Exposing biases in retrieved low cloud properties from CloudSat: A guide for...

Christensen, M. W., G. L. Stephens, and M. Lebsock (2013), Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data, J. Geophys. Res., 118, 12120-12131, doi:10.1002/2013JD020224.

This study provides an assessment of low cloud properties retrieved from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation 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 that 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 Cloud Profiling Radar 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.

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