Abstract

Predictive modeling of extreme weather events is a key tool for addressing the impacts of climate change at both regional and global scales. Regions like California and Bavaria face immense challenges from the increasing frequency of such events. By employing predictive simulation methods, regional and municipal authorities can better plan and appropriately scale climate-resilient infrastructure. However, large-scale, high-fidelity numerical simulations are extremely computationally demanding and require advanced computational and mathematical methods for efficient prediction of regional climate impacts. To meet this need, our project will work towards extending the Julia-based simulation framework Trixi.jl to simulate local extreme weather events with solution-adaptive algorithms for GPU-based supercomputers. In addition, integrating machine learning and automatic differentiation will facilitate sensitivity analyses and enable more sophisticated surrogate modeling.