A Parametric Study of Performance of Two Solar Wind Velocity Forecasting Models During 2006–2011

Abstract

There is an increasing need for the development of a robust space weather forecasting framework. State‐of‐the‐art MHD space weather forecasting frameworks are based upon the Potential Field Source Surface (PFSS) and Schatten Current Sheet (SCS) extrapolation models for the magnetic field using synoptic magnetograms. These models create a solar wind (SW) background for the simulations using empirical relations of Wang, Sheeley and Arge (WSA), at the inner boundary of heliosphere and have been used to simulate coronal mass ejections for specific cases in previous studies. Besides these MHD frameworks, the Heliospheric Upwind eXtrapolation (HUX) technique can extrapolate SW from inner heliospheric boundaries to L1 and can give a reliable estimate of the SW velocity at L1 comparable to MHD models but in a short computational time. We carried out an extensive parametric study of the performance of the Model1 (PFSS+WSA+HUX) and Model2 (PFSS+SCS+WSA+HUX) for SW velocity prediction at L1. We implemented this framework on 60 Carrington Rotations from CR2047 to CR2107 during 2006–2011, covering the descending and deep minimum phase of solar cycle (SC) 23, and the ascending phase of SC 24. Our results show an unexpected decrease in the performance of the framework during the deep minimum phase of cycle 23, which is attributed to the decrease in the observed coronal hole area. As SC 24 began, this decreasing trend vanished due to an increase in the coronal hole (CH) area at the low and mid‐latitudes, suggesting a good correlation between the performance of the framework and the variation in the CH area.

Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction

“…For example, the marginalized posterior of α has relatively small support that varies randomly from one CR to the next. We also notice that the MAP, shown in dashed vertical lines, changes greatly from one CR to the next, which agrees with the previous parametric studies by Kumar and Srivastava (2022) and Riley et al (2015). Since the posterior densities vary greatly from one CR to the next, it is not possible to use the posterior samples from a given CR to create an accurate ensemble prediction of the next CR (in contrast to the adaptive-WSA method proposed by Reiss et al (2020)).…”

“…(2014, §2.5), Meadors et al. (2020, Equation 9), Kumar and Srivastava (2022, Table 1), and Riley et al. (2015, Table 1).…”

“…Additionally, Riley et al. (2015) and Kumar and Srivastava (2022) showed that the optimal parameter can vary greatly from one CR to the next. It is, therefore, important to understand the uncertainties in the WSA parameters and their impact on predicted solar wind speed near Earth.…”

“…The SCS model is added to correct the PFSS radial magnetic field latitudinal variations to match Ulysses' observations (Wang & Sheeley, 1995). However, a recent study by Kumar and Srivastava (2022) showed that adding SCS to the chain of models did not necessarily improve the accuracy of the solar wind radial velocity predictions at L1 (during 2006-2011). We thus analyze the PFSS → WSA → HUX model chain.…”

“…The eight numerical parameters of the WSA model, v 0 , v 1 , α, β, γ, δ, w, ψ, similar to the source surface, cannot be directly measured and are usually adjusted for different observatories, for example, Wilcox solar observatories and GONG (Riley et al, 2015). Additionally, Riley et al (2015) and Kumar and Srivastava (2022) showed that the optimal parameter can vary greatly from one CR to the next. It is, therefore, important to understand the uncertainties in the WSA parameters and their impact on predicted solar wind speed near Earth.…”