Hannah M. Christensen

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties …

emily-g.-simmonds
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Machine Learning; Stochastic Parameterization; Generative Adversarial Networks; Lorenz; Climate; Weather

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz `96 Model

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some …

ii-gagne
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Ensemble Forecasts; Ensemble Data Assimilation; Weak-Constraint 4D-Var; Numerical Weather Prediction; Dynamical Core; Earth System Model; Model Uncertainty; Stochastic Parametrization

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model …

martin-leutbecher
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Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and …

paolo-davini
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