activity
20192022
most citedMachine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

210 citations · 274 across the 4 of their papers we have counts for

collaborators
Showing 2019Show all

2 papers · 1 filter

physics.ao-ph2019★ 210 cited

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★ 22 cited

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…