As the primary locations of deep convective activity and unrestrained tropical wave dynamics, the tropical West Pacific and East Indian oceans are among the most important regions in the tropics. Given that most of the region consists of unpopulated expanses of ocean, observations of tropical atmospheric properties in this important region is exceptionally difficult. Only with the help of satellite observations are we capable of gleaning valuable data from this region, and our utilization of advanced analysis techniques allows us to gain more from these observations then would otherwise be possible. In that vein, this dissertation reports on the use of a unique statistical technique, long known to other fields of research, as applied to a combined-instrument satellite observation dataset over the warm pool region of the tropical West Pacific ocean. The statistical technique, known as k-means cluster analysis, is used to delineate self-similar populations of cloud type, hereafter referred to as cloud regimes, from frequency-distribution histograms of cloud-top height, cloud optical thickness, and rainfall amount. We will show that four primary cloud regimes exist in the tropical region discussed, that the four regimes vary primarily through differences in convective activity, and that these four cloud regimes exist in a coherent temporal structure that explains the long-observed variability in convective activity seen in the tropics. Combining this regime information with satellite observations, along with reanalysis data, we then examine the individual properties of each cloud regime. These observations give us the means to understand the forcings behind cloud regime change in the region. We confirm the structural properties of these regimes using analysis from a cloud-resolving model, and apply our new understanding of the mechanism behind this large-scale forcing to the governance of the tropical hydrologic cycle as a whole. The insights gained from this analysis have benefits to both the fields of atmospheric remote sensing, and of cloud- and climate modeling of the tropical atmosphere. Applications of this technique are of particular interest to researchers developing retrieval algorithms for latent heat profiles using active sensors such as the cloud-profiling radar aboard CloudSat.