In 2010, Khouider developed a stochastic multicloud model to represent the missing variability that occurred in global climate models due to unresolved features of organized tropical convection. Furthermore, he coupled stochastic multicloud model to a simple tropical climate model consisting of a system of ODEs, and highlighted the dynamical features of the coupled model. Using a statistical inference method based on the Bayesian paradigm, he estimated the stochasticity of convection in terms of Giga-LES data. In his study conducted in 2019, he demonstrated the stochastic parametrization of organized convection, and also explored the performance of the stochastic multicloud model in a stand-alone mode where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. Later on, he successfully implemented the stochastic multicloud model in the CFSv2 GCM, used by the Indian Institute for Tropical Meteorology which led to huge improvements of the climate model in terms of simulating tropical modes of variability, including the MJO, monsoon intra-seasonal oscillations, and convectively coupled equatorial waves.
