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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/acp-2020-857
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-857
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Sep 2020

25 Sep 2020

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This preprint is currently under review for the journal ACP.

Atmospheric Boundary Layer height estimation from aerosol lidar: a new approach based on morphological image processing techniques

Gemine Vivone1, Giuseppe D'Amico1, Donato Summa1, Simone Lolli1, Aldo Amodeo1, Daniele Bortoli2,3, and Gelsomina Pappalardo1 Gemine Vivone et al.
  • 1Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Tito Scalo, Potenza, Italy
  • 2Institute of Earth Sciences (ICT), Pole of Evora, Evora, Portugal
  • 3Physics Department, University of Evora, Evora, Portugal

Abstract. The Atmospheric Boundary Layer (ABL) represents the lowermost part of the atmosphere directly in contact with the Earth surface. The estimation of its depth is of crucial importance in meteorology and for anthropogenic pollution studies. The ABL Height (ABLH) measurements are usually far from being adequate, both spatially and temporally. Thus, different remote sensing sources can be of great help in growing both the spatial and temporal ABLH measurement capabilities. To this aim, aerosol backscatter profiles are widely used as proxy to retrieve the ABLH. Hence, the scientific community is making remarkable efforts in developing automatic ABLH retrieval algorithms applied to lidar observations. In this paper, we propose a ABLH estimation algorithm based on image processing techniques applied to the composite image of the total attenuated backscatter coefficient. A pre-processing step is applied to the composite total backscatter image based on morphological filters to properly set-up and adjust the image to detect edges. As final step, the detected edges are post-processed through both mathematical morphology and an object-based analysis. The performance of the proposed approach is assessed on real data acquired by two different lidar systems, deployed in Potenza (Italy) and Evora (Portugal), belonging to the EARLINET network. The proposed approach has shown higher performance than the benchmark consisting of some state-of-the-art ABLH estimation methods.

Gemine Vivone et al.

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Gemine Vivone et al.

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