Speaker
Description
We report on prediction of the solar activity on the time-scale (from several months to a year) using a combination of the 1D nonlinear mean-field dynamo model with magnetic helicity evolution and artificial neural network.
The neural network works as a correction scheme for the forecast by using the currently available observational data (the 13 month running average of the observed solar sunspot numbers) and the dynamo model output.
The dynamo generated large-scale magnetic flux is redistributed by negative effective magnetic pressure instability (NEMPI) producing sunspots and active regions.
The nonlinear mean-field dynamo model includes algebraic nonlinearity (caused by the feedback of the growing magnetic field on the plasma motion) and dynamic nonlinearity (related to the magnetic helicity evolution of small-scale magnetic field).
We compare the forecast errors with horizons of 1, 6, 12, and 18 months, for different forecast methods which use corrections every month based on the same current observations. We perform such forecasting since 2017 and analysed the available accumulated data archive.
The comparison of our forecast with the observed solar activity demonstrates good agreement, and the forecast error is almost stably small both over short (1-3 months) and longer (up to 18 months) ranges of forecasting windows.
In 2024 the solar activity had some prominent burst which affected the forecasts on short time ranges which used other methods to overestimate the current activity. Unlike that, our method have given more accurate prediction of the maximum phase of activity cycle.
