Stochastic parameterization of convective area fractions with a multicloud model inferred from observational data

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,...

Stochastic convection parameterization with Markov Chains in an intermediate-complexity GCM

Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic convection parameterizations. In this paper, it is demonstrated how two different CMC models can be used as mass flux closures in convection parameterizations. More specifically, the CMC models provide a stochastic estimate of the convective area...

Stochasticc convection parameterization

Clouds are chaotic, difficult to predict, but above all, magnificent natural phenomena. There are different types of clouds: stratus, a layer of clouds that may produce drizzle, cirrus, clouds in the higher parts of the atmosphere, and cumulus, clouds that arise in convective updrafts. Thermals, rising air that is often used by birds and gliders...

A temperature conditioned markov chain model for predicting the dynamics of mosquito vectors of disease

Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of...