Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds

TL;DR

This paper introduces a novel visualization method using Variational Autoencoders to analyze droplet size distributions in cloud simulations, revealing insights into aerosol-cloud interactions and precipitation processes.

Contribution

It presents a new approach employing VAEs for visualizing high-dimensional droplet data, enhancing understanding of microphysical cloud processes beyond traditional clustering methods.

Findings

Droplet spectrum evolution is similar across different aerosol levels.

Precipitation initiation processes are alike despite different onset times.

VAE-based visualizations improve interpretation of droplet dynamics.

Abstract

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.