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

Abstract. This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, δ_Ae(λ), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of δ_Ae(λ) and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable δ_Ae(λ) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is 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 the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.