  Solar magnetic fields play a key role in understanding the nature of the
coronal phenomena. Global coronal magnetic fields are usually extrapolated from
photospheric fields, for which farside data is taken when it was at the
frontside, about two weeks earlier. For the first time we have constructed the
extrapolations of global magnetic fields using frontside and artificial
intelligence (AI)-generated farside magnetic fields at a near-real time basis.
We generate the farside magnetograms from three channel farside observations of
Solar Terrestrial Relations Observatory (STEREO) Ahead (A) and Behind (B) by
our deep learning model trained with frontside Solar Dynamics Observatory
extreme ultraviolet images and magnetograms. For frontside testing data sets,
we demonstrate that the generated magnetic field distributions are consistent
with the real ones; not only active regions (ARs), but also quiet regions of
the Sun. We make global magnetic field synchronic maps in which conventional
farside data are replaced by farside ones generated by our model. The
synchronic maps show much better not only the appearance of ARs but also the
disappearance of others on the solar surface than before. We use these
synchronized magnetic data to extrapolate the global coronal fields using
Potential Field Source Surface (PFSS) model. We show that our results are much
more consistent with coronal observations than those of the conventional method
in view of solar active regions and coronal holes. We present several positive
prospects of our new methodology for the study of solar corona, heliosphere,
and space weather.
