Authors: James Staeben (UVA/NASA GSFC), Nick Arge (NASA GSFC), Shaela Jones (NASA GSFC/CUA)

Accurate forecasting of space weather grows increasingly important as we move towards crewed missions to the moon and beyond. The Wang-Sheeley-Arge (WSA) model uses derived coronal solutions and empirical relationships to predict solar wind speed and interplanetary magnetic field (IMF) polarity at any point in the inner heliosphere, which can then be validated using in situ data from satellites. Using Air Force Data Assimilative Photospheric flux Transport (ADAPT) model input maps, which provide different global estimates of the sun’s surface magnetic field, we can generate multiple WSA-ADAPT solutions for forecasting purposes. The WSA Predictive Metric (WSA-PM) is a scoring metric which has been developed to evaluate WSA solar wind predictions. The WSA-PM can provide both relative performance rankings of ADAPT realizations and quantitative measurements of performance using skill scores that compare the different realizations to baseline models such as persistence and recurrence. This poster analyzes the consistency and reliability of individual sets of WSA-ADAPT solar wind predictions, as well as examines differences between WSA-ADAPT prediction performance during solar minimum versus solar maximum to better understand the science driving model performance. The results of this research will allows space weather forecasters to better evaluate the predictive performance of WSA based on the input maps used to drive it, leading to more reliable space weather forecasts.