Improving solar wind forecasts using data assimilation

In terrestrial weather prediction, Data Assimilation (DA) has enabled huge improvements in operational forecasting capabilities. It does this by producing more accurate initial conditions and/or model parameters for forecasting; reducing the impacts of the butterfly effect. However, data assimilation is still in its infancy in space weather applications and it is not quantitatively understood how DA can improve space weather forecasts. A solar wind DA scheme has been developed and used to assimilate observations from STEREO A, STEREO B and ACE data. This allows observational information at 1AU to update and improve the inner boundary of the solar wind model (at 30 solar radii). These improved inner boundary conditions can then be input into a solar wind forecasting model to produce forecasts of the solar wind over the next solar rotation. In this talk, I will show the impacts of using data assimilation for predicting the solar wind and that is capable of removing systematic errors in corotation forecasts. In addition to this, I will show that data assimilation not only produces a forecast of solar wind in near-Earth space, but the whole model domain, impacting upon the evolution of modelled CMEs between the Sun and Earth, potentially improving CME arrival time forecasts.