Forecasting Occurrence and Intensity of Geomagnetic Activity With Pattern‐Matching Approaches

Variability in near-Earth solar wind conditions gives rise to space weather, which can have adverse effects on space- and ground-based technologies. Enhanced sustained coupling with the Earth's magnetosphere lead a geomagnetic storm. The resulting interfere power transmission grids, potentially affecting today's technology-centered society great cost. It is therefore important forecast intensity duration of storms improve decision making capabilities infrastructure operators. 150 years aaH index substantial history observations from empirical predictive schemes be built. Here we investigate forecasting activity two pattern-matching techniques, using long record. techniques are an Analogue Ensemble (AnEn) Forecast, Support Vector Machine (SVM). AnEn produces probabilistic by explicitly identifying analogs for recent historical data. SVM deterministic through dependencies identified interpretable machine learning approach. As third comparative forecast, use 27 days recurrence model, based synodic rotation period. methods analyzed several metrics compared. All forecasts outperform climatology considered recurrence. A Cost/Loss analysis reveals potential economic value maximized AnEn, but shown as superior true skill score. likely that best method user will depend their need information tolerance false alarms.