LAUSR

Indian summer monsoon (ISM) low‐pressure systems are considered as the lifeline for seasonal monsoon rainfall over the Indian region. However, the current models have limitations in predicting their characteristics and rainfall. This study assesses the influence of five cloud microphysical parameterization Schemes (MP) on simulations of 14 monsoon deep depressions (DD) using the weather research and forecasting (WRF) model. The simulations are carried out with a lead time up to 96 hr in a nested domain resolution of 27, 9, and 3 km. The five cloud microphysical parameterizations, that is, WRF Double moment (WDM6), WRF single moment (WSM6), Milbrandt (MIL), Thompson (THOM), and Aerosol Aware Thompson (AAT), are considered in this study, leading to a total of 70 simulations. The results are validated through composite analysis at a radial distance of 300 km from the respective storm centres. The choice of MP significantly impacts the key characteristics of the monsoon DDs such as rainfall, wind, temperature, moisture, humidity, hydrometeors, and associated convective processes. In terms of rainfall, it is found that WDM6 (AAT) has the best (worst) performance with a lead time up to day‐4. Besides, WDM6 has simulated the best result in terms of temperature and specific humidity. Examining the hydrometeors distribution around the storm intense convective region (i.e., 300 km), it is noted that frozen hydrometeors (i.e., ice, snow, and graupel) are mainly modulating the rainfall. There is a general tendency to overestimate snow and graupel among MP except WDM6. Further, ice hydrometeors are well represented in WDM6 compared with others leading to better rainfall forecast. The moisture flux convergence and absolute vorticity are two major mechanisms determining the convection within the storm's core zone, and WDM6 stands out the best among these schemes. The findings of this study are relevant and have direct consequences to the operational applications.