Solar energy paper index
Source Surface Height Optimisation for Improved Solar Wind Velocity Forecasting Across Solar Cycles 23, 24 and 25
One-line summary
A solar energy research paper on Source Surface Height Optimisation for Improved Solar Wind Velocity Forecasting Across Solar Cycles 23, 24 and 25.
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Chinese explanation / 中文解读
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Original abstract
The potential field source surface (PFSS) model is a cornerstone of many state-of-the-art space weather forecasting frameworks. It includes a single physical free parameter, the source surface (SS) height. Traditionally, a fixed SS height of 2.5 solar radii has been adopted in most studies. However, several studies have investigated the SS height in the PFSS model using various datasets and temporal intervals. Earlier efforts primarily aimed to improve estimates of the heliospheric open magnetic flux. Our recent study Kumar et al. (2025), showed that optimising the SS height substantially improves the performance of the Wang Sheeley Arge (WSA) solar wind speed prediction model at L1. That analysis employed two types of GONG synoptic magnetic field maps, namely standard (STD) and zero-point-corrected (ZPC) maps, over selected periods of solar cycles (SCs) 24 and 25. In this study, we perform a comprehensive investigation of SCs 23 to 25 using synoptic magnetic field maps derived from ground-based (GONG) and space-based (SDO/HMI) observatories. Our results demonstrate that optimising the SS height in the PFSS model significantly improves the WSA model performance compared to the commonly adopted fixed SS height. We find that during solar minimum, higher SS heights (less than or equal to 2.5 solar radii) yield better solar wind speed predictions at L1, whereas during active phases the opposite trend is observed. Although the absolute optimised SS height varies across SCs, the overall distribution pattern remains similar. These finding suggests a relationship between SS height and SC phase over long time scales. Overall, the SS height optimisation enhances the background solar wind speed prediction models in the heliosphere.
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