A team led by the Southwest Research Institute (SwRI) has developed an improved model for predicting solar activity by incorporating information about the Sun’s far side.
The approach combines observations from Earth with space-based data to produce a more complete picture of the Sun’s magnetic field.
Led by SwRI research scientist Dr Bibhuti Jha and co-authored by SwRI lead scientist Dr Lisa Upton, the researchers analysed near-simultaneous maps of the entire solar surface collected between 2010 and 2014.
Their findings have enabled the development of more comprehensive surface flux transport (SFT) models, offering a more accurate way to track solar activity and understand how it changes over time.
The improved model could help scientists strengthen space weather forecasting by providing better information about the Sun’s magnetic field across both its visible and hidden hemispheres.
This is an important step towards more reliable predictions of solar behaviour and future solar cycles.
The challenge of tracking the Sun’s far side
SFT models monitor and forecast solar activity, helping scientists anticipate space weather events that can affect Earth.
However, previous models have been limited by the lack of simultaneous observations of the entire solar surface.
At any given time, approximately half of the Sun is hidden from direct view from Earth.
Unlike the Moon, the Sun rotates on its axis, completing a rotation roughly every 27 days. As it turns, regions on its far side eventually become visible.
This delay can complicate forecasting. Sunspots and other active regions may emerge unexpectedly, forcing scientists to revise their models as new observations become available.
Accounting for activity on the far side is therefore essential to developing a more complete understanding of the Sun’s changing magnetic field.
Improving solar observations with helioseismology
Scientists began using acoustic waves in the 1970s to investigate activity on the Sun’s far side.
This technique, known as helioseismology, provides information about solar regions that cannot be observed directly from Earth.
Helioseismology significantly improved space weather forecasting. However, the technique can sometimes identify active regions that do not appear when the corresponding area of the Sun rotates into view.
These misleading detections, known as ghost active regions, can introduce inaccuracies into solar models and undermine forecast reliability.
The SwRI-led team has developed a method to address this problem by evaluating the reliability of far-side observations before incorporating them into its SFT models.
A probabilistic approach to solar activity
Rather than treating every detected far-side active region as equally reliable, the researchers assign each one a probability based on its properties.
This allows the model to assess the likelihood that an apparent active region is genuine before using the information to map the Sun’s magnetic field.
The approach helps filter out ghost active regions and improves the accuracy of the resulting solar surface representation.
By combining this assessment with observations from Earth and space-based assets, the researchers have established a practical way to integrate far-side helioseismology data into SFT models.
The resulting framework provides a more realistic representation of the magnetic field across the Sun’s visible and invisible hemispheres, addressing a longstanding limitation in solar activity modelling.
Towards better predictions of future solar cycles
Understanding how the Sun’s magnetic field evolves is central to tracking changes in solar activity over successive cycles.
More complete models could help researchers better understand these patterns and improve their ability to anticipate how the Sun will behave in the coming years.
By improving how scientists identify and account for active regions on the far side of the Sun, the approach could strengthen the information used to forecast solar activity and the space weather conditions that can affect Earth.
