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  • Author or Editor: Jackson Tan x
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Nayeong Cho
,
Jackson Tan
, and
Lazaros Oreopoulos

Abstract

We present an updated cloud regime (CR) dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically, joint histograms that partition cloud fraction within distinct combinations of cloud-top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal-area grid and prespecified 3-h UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1° daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal-angle grids. Both editions consist of 11 clusters whose centroids are nearly identical. We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

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Daeho Jin
,
Lazaros Oreopoulos
,
Dongmin Lee
,
Jackson Tan
, and
Nayeong Cho

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.

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