Probabilistic Solar Wind Estimation for Operational Space Weather Prediction at Mars

Unlike Earth, Mars does not possess an upstream solar wind monitor. This lack of continuous solar wind observations has fundamentally limited scientific studies that investigate solar wind impacts on the Mars space environment, and with increasing relevance, operational tasks for predicting space weather at the planet. Previous estimates of the solar wind have been pursued through physics-based modeling (e.g. magnetohydrodynamic models) or empirical (e.g. assuming statistical relationships with downstream observations) proxies. Proxies are often based on downstream observations from multiple orbiting spacecraft. These spacecraft pass in and out of the bow shock providing a semiregular sampling of the pristine solar wind. The most complete, and ongoing, set of the solar wind’s magnetic field and plasma parameters is from the NASA MAVEN spacecraft. MAVEN has orbited Mars since 2014, but additional assets add resolution to this dataset such as including ESA’s MEX mission which has been in orbit since 2003, the CNSA’s Tianwen-1 orbiter since 2021, and NASA’s ESCAPADE mission scheduled for orbital insertion in 2027.

In this presentation we will summarize a prior effort to create a continuous solar wind estimation upstream from Mars. This virtual solar wind monitor, or vSWIM (see Azari, Abrahams, Sapienza, Halekas, Biersteker, Mitchell, Pérez et al., 2024, doi: 10.1029/2024JH000155) was trained and assessed on MAVEN data with Gaussian process regression. Gaussian process regression, a type of machine learning, was used to provide predictions, and uncertainties on these predictions, at various temporal resolutions. vSWIM currently enables informed solar wind estimation at Mars for most of the time since 2014. We will then discuss current progress on improving vSWIM’s capacity for multi-spacecraft integration for enhanced operational space weather prediction efforts at Mars.