Effects of observational data shortage on accuracy of global solar activity forecast

Building a reliable forecast of solar activity is a long-standing problem that requires an accurate description of past and current global dynamics. Relatively recently, synoptic observations of magnetic fields and subsurface flows have become available. In this paper, we present an investigation of the effects of short observational data series on the accuracy of solar cycle prediction. This analysis is performed using the annual sunspot number time-series applied to the Parker-Kleeorin-Ruzmaikin dynamo model and employing the Ensemble Kalman Filter (EnKF) data assimilation method. The testing of cycle prediction accuracy is performed for the last six cycles (for Solar Cycles 19-24) by sequentially shortening the observational data series to predict a target cycle and evaluate the resulting prediction accuracy according to specified criteria. According to the analysis, reliable activity predictions can be made using relatively short time-series of the sunspot number. The accuracy of the solar activity has a weak dependence on the length of available observations. It is demonstrated that at least three cycles of observations are needed to obtain robust forecasts.

Automated summary

The study demonstrates that reliable solar activity predictions can be made using relatively short time-series of sunspot numbers, with the accuracy of the predictions having weak dependence on the length of available observations. At least three cycles of observations are needed to obtain robust forecasts.