Speaker
Description
The most widely used method of forecasting the solar wind conditions in near-Earth space is through a coupled modelling framework. 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 initialised using observations of the photospheric magnetic field and beyond this, when modelling the ambient solar wind, there are no further observational constraints. This means that the models are essentially free-running, and so large errors can propagate through the model, reducing the accuracy of forecasts. Data assimilation (DA) is a technique that combines model output with observations of a system to form an optimum estimation of reality. In the context of space weather forecasting, we can assimilate observations from orbiting spacecraft, and this can be used to adjust the inner boundary of the solar wind models.
Previous work using solar wind data assimilation has made use of the Magnetohydrodynamics Around a Sphere (MAS) coronal model due to its availability and long archive. 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 output from the WSA model to produce an updated inner boundary condition for the Heliospheric Upwind Extrapolation with time-dependence (HUXt) model. This involves some processing of 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 the use of BRaVDA can help with WSA bias correction and show that using the optimum level of processing can lead to improved solar wind forecasts.
