A set of parameters that characterize the complexity and energy potential of solar active-regions is fed through several Machine Learning and conventional statistics algorithms to forecast solar flares.
The majority of flare forecasting methods rely on observations of magnetic field on the Sun’s surface, but which observable, Br or Blos, is a better predictor? Through comparing a few magnetic properties derived from both observables, this nugget gives some suggestion.
The dipole, quadrupole, and octupole components of the Sun’s magnetic field are calculated and visualized, covering the last 22 years of the Sun’s activities.
The Sun’s meridional flow varies with the solar cycle, and this is possibly caused by the back-reaction of the dynamo-generated magnetic field on the meridional flow due to the Lorentz force.
Two homologous circular-ribbon flares associated with two filament eruptions were observed and analyzed. The emergence of magnetic flux ropes helped to inject free energy into the region and drive the magnetic reconnection above it.
Using a combination of the magnetograms, we find signs of the beginning of the 25th cycle from both HMI and WSO by calculating the inclination angles determined from the variation in line of sight field during a disk passage.
Large-scale inflows form around emerging solar active regions in the near-surface layer and alter the global meridional flow patterns.
A realistic MHD simulation driven directly by SDO/HMI vector magnetograms reproduced a solar eruption in a non-potential flux-emerging active region.
The HMI team has developed several Python codes that can be used to conveniently analyze its many magnetic field data products. Check it out!
The magnetic twist of a flux rope is carefully calculated for an active region model. The flux rope is close to the threshold of helical kink instability and has powered a series of eruptions.