Aerosol models for the CALIPSO lidar inversion algorithms

Ali H. Omar; David M. Winker; Jae-Gwang Won

doi:10.1117/12.511067

12 January 2004 Aerosol models for the CALIPSO lidar inversion algorithms

Ali H. Omar, David M. Winker, Jae-Gwang Won

Author Affiliations +

Proceedings Volume 5240, Laser Radar Technology for Remote Sensing; (2004) https://doi.org/10.1117/12.511067
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

We use measurements and models to develop aerosol models for use in the inversion algorithms for the Cloud Aerosol Lidar and Imager Pathfinder Spaceborne Observations (CALIPSO). Radiance measurements and inversions of the AErosol RObotic NETwork (AERONET) are used to group global atmospheric aerosols using optical and microphysical parameters. This study uses more than 10⁵ records of radiance measurements, aerosol size distributions, and complex refractive indices to generate the optical properties of the aerosol at more 200 sites worldwide. These properties together with the radiance measurements are then classified using classical clustering methods to group the sites according to the type of aerosol with the greatest frequency of occurrence at each site. Six significant clusters are identified: desert dust, biomass burning, urban industrial pollution, rural background, marine, and dirty pollution. Three of these are used in the CALIPSO aerosol models to characterize desert dust, biomass burning, and polluted continental aerosols. The CALIPSO aerosol model also uses the coarse mode of desert dust and the fine mode of biomass burning to build a polluted dust model. For marine aerosol, the CALIPSO aerosol model uses measurements from the SEAS experiment. In addition to categorizing the aerosol types, the cluster analysis provides all the column optical and microphysical properties for each cluster.

Citation Download Citation

Ali H. Omar, David M. Winker, and Jae-Gwang Won "Aerosol models for the CALIPSO lidar inversion algorithms", Proc. SPIE 5240, Laser Radar Technology for Remote Sensing, (12 January 2004); https://doi.org/10.1117/12.511067

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