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The CloudSat radar‐lidar geometrical profile product (RL‐GeoProf): Updates, improvements, and selected results

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Derived by combining data from the CloudSat radar and the CALIPSO lidar, the so‐called radar‐lidar geometrical profile product (RL‐GeoProf) allows for characterization of the vertical and spatial structure of hydrometeor layers. RL‐GeoProf is one of the standard data products of the CloudSat Project. In this paper we describe updates to the RL‐GeoProf algorithm. These improvements include a significant fix to the CALIPSO Vertical Feature Mask (VFM) that more accurately renders the occurrence frequencies of low‐level clouds over the global oceans. Additionally, we now account for the navigational challenges associated with coordinated measurements of the two instruments by providing additional diagnostic information in the data files. We also document how the along‐track averaging of the VFM influences the accuracy of RL‐GeoProf. We find that the 5 km averaged VFM when merged with data from the CloudSat radar provides a global description of cloud occurrence that best matches an independently derived cloud mask from Moderate Resolution Imaging Spectroradiometer (MYD35) over daytime global oceans. Expanding on the comparison with MYD35, we demonstrate that RL‐GeoProf and MYD35 closely track the monthly averaged cloud occurrence fraction during a 4 year span of measurements. A more detailed examination reveals latitudinal dependency in the comparison. Specifically, MYD35 tends to be significantly low biased relative to RL‐GeoProf over the Polar Regions when cloud layers present low visible and thermal contrast with underlying surfaces. Additional analyses examine the geometrically defined hydrometeor layer occurrence climatologies over select regions of the Earth and the seasonal variations of low‐based and low‐topped cloud cover. A revised radar‐lidar geometrical profile algorithm for CloudSat and CALIPSO Selected cloud occurrence statistics from RL‐GeoProf and MODIS
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Keywords: clouds; lidar; radar; remote sensing; satellite

Document Type: Research Article

Publication date: August 16, 2014

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