New Insights from Holographic Observations of Stratocumulus Clouds

The development and field deployment of holographic cloud imaging systems has enabled in situ measurements to provide unprecedented insights into cloud microstructure. In particular, the holographic images taken by these instruments enable a nuanced, local look within a cloud sub-volume well suited to address open questions related to cloud droplet spatial correlations, model parameter estimation, and drop size distribution structure and variability. In this work, we add to this contribution by analyzing data from the HOLODEC cloud imaging instrument deployed during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) field campaign.

Using a technique that combines holographic cloud data, statistical hypothesis testing, and machine learning we present an analysis that reveals that stratocumulus clouds seem to look far different locally than when their properties are horizontally averaged. The analysis suggests that no part of the cloud resembles the average but, instead, local drop size distributions tend to be quite narrow and occur in spatial “pockets”. Upon spatial averaging, these locally narrow “pockets” combine to form the wider and longer-tailed distributions familiar from previous cloud-probe measurements and often used in cloud microphysics models.

In this presentation, we will summarize the analysis technique, show that the observed “pockets” generally do not seem to be well correlated to other commonly tracked microphysical variables (e.g. LWC, vertical velocity, etc.), and discuss how this analysis may give new insights regarding the value and variability of estimated cloud process rates within stratocumulus clouds.