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Near-real-time processing of a ceilometer network assisted with sun-photometer data: Monitoring a dust outbreak over the Iberian Peninsula

AutorCazorla, Alberto; Casquero-Vera, Juan Andrés; Román, Roberto; Guerrero-Rascado, J. L.; Toledano, Carlos; Cachorro, Victoria Eugenia; Orza, José Antonio G.; Cancillo, María Luísa; Serrano, A.; Titos, Gloria; Pandolfi, Marco; Alastuey, Andrés; Hanrieder, Natalie; Alados-Arboledas, Lucas
Palabras claveAerosol property
Data processing
Fecha de publicación9-oct-2017
EditorEuropean Geosciences Union
CitaciónAtmospheric Chemistry and Physics 17 (19): 11861-11876 (2017)
ResumenThe interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions. In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by co-located multiwavelength lidar measurements. A difference smaller than 15ĝ in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation. © 2017 Author(s).
Versión del editorhttps://doi.org/10.5194/acp-17-11861-2017
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