Forecasting the properties of the solar wind using simple pattern recognition: SOLAR WIND PREDICTION


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 Δt hours into the future (where Δt can range from 6 h to several weeks) based on a simple pattern recognition algorithm. At some time, t , the algorithm takes the previous Δt hours and compares it with a sliding window of Δt 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 Δt 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.

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