Authors: Daniel da Silva (NASA/GSFC, UMBC), Nick Arge (NASA/GSFC), Shaela Jones (NASA/GSFC, Catholic University), Samantha Wallace (NASA/GSFC)

The origin point on the sun corresponding to the solar wind at a given location in the solar system (the “footpoint”) can be estimated from Heliosphere models by tracking outgoing solar parcels. However, these point-estimates are unavoidably created under the influence of multiple sources of uncertainty such as unobserved far side variation, simplified physics, and instrument noise. To capture the full story, a stronger approach is to model the footpoint location as probability distribution or confidence interval taking the uncertainty into account. This way, a broader array of information is communicated, and users of the model can make inferences based on the consensus or lack thereof in the probability distribution. This talk presents a method developed to produce such probability distributions for the solar wind footpoint, based on the WSA solar wind model, the ADAPT ensemble flux transport model, and the well-proven methods of Kernel Density Estimation (KDE) from the statistician’s toolbox.