Add to ORKGA stochastic parameterization of shallow cumulus convection for high-resolution numerical weather prediction and climate model
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research ...
A well-represented description of convection in weather and climate models is essential since convec...
This research has focused on the development of a parameterization scheme for mesoscale convective s...
htmlabstractIn this paper, we report on the development of a methodology for stochastic parameteriza...
The parameterization of shallow cumuli across a range of model grid resolutions of kilometrescales f...
Cumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equili...
Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are di...
A new configuration of a parameterization for shallow convection in the Icosahedral Nonhydrostatic M...
computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface inte...
A stochastic parameterization scheme for deep convection is described, suitable for use in both clim...
Shallow cumulus convection is important not only because of its role in mediating local turbulent tr...
A stochastic parameterization scheme for deep convection is described, suitable for use in both clim...
This study presents a systematic analysis of convective parameterizations performance with interacti...
Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic con...
The paper describes a convection parameterization employing a new formulation of the quasi-equilibri...
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research ...
A well-represented description of convection in weather and climate models is essential since convec...
This research has focused on the development of a parameterization scheme for mesoscale convective s...
htmlabstractIn this paper, we report on the development of a methodology for stochastic parameteriza...
The parameterization of shallow cumuli across a range of model grid resolutions of kilometrescales f...
Cumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equili...
Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are di...
A new configuration of a parameterization for shallow convection in the Icosahedral Nonhydrostatic M...
computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface inte...
A stochastic parameterization scheme for deep convection is described, suitable for use in both clim...
Shallow cumulus convection is important not only because of its role in mediating local turbulent tr...
A stochastic parameterization scheme for deep convection is described, suitable for use in both clim...
This study presents a systematic analysis of convective parameterizations performance with interacti...
Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic con...
The paper describes a convection parameterization employing a new formulation of the quasi-equilibri...
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research ...
A well-represented description of convection in weather and climate models is essential since convec...
This research has focused on the development of a parameterization scheme for mesoscale convective s...