Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of twodimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June–August) and winter (December–February) seasons in separate cluster analyses. ATrain data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (‘‘GEOPROF-lidar’’) data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloudtop height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed verticalprofile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene.