In predicting the Kp index, historical Kp values add little to short-term solar wind-based forecasts, while long-term forecasts rely primarily on recurrence.
Abstract Current algorithms for the real‐time prediction of the K p index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and K p time series as input to artificial neural networks. We explore the relative efficiency of the solar wind‐based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short‐term forecasts of approximately half a day, the addition of the historical values of K p to the measured solar wind values provides a barely noticeable improvement. For a longer‐term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high K p results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.
Shprits et al. (Tue,) studied this question.