Using Solar Wind Data Assimilation Results to Drive Dynamic Solar Wind Models

A coupled modeling framework is often used to forecast the near-Earth solar wind conditions. This consists of a coronal model for close to the Sun and a heliospheric model for propagating the solar wind out to Earth. The coronal model is initialized using photospheric magnetic field observations and beyond this, there are no further observational constraints. Models are therefore essentially free-running, and so large errors can propagate through the model, reducing the accuracy of forecasts. Data assimilation is a technique that combines model output with observations to form an optimum estimation of reality. In the context of space weather forecasting, we can assimilate observations from orbiting spacecraft, which can be used to adjust the inner boundary of the heliospheric model. Previous work using solar wind data assimilation has made use of the Magnetohydrodynamics Around a Sphere (MAS) coronal model; however, the Wang-Sheeley-Arge (WSA) model is more commonly used operationally. In this work, we present how the Burger Radius Variational Data Assimilation (BRaVDA) scheme can be used with the WSA model to produce an updated inner boundary condition for the Heliospheric Upwind Extrapolation with time-dependence (HUXt) model. This involves processing the BRaVDA output, as this would be required for use in any solar wind model, and how the output can be used to modify the WSA map for use in 3D physics-based models. We find that BRaVDA can help with WSA bias correction and show that using the optimum level of processing can lead to improved solar wind forecasts.