The relationship between cloud droplet number concentration (<em>N<sub>d</sub></em>) 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-<em>N<sub>d</sub></em> phase space, the “inverted-V” relationship observed between 𝓛 and <em>N<sub>d</sub></em> 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, <em>N<sub>d</sub></em>, 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 <em>N<sub>d</sub></em> is mostly independent of cloud morphology and explains the inverted-V pattern.