Cloud‐edge mixing: Direct numerical simulation and observations in Indian Monsoon clouds

Abstract

A direct numerical simulation (DNS) with the decaying turbulence setup has been carried out to study cloud‐edge mixing and its impact on the droplet size distribution (DSD) applying thermodynamic conditions observed in monsoon convective clouds over Indian subcontinent during the Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX). Evaporation at the cloud‐edges initiates mixing at small scale and gradually introduces larger‐scale fluctuations of the temperature, moisture, and vertical velocity due to droplet evaporation. Our focus is on early evolution of simulated fields that show intriguing similarities to the CAIPEEX cloud observations. A strong dilution at the cloud edge, accompanied by significant spatial variations of the droplet concentration, mean radius, and spectral width, are found in both the DNS and in observations. In DNS, fluctuations of the mean radius and spectral width come from the impact of small‐scale turbulence on the motion and evaporation of inertial droplets. These fluctuations decrease with the increase of the volume over which DNS data are averaged, as one might expect. In cloud observations, these fluctuations also come from other processes, such as entrainment/mixing below the observation level, secondary CCN activation, or variations of CCN activation at the cloud base. Despite large differences in the spatial and temporal scales, the mixing diagram often used in entrainment/mixing studies with aircraft data is remarkably similar for both DNS and cloud observations. We argue that the similarity questions applicability of heuristic ideas based on mixing between two air parcels (that the mixing diagram is designed to properly represent) to the evolution of microphysical properties during turbulent mixing between a cloud and its environment.

Investigation of Turbulent Entrainment‐Mixing Processes With a New Particle‐Resolved Direct Numerical Simulation Model

“…Thus, all together, these results suggest that entrainment‐mixing processes is best parameterized using ψ 2 as a function of transition scale number, reinforcing the result of Lu, Liu, Niu, Krueger, and Wagner () based on the observational analysis and EMPM simulations. The similarity between our DNS results and the observational results is noteworthy, which somewhat echoes the results for the mixing diagram reported in Kumar et al ().…”

Physical Interpretation of Mixing Diagrams

“…Note that nonequilibrium trajectories on scattering diagrams obtained in LES of cloud‐clear‐air mixing were presented in studies by Andrejczuk et al (, ). Such trajectories obtained in DNS are also presented in studies by Kumar et al (, ). They show significant distance between original and final points in their scattering diagrams.…”

“…We would like to complete the discussion by comments concerning the role of high resolution in the investigation of mixing process. Utilization of high‐frequency measurements (10 Hz) allowed Kumar et al () to investigate the cloud edge structure more accurately and to reveal zones with significant changes of effective radius. We do not expect, however, that improvement of the resolution of measurements as well as increase in the model resolution may dramatically change our understanding about the nature of the role of mixing on cloud evolution.…”

Observational Relationship Between Entrainment Rate and Environmental Relative Humidity and Implications for Convection Parameterization

“…Cumulus clouds play important roles in vertical transport of energy and mass (Heiblum et al, 2016); convective parameterizations are critical to simulations of precipitation, Madden-Julian Oscillation, etc (Del Genio & Wu, 2010;Lu & Ren, 2016;Su et al, 1999;Wu et al, 2007;Zhang & McFarlane, 1995). Many studies have focused on entrainment-mixing near cloud edges (Kumar et al, 2013(Kumar et al, , 2017Lu et al, 2018;Malinowski et al, 2013;Small et al, 2013;Xue & Feingold, 2006). Entrainment rate (λ) is a key quantity in convective parameterizations (Blyth, 1993;Donner et al, 2016;Luo et al, 2010;Moser & Lasher-Trapp, 2017;Nie & Kuang, 2012).…”