Cloud-Screening and Quality Control Algorithms for the AERONET Database

Abstract

Automatic globally distributed networks for monitoring aerosol optical depth provide measurements of natural and anthropogenic aerosol loading, which is important in many local and regional studies as well as global change research investigations. The strength of such networks relies on imposing a standardization 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 database requires the development of a consistent, reproducible, and system-wide cloud-screening procedure. This paper discusses the methodology and justification of the cloud-screening algorithm developed for the AERONET database. The procedure has been comprehensively tested on experimental data obtained in different geographical and optical conditions. These conditions include biomass burning events in Brazil and Zambia, hazy summer conditions in the Washington DC area, clean air advected from the Canadian Arctic, and variable cloudy conditions. For various sites our screening algorithm eliminates from ~20% to 50% of the initial data depending on cloud conditions. Certain shortcomings of the proposed procedure are discussed.