Speaker
Description
Over the past several decades, there has been a growing recognition of the adverse impacts that space weather can pose to human infrastructure, activity, and health. Consequently, as modern society becomes increasingly reliant on vulnerable technological systems, there is a corresponding demand for accurate and reliable space weather forecasting capabilities. Successful space weather forecasting requires precise knowledge of the ambient, or quasi-steady background, solar wind as this is the medium through which solar disturbances propagate towards the Earth. For example, the ambient solar wind strongly influences the transit time of interplanetary coronal mass ejections and contributes to significant near-Earth space weather effects through the formation of stream interaction regions. State-of-the-art predictions of the ambient solar wind are today based on three-dimensional (3D), global, magnetohydrodynamic (MHD) models. Despite their sophistication and physical fidelity, the predictive potential of these models is not yet fully realized---solar wind forecasts often fail to accurately match observations. While the global MHD models are data-driven and estimate boundary data at the inner inflow heliospheric boundary for the simulations by combining observations of the solar magnetic field and empirical estimates of the plasma properties, these models are typically also "free-running" and unconstrained by in-situ. measurements of the solar wind outflows. Thus, any uncertainties or errors arising from driving inflow boundary conditions can grow unbounded and pollute or contaminate the predictions. To overcome these limitations, this presentation introduces a novel variational data assimilation framework---the first of its kind---that optimally combines in-situ. solar wind observations with the predictive solutions of a global MHD model. A Bayesian inverse MHD problem is solved to obtain the most probable solar wind inflow boundary data for the given set of observational data. An overview of the variational data assimilation approach and several results from observing system simulation experiments conducted using both ACE and STEREO spacecraft data will be presented, with the latter demonstrating the substantially improved predictions of near-Earth solar wind conditions afforded by the proposed data assimilation approach.
| Numerical model | CFFC |
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