex physical model. Based on the posterior a small ensemble of background simulations is created, which is then augmented with new experimental designs for different flux rope parameters. The resulting set of CME simulations yields quantities such as velocity, density and Bz component of magnetic field at 1 au as well as synthetic white-light images in the low corona. These images are processed to calculate time-varying brightness ratios relative to the background before CME eruption and then the edges of the eruption are detected. We find that the comparisons with observed white light images and the 1 au quantities facilitate the prediction of average solar wind speed during the CME event and errors in CME shock arrival time. We also use the Sliced-Wasserstein distance to better quantify similarities between simulation white light images. These analyses are found to be useful to constrain the parameter space for improving the model predictions of a CME event.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMSH52B..06C