Near-real-time processing of a ceilometer network assisted with sun-photometer data: monitoring a dust outbreak over the Iberian Peninsula
The 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 si...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-10-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/17/11861/2017/acp-17-11861-2017.pdf |
| Summary: | The 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.
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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 15 % 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. |
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| ISSN: | 1680-7316 1680-7324 |