Key parameters for droplet evaporation and mixing at the cloud edge

TL;DR

This paper develops a statistical model to better understand droplet evaporation and mixing at cloud edges, reconciling simulation results with observational data to improve climate and weather predictions.

Contribution

It introduces a new statistical framework that accurately describes evaporation-mixing processes and infers droplet history from observational data.

Findings

The model matches direct numerical simulation results.

It explains discrepancies between simulations and observations.

Provides insights into cloud droplet dynamics.

Abstract

The distribution of liquid water in ice-free clouds determines their radiative properties, a significant source of uncertainty in weather and climate models. Evaporation and turbulent mixing cause a cloud to display large variations in droplet-number density, but quite small variations in droplet size [Beals et al. (2015)]. Yet direct numerical simulations of the joint effect of evaporation and mixing near the cloud edge predict quite different behaviors, and it remains an open question how to reconcile these results with the experimental findings. To infer the history of mixing and evaporation from observational snapshots of droplets in clouds is challenging because clouds are transient systems. We formulated a statistical model that provides a reliable description of the evaporation-mixing process as seen in direct numerical simulations, and allows to infer important aspects of the history of observed droplet populations, highlighting the key mechanisms at work, and explaining the differences between observations and simulations.