Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds
This work addresses the problem of interpreting complex cloud microphysics data for climate scientists, but it is incremental as it applies an existing method (VAEs) to a new domain.
The paper tackled the challenge of analyzing high-dimensional droplet size distributions from Large Eddy Simulations of shallow clouds by using Variational Autoencoders to create intuitive visualizations, revealing that droplet spectrum evolution is similar across aerosol levels but occurs at different paces, suggesting precipitation initiation processes are alike despite timing variations.
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.