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Participate in an interactive workshop challenge that combines scientific machine learning (SciML) with computational fluid dynamics (CFD) to build surrogate models of weather systems. Engage with Julia-based weather modeling tools including SpeedyWeather.jl and RainMaker.jl while competing to optimize parameters that maximize rainfall in specific locations. Start with premade scripts demonstrating weather model execution, then develop machine learning surrogate models as your foundation for parameter optimization. Compete on a live interactive leaderboard that updates automatically through continuous integration using Documenter.jl builds to track participant progress in real-time. Explore various ML and SciML techniques using tools such as LIBSVM.jl and XGBoost.jl to achieve optimization goals while learning to integrate climate and weather modeling with machine learning workflows. Share solutions with fellow participants at the conclusion, with recognition for the challenge winner, while gaining hands-on experience with Julia's ML ecosystem and discovering new tooling possibilities for enhanced scientific computing workflows in atmospheric modeling.
