Abstract
To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.
Significance Statement
Clouds and precipitation are related in close but complex ways. In this work we attempt to provide a classification of daytime cloud–precipitation co-occurrence and covariability, with emphasis on tropical regions. We achieve such a classification using a k-means clustering algorithm applied to cloud fraction and precipitation intensity histograms, which yields “hybrid” clusters, that is, groups whose members have similar cloud and precipitation properties. These hybrid clusters reveal more detailed features of coincident daytime cloud and precipitation systems than do clusters in which clouds and precipitation are treated separately. Moreover, the realization that precipitation features associated with high and optically thick clouds have very distinct patterns enables hybrid cluster prediction solely on the basis of precipitation information. This has the important implication that rarer cloud observations can be extended to the more frequent (including nighttime) precipitation domain.
Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0253.s1.
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