Quasi Geostrophic

Quasi-geostrophic (QG) models simplify fluid dynamics, focusing on large-scale atmospheric and oceanic flows, primarily to improve the accuracy and efficiency of weather and climate prediction. Current research emphasizes using machine learning, particularly neural networks (including generative diffusion models, reservoir computing architectures, and variational autoencoders), to improve QG model accuracy, address limitations in data assimilation (e.g., through 4D-Var and ensemble methods), and develop more effective subgrid-scale parameterizations. These advancements aim to enhance the representation of turbulent flows, improve the prediction of extreme weather events, and ultimately lead to more reliable climate projections.