On Solutions of the PFSS Model With GONG Synoptic Maps for 2006–2018

Abstract

The potential field source surface (PFSS) model is widely used to derive the magnetic field of the solar corona. The only free parameter in the PFSS model is the radius of the so-called source surface, where magnetic field lines are forced to open. The radius of this surface is typically set to 2.5 solar radii in research and operational PFSS numerical models. Here, using Global Oscillation Network Group (GONG) synoptic maps of the photospheric field, solutions of the PFSS model for various heights of the source surface are investigated for 2006-2018. In particular, numerically derived open solar magnetic flux and coronal holes are examined. Solutions of the PFSS model based on GONG synoptic maps are particularly important since they are often used to drive operational space weather forecast models. Comparisons between observations and numerical results in this paper suggest that the radius of the source surface is significantly lower than 2.5 solar radii during the active phase of solar cycle 24. The fact that the source surface location depends on the solar activity suggests that relations which associate solar wind properties with the coronal magnetic field in the PFSS-based solar wind modes should be revisited. Furthermore, although the correction of the polar magnetic field is part of GONG synoptic map production pipeline, the results suggest that better treatment of polar fields is needed to cover observational gaps. The issue with the polar fields in GONG maps is particularly pronounced in recent years.

Forecasting the Ambient Solar Wind with Numerical Models. II. An Adaptive Prediction System for Specifying Solar Wind Speed near the Sun

“…Furthermore, this approach shows promise in the application to coronal model parameters such as the source surface distance. Recent studies on the source surface distance suggest that the choice of the distance influences the results largely, and this varies with the solar activity [e.g., 15,2,28]. A problem here is that of choosing the right model coefficients in Equation 2-4 for the solar wind models for the relevant source surface height.…”

An integrated data-driven solar wind – CME numerical framework for space weather forecasting

“…Although it is expected that many solar wind and CME cases, particularly before 2013 (Nikolić, 2019), can be well described using standard GONG maps, to obtain more consistent results over time, the use of zero-point corrected as well as ADAPT synoptic maps in SW simulations should be explored further. As the predicted background solar wind depends intimately on the synoptic maps, studies similar to those of Hinterreiter et al (2019), MacNeice (2009) and Wold et al (2018), should be undertaken to assess performances of solar wind models with the GONG zero-point corrected and ADAPT maps.…”

“…However, these standard maps, which have been available since 2006, show degradation of their accuracy over time. For example, it was shown recently by Nikolić (2019) that the PFSS solutions obtained using the standard GONG maps exhibit issues, particularly in the polar regions since 2013. The issue with the standard GONG maps can be attributed to background (i.e., zero point) magnetic field variations.…”

“…The changes in the accuracy can not only be due to the fact that the solar activity changes, but also due to changes in the input data quality. Recently, for example, solutions of the PFSS and SCS models based on standard GONG maps have been investigated by Nikolić (2019) for the period from 2006-2018. It was shown that the area of the coronal holes, which are known to be sources of the highspeed wind, derived using the standard GONG maps exhibits significant deviations after 2013, particularity in high-latitude regions.…”

Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

“…These models can be either physics-based or empirical. Empirical models (such as, in the solar domain, Altschuler and Newkirk, 1969;Schatten et al, 1969;Schatten, 1971;Wang and Sheeley Jr, 1992;Nikolić, 2019) usually require less computational resources, enabling faster forecasts. They can also serve as a baseline for physics-based models (Siscoe et al, 2004).…”