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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 2
Atmos. Meas. Tech., 9, 491–507, 2016
https://doi.org/10.5194/amt-9-491-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: EARLINET, the European Aerosol Research Lidar Network

Atmos. Meas. Tech., 9, 491–507, 2016
https://doi.org/10.5194/amt-9-491-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Feb 2016

Research article | 12 Feb 2016

EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

Giuseppe D'Amico1, Aldo Amodeo1, Ina Mattis2,4, Volker Freudenthaler3, and Gelsomina Pappalardo1 Giuseppe D'Amico et al.
  • 1Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), Tito Scalo, Potenza, Italy
  • 2Leibniz Institute for Tropospheric Research, Leipzig, Germany
  • 3Ludwig-Maximilians-Universität, Meteorologisches Institut Experimentelle Meteorologie, Munich, Germany
  • 4Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeißenberg, Germany

Abstract. In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations.

During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented.

The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product.

Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

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