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

TLDR

CALIPSO, launched in 2005, will continuously measure the Earth's atmosphere for three years, but retrieving cloud and aerosol spatial and optical properties from its lidar backscatter data is challenged by large target distances, high satellite speed, low signal‑to‑noise ratios, and space‑based mass and power constraints. 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. The scheme uses three core retrieval algorithms—boundary location, feature classification, and optical properties analysis—whose outputs are combined through nested multi‑grid averaging to produce spatially resolved cloud and aerosol products. The authors demonstrate the retrievals’ interconnections by presenting data product examples that include feature top and base altitudes, feature type, and layer optical depths.

Abstract

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.