Cloud screening and quality control algorithm for star photometer data: assessment with lidar measurements and with all-sky images
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AuthorPérez-Ramírez, Daniel; Lyamani, H.; Olmo Reyes, Francisco José; Whiteman, D. N.; Navas-Guzmán, Francisco; Alados Arboledas, Lucas
Copernicus Publications; European Geosciences Union (EGU)
Aerosol optical depthIberian peninsulaSun photometryEastern SpainAERONETGranadaAbsorptionRetrieval
Pérez-Ramírez, D.; et al. Cloud screening and quality control algorithm for star photometer data: assessment with lidar measurements and with all-sky images. Atmospheric Measurement Techniques, 5: 1585-1599 (2012). [http://hdl.handle.net/10481/32284]
SponsorshipThis work was supported by the Spanish Ministry of Science and Technology through projects CGL2008-01330-E/CLI (Spanish Lidar Network), CGL2010-18782 and CSD2007-00067; by the Andalusian Regional Government through projects P10-RNM-6299 and P08-RNM-3568; by the EU ACTRIS project (EU INFRA-2010-1.1.16-262254); and by the Postdoctoral Program of the University of Granada.
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, δAe(λ), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of δAe(λ) and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable δAe(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.