Nudging solar wind forecasts back towards reality

In order to forecast space weather, it is necessary to accurately model the solar wind, the continually expanding solar atmosphere which fills the solar system. At present, telescopic observations of the Sun's surface are used to provide the starting conditions for computer simulations of the solar wind, which then propagate conditions all the way from the Sun to Earth. But spacecraft also make direct measurements of the solar wind, which provide useful additional information that is not presently used. In this study we use a simple solar wind model to develop a method to routinely "assimilate" spacecraft observations into the model and thus improve space‐weather forecasts. This data assimilation (DA) approach closely follows that of terrestrial weather prediction, where DA has led to increasingly accurate forecasts. We use artificial and real spacecraft observations to test the new solar wind DA method and show that the error in predicting the near‐Earth solar wind can be reduced by around a fifth using...
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Forecast uncertainty in the near-Earth solar wind conditions

Long lead-time space-weather forecasts require accurate prediction of the solar wind conditions in near-Earth space. The current state-of-the-art involves coupled numerical models initialised using photospheric magnetic field observations. This deterministic approach means there is no estimate of forecast uncertainty. Large ensembles with perturbed boundary conditions aren’t really feasible due to computational expense.  We have developed a method for producing a large ensemble of near-Earth solar wind conditions using the numerical model output with a simple 1-dimensional solar wind model (http://onlinelibrary.wiley.com/doi/10.1002/2017SW001679/full). This approach produces a probabilistic solar wind forecast which accurately captures the forecast uncertainty....
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