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12 January 2005 Sampling uncertainties in observing the global aerosol with a nadir-viewing satellite lidar
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Abstract
In an attempt to better understanding climate and better comprehend the effects of clouds and aerosols on the Earth’s Radiation Budget, NASA has been developing several satellite missions. Among them, the Cloud-Aerosol-Lidar and Infrared Pathfinder Spaceborne Observation (CALIPSO) mission will observe clouds and aerosols with a combination of lidar and passive instruments. CALIPSO will fly in formation with EOS Aqua, EOS Aura, Cloudsat and Parasol. This novel satellite formation will provide a unique comprehensive data set of cloud and aerosol optical and physical properties, and radiative fluxes. In this paper, the characterization of global aerosol properties with sparsely sampled observations is investigated using a dataset of aerosol optical depth (AOD) from the MATCH climate model. MATCH is an offline Chemistry and Transport Model (ChTM) primarily developed by NCAR that includes a number of aerosol sources as well as a variety of transformation and removal mechanisms. The CALIPSO satellite is "flown" through this dataset and the aerosol optical depths at the CALIPSO footprint locations are sampled to produce an AOD subset. Averages computed from the subset are compared with averages from the full model output to investigate the magnitude of uncertainties due to sparse sampling of the aerosol field. Initially, uncertainties in satellite sparsely sampled measurements of global aerosol distribution are quantified in terms of zonal averages. The goal of this effort is to determine the correct satellite average scaling to accurately represent global aerosol coverage. Ultimately, sampling errors will also be assessed at regional scales.
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Maria Santa Maria and David M. Winker "Sampling uncertainties in observing the global aerosol with a nadir-viewing satellite lidar", Proc. SPIE 5653, Lidar Remote Sensing for Industry and Environmental Monitoring V, (12 January 2005); https://doi.org/10.1117/12.583098
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