Flow-based generative models for forecasting and data assimilation

Abstract: Flow-based and diffusion generative models have emerged as powerful tools for sampling from complex, high-dimensional distributions, such as those found in image generation. In weather forecasting, they enable the generation of ensemble forecasts at a fraction of the computational cost of traditional numerical models. These models have also shown promise for solving inverse problems like data assimilation, offering advantages over classical methods in high-dimensional, nonlinear settings. In this talk, I will introduce the core ideas behind these approaches and present some of our recent results.