Investigating the cloud liquid water and droplet concentration relationship across cloud morphologies with machine learning

Abstract. The relationship between cloud droplet number concentration (Nd) and cloud liquid water path (𝓛) is investigated in the context of detailed information about cloud morphology derived from self-supervised machine learning (ML) interpretation of satellite imagery over the North Atlantic ocean. While different cloud morphologies occupy distinct regions in the 𝓛-Nd phase space, the “inverted-V” relationship observed between 𝓛 and Nd does not arise as a result of their distribution in this space, and most cloud morphologies independently exhibit the inverted-V pattern to some degree. A novel approach to investigate this relationship is demonstrated using continuous vector space image embedding representations of cloud morphology produced by the self-supervised ML. Analysis of non-linear regression between these cloud morphology representations, Nd, and 𝓛 indicates that while cloud morphology information can explain most of the variance in 𝓛 between cloud scenes, the portion of 𝓛 variance that can be explained by Nd is mostly independent of cloud morphology and explains the inverted-V pattern.