Stochastic methods are a crucial area in contemporary climate research and are
increasingly being used in comprehensive weather and climate prediction models as well as …

There is no more challenging problem in computational science than that of estimating, as
accurately as science and technology allows, the future evolution of Earth's climate; nor …

Stochastic Parameterization: Toward a New View of Weather and Climate Models in:
Bulletin of the American Meteorological Society Volume 98 Issue 3 (2017) Jump to Content …

Simple chaotic systems are useful tools for testing methods for use in numerical weather
simulations owing to their transparency and computational cheapness. The Lorenz system …

A conditional Gaussian framework for understanding and predicting complex multiscale
nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such …

Land surfaces and soil conditions are key sources of climate predictability at the seasonal
time scale. In order to estimate how the initialization of the land surface affects the …

Deep learning can accurately represent sub‐grid‐scale convective processes in climate
models, learning from high resolution simulations. However, deep learning methods usually …

The parameterization of shallow cumuli across a range of model grid resolutions of kilometre‐
scales faces at least three major difficulties:(1) closure assumptions of conventional …

The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization in:
Journal of the Atmospheric Sciences Volume 72 Issue 1 (2015) Jump to Content Jump to Main …

A cellular automaton (CA) is introduced to the deep convection parametrization of the high‐
resolution limited‐area model Aire Limitée Adaptation/Application de la Recherche à …