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This paper presents the development and set up of a cloudscreening and data quality control algorithm for a star photometer based onCCD camera as detector. These algorithms are necessary for passiveremote sensing techniques to retrieve the columnar aerosol optical depth, δAe(λ), andprecipitable water vapor content, W, at nighttime. This cloud screeningprocedure consists of calculating moving averages of δAe(λ) and W underdifferent time-windows combined with a procedure for detecting outliers.Additionally, to avoid undesirable δAe(λ) and W fluctuations caused by theatmospheric turbulence, the data are averaged on 30 min. The algorithmis applied to the star photometer deployed in the city of Granada(37.16° N, 3.60° W, 680 m a.s.l.; South-East of Spain) for themeasurements acquired between March 2007 and September 2009. The algorithmis evaluated with correlative measurements registered by a lidar system andalso with all-sky images obtained at the sunset and sunrise of the previousand following days. Promising results are obtained detecting cloud-affecteddata. Additionally, the cloud screening algorithm has been evaluated underdifferent aerosol conditions including Saharan dust intrusion, biomassburning and pollution events

Cloud screening and quality control algorithm for star photometer data: assessment with lidar measurements and with all-sky images