Advancing Weather and Climate Prediction with Data-Driven Methods

Explore a cutting-edge colloquium on advancing weather and climate prediction through data-driven methods, presented by Will Chapman from NSF-NCAR. Delve into the transformative role of Machine Learning (ML) in atmospheric sciences, covering four key themes: model post-processing, scientific discovery, hybrid modeling, and model emulation. Gain insights into how ML is revolutionizing the accuracy and efficiency of weather forecasts and climate predictions, often surpassing traditional methods. Discover the potential for strengthening convergence between computer and atmospheric sciences, leading to novel frameworks and theoretical advancements in both fields. Learn from Chapman's expertise as a project scientist with the Climate and Global Dynamics Group at NCAR, and his background in engineering and atmospheric science from prestigious institutions. Understand the importance of these advancements in addressing the increasing frequency of climate and weather-related disasters globally.