Data assimilation through Kalman filtering is a powerful statistical tool that allows researchers to combine modeling and observations and thus to increase the degree of knowledge of a given system.
The application of this technique to an empirical solar wind forecasting model which enables the forecasting of solar wind parameters from coronal hole observations is here described and discussed.
The forecasts for the solar wind proton velocity and temperature and for the magnetic field magnitude
with and without data assimilation are validated against Advanced Composition Explorer observations, and it is shown that Kalman filtering can improve the quality of the forecasts and extend the period of applicability of the baseline model. In a subset of cases, some degree of robustness toward solar transient activity not accounted for in the original model is also provided.