A Novel Bimodal Forecasting Model for Solar Cycle 25

Abstract

Abstract In this paper, a novel bimodal model to predict a complete sunspot cycle based on comprehensive precursor information is proposed. We compare the traditional 13 month moving average with the Gaussian filter and find that the latter has less missing information and can better describe the overall trend of the raw data. Unlike the previous models that usually only use one precursor, here we combine the implicit and geometric information of the solar cycle (peak and skewness of the previous cycle and start value of the predicted cycle) with the traditional precursor method based on the geomagnetic index and adopt a multivariate linear approach with a higher goodness of fit (>0.85) in the fitting. Verifications for cycles 22–24 demonstrate that the model has good performance in predicting the peak and peak occurrence time. It also successfully predicts the complete bimodal structure for cycle 22 and cycle 24, showing a certain ability to predict whether the next solar cycle is unimodal or bimodal. It shows that cycle 25 is a single-peak structure and that the peak will come in 2024 October with a peak of 145.3.