Quantification of Satellite Cloud Retrieval Uncertainties from Multiple Platforms Using A-Train Observations

Satellite cloud property retrievals are used for both initialization and validation of cloud models and global circulation models. It is therefore important to characterize the uncertainty of the retrievals and identify any existing biases. The NASA Langley Cloud and Radiation Group uses a common set of algorithms to retrieve cloud thermodynamic phase, altitude, optical depth, and water path from imagers such as AVHRR, and the CERES Clouds Working Group routinely retrieves these same properties from MODIS and VIIRS data. Active remote sensors in the A-Train Constellation provide an opportunity to independently assess the passive satellite retrievals. This paper presents a detailed analysis of cloud properties retrieved from AVHRR, VIIRS, and MODIS data. Cloud top and base altitudes were compared to CALIOP and CloudSat retrievals, respectively. Optical depths and ice water paths were compared to CALIOP values for thin cirrus clouds. Liquid water paths were compared to AMSR-E microwave retrievals. Appropriate quality control measures and caveats are discussed. Results are shown for tropical, mid-latitude, and polar regimes as well as for different viewing geometry and cloud types.