Forecasting the properties of the solar wind using simple pattern recognition

An accurate forecast of the solar wind plasma and magnetic field properties is a crucial capability for space weather prediction. However, thus far, it has been limited to the large‐scale properties of the solar wind plasma or the arrival time of a coronal mass ejection from the Sun. As yet there are no reliable forecasts for the north‐south interplanetary magnetic field component, B n (or, equivalently, B z ). In this study, we develop a technique for predicting the magnetic and plasma state of the solar wind Δ t hours into the future (where Δ t can range from 6 h to several weeks) based on a simple pattern recognition algorithm. At some time, t , the algorithm takes the previous Δ t hours and compares it with a sliding window of Δ t hours running back all the way through the data. For each window, a Euclidean distance is computed. These are ranked, and the top 50 are used as starting point realizations from which to make ensemble forecasts of the next Δ t hours. We find that this approach works remarkably well for most solar wind parameters such as v , n p , T p , and even B r and B t , but only modestly better than our baseline model for B n . We discuss why this is so and suggest how more sophisticated techniques might be applied to improve the prediction scheme.