Cumulus parameterization (CP) in state-of-the-art global climate models (GCM) is based on the quasi-equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying atmospheric large-scale state. This view is not compatible with the organization and dynamical interactions across multiple scales of cloud systems in the tropics and progress in this research area was slow over decades despite the widely recognized major shortcomings. Novel ideas on how to represent key physical processes of moist convection-large-scale interaction to overcome the QEA have surged recently. The stochastic multicloud model (SMCM) CP in particular mimics the dynamical interactions of multiple cloud types that characterize organized tropical convection. Here, the SMCM is used to modify the Zhang-McFarlane (ZM) CP by changing the way in which the bulk mass flux is calculated. This is done by introducing a stochastic ensemble of plumes characterized by randomly varying detrainment level distributions based on the cloud area fraction (CAF) of the SMCM. The SMCM is here extended to include shallow cumulus clouds resulting in a unified shallow-deep CP. The new stochastic multicloud plume CP is validated against the control ZM scheme in the context of the single column Community Climate Model of the National Center for Atmospheric Research using six test-cases including both tropical ocean and midladitude land convection. Some key features of the SMCM SP such as it capability to represent the tri-modal nature of organized convection are emphasized.

The performance of present operational global forecast system (GFS) at T1534 (~12.5 km) horizontal resolution with modified fractional cloud condensate to precipitation conversion parameter in the simplified Arakawa-Schubert (SAS) convection scheme is evaluated for the summer monsoon seasons of 2018 and 2019 over the Indian region. The modified parameter has the form of an exponential decreasing function of temperature above the freezing level, whereas below the freezing level, it is constant and similar to default conversion parameter. The results reveal that the GFS T1534 with modified conversion parameter (EXPT) shows better fidelity in forecasting the mean summer monsoon rainfall over the Indian continent region as compared to default GFS T1534 (CTRL). The rainfall probability distribution function analysis indicates notable improvement in forecasting moderate and heavier category rainfall in EXPT as compared to CTRL. The improved distribution of total rainfall is found be contributed by the proper forecasting of convective and large-scale rainfall in EXPT. It is likely that the reduced rate of conversion of cloud condensate to convective precipitation above the freezing level leads to decrease in convective rainfall, which eventually increases the moisture in the upper level through detrainment and hence enhancement in large-scale rainfall. Further, EXPT shows relative improvement in forecasting outgoing longwave radiation, wind circulation, cloud fraction, dynamical-thermodynamical processes and moist-convective feedback through better lower tropospheric moistening over the Indian region. Finally, various skill score analyses suggest that EXPT shows better skill in predicting moderate and heavier category rainfall with longer lead time over the continental India.