210 citations · 274 across the 4 of their papers we have counts for
5 papers
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen +40
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative s…
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking +1
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through paramet…
The Fractal Nature of Clouds in Global Storm-Resolving Models
Hannah M. Christensen, Oliver G. A. Driver
Clouds in observations are fractals: they show self-similarity across scales ranging from one to 1000 km. This includes individual storms and large-scale cloud structures typical o…
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…
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…