Publication Date:
2013-10-18
Description:
[1] This study provides an assessment of low-cloud properties retrieved from CloudSat, MODIS (MODerate Imaging Spectroradiometer), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) with the goal of exposing biases that hinder meaningful comparisons with the simulated cloud properties in global climate models (GCMs). Being pertinent to GCM comparisons, CloudSat is the only satellite that can provide the vertical structure of cloud water and ice content from space. Biases in CloudSat low cloud properties are found to be tied to problems involving cloud detection and algorithm retrieval failures related to precipitation and strict cloud screening procedures. We show MODIS and CloudSat cloud liquid water path (LWP) data agree when carefully screened for lack of precipitation, but, significantly depart in precipitating clouds due to rain water contamination of LWP in the CloudSat retrieval algorithm. The presence of drizzle and rain (occurring about 20 % of the time) is associated with different mean LWP, mean particle sizes, and optical depths of all low clouds and therefore, the radiative properties of the oceanic low clouds. Another, more significant source of the LWP bias stems from the apparent lack of cloud detection. On average the CPR misses clouds with adequate liquid and ice water retrievals as detected by MODIS in approximately 45 % of warm clouds with the bulk of the bias occurring in clouds below 1 km in the so called “ground clutter zone.” By incorporating additional sensors such as MODIS, the following results suggest that this LWP bias can be greatly reduced.
Print ISSN:
0148-0227
Topics:
Geosciences
,
Physics
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