Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model

Abstract In this study, we develop an artificial intelligence (AI)-based solar surface flux transport (SFT) model. We predict synoptic maps for the next solar rotation (27.2753 days) using deep learning. Our model takes the latest synoptic maps and their sine-latitude grid data as inputs. Synoptic maps, which represent global magnetic field distributions on the solar surface, have been widely used as initial boundary conditions in the Sun and space-weather prediction models. Here we train and evaluate our deep-learning model, based on the Pix2PixCC architecture, using data sets of Solar Dynamics Observatory/Helioseismic and Magnetic Imager, Solar and Heliospheric Observatory/Michelson Doppler Imager, and National Solar Observatory/Global Oscillation Network Group synoptic maps with a resolution of 360 by 180 (longitude and sine latitude) from 1996 to 2023. We present results of our model and compare them with those from the persistent model and the conventional SFT model, including the effects of differential rotation, meridional flow, and diffusion on the solar surface. The average pixel-to-pixel correlation coefficient between the target and our AI-generated data, after 10 by 10 binning with a 10° resolution in longitude, is 0.71. This result is qualitatively similar to the results of the conventional SFT model (0.65–0.68) and better than the results of the persistent model (0.56). Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. Using synthetic input data with bipolar structures, we confirm that our model successfully reproduces differential rotation and meridional flow. Finally, we discuss the advantages and limitations of our model in view of magnetic field evolution and its potential applications.