Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fr...
The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather...
The last decade has seen the success of stochastic parameterizations in short-term, medium-range and...
Abstract In this paper it is argued that ensemble prediction systems can be devised i...
htmlabstractObservational data of rainfall from a rain radar in Darwin, Australia, are combined with...
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defini...
Observations of tropical convection from precipitation radar and the concurring large-scale atmosphe...
Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are di...
Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic con...
In this paper, we report on the development of a methodology for stochastic parameterization of conv...
Traditional parameterizations of the interaction between convection and the environment have relied ...
The aim for a more accurate representation of tropical convection in global circulation models is a ...
Many numerical models for weather prediction and climate studies are run at resolutions that are too...
Convective parameterizations in general circulation models (GCMs) generally only aim to simulate the...
Abstract Stochastic parameterizations are continuously providing promising simulations of unresolved...
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research ...
The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather...
The last decade has seen the success of stochastic parameterizations in short-term, medium-range and...
Abstract In this paper it is argued that ensemble prediction systems can be devised i...
htmlabstractObservational data of rainfall from a rain radar in Darwin, Australia, are combined with...
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defini...
Observations of tropical convection from precipitation radar and the concurring large-scale atmosphe...
Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are di...
Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic con...
In this paper, we report on the development of a methodology for stochastic parameterization of conv...
Traditional parameterizations of the interaction between convection and the environment have relied ...
The aim for a more accurate representation of tropical convection in global circulation models is a ...
Many numerical models for weather prediction and climate studies are run at resolutions that are too...
Convective parameterizations in general circulation models (GCMs) generally only aim to simulate the...
Abstract Stochastic parameterizations are continuously providing promising simulations of unresolved...
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research ...
The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather...
The last decade has seen the success of stochastic parameterizations in short-term, medium-range and...
Abstract In this paper it is argued that ensemble prediction systems can be devised i...