Eulerian-Lagrangian particle-based model for diffusional growth for the better parameterization of ISM clouds: A road map for improving climate model through small-scale model using observations
Moumita Bhowmik, Anupam Hazra, Suryachandra A. Rao, Lian-Ping Wang

TL;DR
This paper introduces a small-scale Eulerian-Lagrangian particle-based model to better understand diffusional growth rates and dispersion in clouds, aiming to improve climate and weather prediction models, especially for Indian monsoon precipitation.
Contribution
It provides the first calculation of diffusional growth rates using airborne data and a particle-based model, linking dispersion to droplet concentration for improved parameterization.
Findings
Diffusional growth rates range from 0.00025 to 0.0015 cm/s.
Relative dispersion varies with humidity, matching observations.
A linear relationship between dispersion and droplet concentration is established.
Abstract
The 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 rates are calculated using an Eulerian-Lagrangian particle-based small-scale model on in situ airborne measurement data of Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) during monsoon over Indian sub-continent. The results show that diffusional growth rates varies in the range of 0.00025 - 0.0015(cm/s). The generic problem of the overestimation of light rain in NWP models might be related with the choice of cm in the model. It is also shown from DNS experiment using Eulerian-Lagrangian particle-based small-scale model that the relative dispersion is constrained with average values in the range of ~ 0.2 - 0.37 (~ 0.1- 0.26) in less humid (more…
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Taxonomy
TopicsAtmospheric aerosols and clouds · Climate variability and models · Atmospheric chemistry and aerosols
