New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning

This challenge is particularly acute at high latitudes where the effects of space weather are most direct and no reliable predictive capability exists. We take advantage of a large volume of data from Global Navigation Satellite Systems (GNSS) signals, increasingly sophisticated tools for data-driven discovery, and a machine learning algorithm known as the support vector machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation.

This work, to our knowledge, represents the first time an SVM model has been created to predict high-latitude phase scintillation. We use the true skill score to evaluate the SVM model and to establish a benchmark for high-latitude ionospheric phase scintillation prediction. The SVM model significantly outperforms persistence (i.e., current and future scintillation are identical), doubling the predictive skill according to the true skill score for a 1-hr lead time.