Assessing the Influence of Coalescence on Precipitation Patterns: A Comparative Study with Disdrometer Data

Abstract

 The microphysical processes like nucleation, condensation, evaporation and coalescence are pivotal in precipitation formation. However, the parameterization of these processes are a major source of uncertainty in numerical weather prediction models (NWP) due to their complex and highly variable nature. Warm clouds, characterized by temperatures above freezing, play a significant role in precipitation generation through processes like collision-coalescence. In this study, we explore the coalescence dynamics of warm rain using a stochastic coalescence model (SCM) implemented within the Python framework for the super droplet model (PySDM). We utilized RD-80 Impact Disdrometer data to validate the model results, which provide insights into raindrop size distributions and rainfall characteristics. By Disdrometer, we mean an instrument used to measure the size and velocity of precipitation particles, providing valuable data for understanding precipitation dynamics. Our experimental setup involved simulating the coalescence process of super droplets within a designated coalescence cell and comparing the resulting mass density data with ground observations. By conducting 200 experiments spanning a duration of 20,000 seconds, we captured the evolution of droplet populations. Our findings demonstrate the utility of PySDM in bridging the observational gap between ground-level measurements and atmospheric droplet dynamics, thereby enhancing our understanding of warm rain processes and improving precipitation forecasting capabilities.