| Name | Irina Kitiashvili |
| Affiliation | NASA Ames Research Center, USA |
| Title | Global Solar Activity Forecast Using Synoptic Magnetograms |
| Authors | Irina Kitiashvili |
| Abstract | The problem of predicting the solar 11-year activity cycles remains unsolved for many reasons, such as the complexity of physical processes in the solar interior, the lack of knowledge about past and current global solar dynamics, and limitations of available observations. The sunspot number, which has the longest observational time series, is traditionally considered as the principal measure of a solar cycle's strength. Synoptic observations of magnetic fields on the solar surface provide a more physics-based dataset for the solar cycle prediction problem. In this presentation, I discuss a data assimilation approach to predict global solar activity using synoptic magnetograms and a non-linear dynamo model. I present validation of the approach and prediction results for Solar Cycle 25 and examine criteria for model predictive capabilities in situations with limited available observational data. |
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