Cluster Analysis of A-Train Data: Approximating the Vertical Cloud Structure of Oceanic Cloud Regimes

AbstractModerate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, 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. A-Train 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 (“...

[1]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[2]  Tobias Wehr,et al.  A 3D cloud‐construction algorithm for the EarthCARE satellite mission , 2011 .

[3]  G. Tselioudis,et al.  Objective identification of cloud regimes in the Tropical Western Pacific , 2003 .

[4]  W. Paul Menzel,et al.  The MODIS cloud products: algorithms and examples from Terra , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  J. Norris,et al.  Cluster analysis of midlatitude oceanic cloud regimes: mean properties and temperature sensitivity , 2010 .

[6]  Steven D. Miller,et al.  Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations , 2014 .

[7]  Jonathan H. Jiang,et al.  Touring the atmosphere aboard the A-Train , 2010 .

[8]  Gerald G. Mace,et al.  Cluster analysis of tropical clouds using CloudSat data , 2007 .

[9]  David W. Aha,et al.  Improvement to a Neural Network Cloud Classifier , 1996 .

[10]  Cirrus Cloud Properties and the Large-Scale Meteorological Environment: Relationships Derived from A-Train and NCEP–NCAR Reanalysis Data , 2013 .

[11]  G. Tselioudis,et al.  Global Weather States and Their Properties from Passive and Active Satellite Cloud Retrievals , 2013 .

[12]  C. Kummerow,et al.  A Clustering Approach to Compare Cloud Model Simulations to Satellite Observations , 2012 .

[13]  S. Klein,et al.  Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model , 2005 .

[14]  W. Paul Menzel,et al.  Cloud Properties inferred from 812-µm Data , 1994 .

[15]  E. Williams,et al.  The Identification and Verification of Hazardous Convective Cells over Oceans Using Visible and Infrared Satellite Observations , 2008 .

[16]  Zhien Wang,et al.  A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements , 2010 .

[17]  G. Stephens,et al.  Comparison of regime‐sorted tropical cloud profiles observed by CloudSat with GEOS5 analyses and two general circulation model simulations , 2011 .