The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite will be launched in April of 2005, and will make continuous measurements of the Earth's atmosphere for the following three years. Retrieving the spatial and optical properties of clouds and aerosols from the CALIPSO lidar backscatter data will be confronted by a number of difficulties that are not faced in the analysis of ground-based data. Among these are the very large distance from the target, the high speed at which the satellite traverses the ground track, and the ensuing low signal-to-noise ratios that result from the mass and power restrictions imposed on space-based platforms. In this work we describe an integrated analysis scheme that employs a nested, multi-grid averaging technique designed to optimize tradeoffs between spatial resolution and signal-to-noise ratio. We present an overview of the three fundamental retrieval algorithms (boundary location, feature classification, and optical properties analysis), and illustrate their interconnections using data product examples that include feature top and base altitudes, feature type (i.e., cloud or aerosol), and layer optical depths.

Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products