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Article

Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC)

1
Institut Pierre Simon Laplace (IPSL), CNRS, École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau Cedex, France
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Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace (LMD/IPSL), CNRS, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, Sorbonne Université, 91128 Palaiseau Cedex, France
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Institut Pierre-Simon Laplace (IPSL), UVSQ, Université Paris-Saclay, École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau Cedex, France
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Department of Meteorology, University of Reading, Reading RG6 6ET , UK
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Federal Office of Meteorology and Climatology MeteoSwiss, 8058 Zurich, Switzerland
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Institute for Atmospheric and Climate Science (IAC), ETH Zurich, 8092 Zurich, Switzerland
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Ludwig Maximilian University of Munich, 80539 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3259; https://doi.org/10.3390/rs12193259
Received: 1 September 2020 / Revised: 2 October 2020 / Accepted: 5 October 2020 / Published: 7 October 2020
(This article belongs to the Special Issue Remote Sensing of the Atmospheric Boundary Layer)
A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales. View Full-Text
Keywords: boundary layer height; lidar; ceilometer; ALC network; E-PROFILE boundary layer height; lidar; ceilometer; ALC network; E-PROFILE
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MDPI and ACS Style

Kotthaus, S.; Haeffelin, M.; Drouin, M.-A.; Dupont, J.-C.; Grimmond, S.; Haefele, A.; Hervo, M.; Poltera, Y.; Wiegner, M. Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC). Remote Sens. 2020, 12, 3259. https://doi.org/10.3390/rs12193259

AMA Style

Kotthaus S, Haeffelin M, Drouin M-A, Dupont J-C, Grimmond S, Haefele A, Hervo M, Poltera Y, Wiegner M. Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC). Remote Sensing. 2020; 12(19):3259. https://doi.org/10.3390/rs12193259

Chicago/Turabian Style

Kotthaus, Simone, Martial Haeffelin, Marc-Antoine Drouin, Jean-Charles Dupont, Sue Grimmond, Alexander Haefele, Maxime Hervo, Yann Poltera, and Matthias Wiegner. 2020. "Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC)" Remote Sensing 12, no. 19: 3259. https://doi.org/10.3390/rs12193259

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