Presentation
Building the Foundation for Machine Learning-Based Mars Weather Forecasting
DescriptionMars is a leading target for human exploration, yet its weather remains difficult to predict due to phenomena such as global dust storms. While Earth forecasting has advanced through machine learning (ML), Mars lacks comparable systems. This work investigates whether Microsoft’s Aurora, a state-of-the-art Earth climate foundation model, can be adapted for Martian data. Using the EMARS reanalysis, we regridded variables to Aurora’s expected layout and executed inference on the University of Michigan’s Great Lakes supercomputing cluster. We verified Aurora’s Earth pipeline by reproducing ERA5 benchmarks and established a functional pathway for applying Aurora to Mars; however, full predictive accuracy requires Mars-specific surface/static variables and fine-tuning. The poster will present the adaptation pipeline, validation on ERA5, preliminary EMARS runs, and a roadmap to reliable ML-based Mars weather forecasting, emphasizing the role of HPC in data preparation and model execution.
Event Type
Research and ACM SRC Posters
TimeThursday, 20 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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