Estimation of Diffusional Growth Rate and Reassessing Existing Parameterizations for Monsoon Precipitating Clouds: A Process-Based Approach

Abstract

Quantitative prediction of the intensity of rainfall events (light or heavy) has remained a challenge in Numerical Weather Prediction (NWP) models. For the first time, the mean coefficient of diffusional growth rate (\(c_m\)) is calculated using a Eulerian-Lagrangian particle-based model on in situ airborne measurement data from the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) during monsoon over the Indian sub-continent. The results show that \(c_m\) varies in the range of \(\sim 0.25\times 10^{-3} - 1.5\times 10^{-3}\) (cm \(s^{-1}\)). The generic problem of overestimation of light rain in NWP models might be related to the choice of \(c_m\) in the model. It is also shown from a direct numerical simulation (DNS) experiment using small-scale model that relative dispersion (\(\epsilon \)) is constrained with average values in the range of \(\sim \) 0.2\(-\)0.37 (\(\sim \) 0.1\(-\)0.26) in less humid (more humid) conditions. This is in agreement with in situ airborne observation (\(\epsilon \) \(\sim \) 0.36) and previous studies over the Indian sub-continent. The linear relationship between relative dispersion (\(\epsilon \)) and cloud droplet number concentration (NC) is obtained using CAIPEEX. The present study compares different exciting parameterizations for the cloud-to-rain “autoconversion” and effective radius using a sophisticated parcel-DNS model guided by CAIPEEX observation. The dispersion-based ‘autoconversion’ and effective radius parameterization schemes for the Indian region must be useful for the calculation of Indian summer monsoon precipitation in the general circulation model. The present study also provides valuable guidance for parameterizing the effective radius, which is important for the radiation scheme.