Cloud-screening and quality control algorithms for the AERONET database

Automatic globally distributed networks for monitoring aerosol optical dept h provide measurements of natural and anthropogenic aerosol loading, which is important in many local and regional studies as well as global change re search investigations. The strength of such networks relies on imposing a s tandardization of measurement and processing, allowing multiyear and large- scale comparisons. The development of the Aerosol Robotic Network (AERONET) for systematic ground-based sunphotometer measurements of aerosol optical depth is an essential and evolving step in this process. The growing databa se requires the development of a consistent, reproducible, and system-wide cloud-screening procedure. This paper discusses the methodology and justifi cation of the cloud-screening algorithm developed for the AERONET database. The procedure has been comprehensively tested on experimental data obtaine d in different geographical and optical conditions. These conditions includ e biomass burning events in Brazil and Zambia, hazy summer conditions in th e Washington DC area, clean air advected from the Canadian Arctic, and vari able cloudy conditions. For various sites our screening algorithm eliminate s from similar to 20% to 50% of the initial data depending on cloud conditi ons. Certain shortcomings of the proposed procedure are discussed. (C) Else vier Science Inc., 2000.