2 papers
physics.ao-ph2019
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne, Hannah M. Christensen, Aneesh C. Subramanian +1
Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some ex…
physics.ao-ph2019
Constraining stochastic parametrisation schemes using high-resolution simulations
Hannah M. Christensen
Stochastic parametrisations are used in weather and climate models to improve the representation of unpredictable unresolved processes. When compared to a deterministic model, a st…