Stochastic Parameterization

Stochastic parameterization addresses the challenge of representing unresolved small-scale processes in large-scale models of complex systems like weather and climate. Current research focuses on developing data-driven methods, employing machine learning techniques such as generative adversarial networks, Bayesian neural networks, and generative models, to learn the probability distributions of these subgrid-scale processes. This improved representation of uncertainty in model parameters enhances the accuracy and reliability of predictions, impacting fields ranging from weather forecasting to drug design by providing more robust and informative simulations.