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

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

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

In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EAR-LINET 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 preprocessing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns

Atmospheric boundary layer height estimation from aerosol lidar: A new approach based on morphological image processing techniques

The atmospheric boundary layer (ABL) represents the lowermost part of the atmosphere directly in contact with the Earth's surface. The estimation of its depth is of crucial importance in meteorology and for anthropogenic pollution studies. 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 a 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 Évora (Portugal), belonging to the European Aerosol Research Lidar Network (EARLINET). The proposed approach has shown higher performance than the benchmark consisting of some state-of-The-Art ABLH estimation methods. © 2021 Copernicus GmbH. All rights reserved.ACTRIS (https://www.actris.eu/, last access: 15 March 2021) has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement nos. 654109 (ACTRIS-2), 759530 (ACTRIS-PPP), 871115 (ACTRIS-IMP) and 824068 (ENVRI-FAIR), and previously from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 262254. The Portuguese lidar station is also supported by national funds through FCT – Foundation for Science and Technology, I. P., within the scope of projects UIDB/04683/2020 and UIDP/04683/2020, and also through project TOMAQAPA (PTDC/CTAMET/29678/2017). Moreover, the authors gratefully acknowledge CloudNET for providing ECMWF and GDAS atmospheric forecasts for all the measurement cases included in this study

CEOS Intercalibration of Ground-Based Spectrometers and Lidars: Final Report

The ESA CEOS Intercalibration project concentrated on important calibration activities addressing three key components of the ground-based network ground-truthing capacity in Europe, namely the Dobson/Brewer network of ozone spectrophotometers, the aerosol lidar EARLINET network and the UV-Vis MAXDOAS technique for air quality remote-sensing. This document summarizes activities and achievements during the third part of the ESA CEOS Intercalibration project. The period covered by this report extends from February 2012 until October 2012

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

In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EAR-LINET 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 preprocessing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns

CEOS Intercalibration of Ground-Based Spectrometers and Lidars: Second Progress Report

This document summarizes activities and achievements during the second part of the ESA CEOS Intercalibration project. The period covered by this report extends from February 2011 until January 2012

EARLINET: towards an advanced sustainable European aerosol lidar network

The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.Peer ReviewedPostprint (published version

One year of tropospheri clidar measurements of aerosol extinction and backscatter

The aerosol lidar system operational at IMAA-CNR in Tito Scalo (PZ) (Southern Italy, 40°36'N, 15°44'E, 820 m above sea level) is part of the EARLINET project. Systematic lidar measurements of aerosol backscatter and extinction in the troposphere have been performed since May 2000. Aerosol backscatter measurements were performed at both 355 nm and 532 nm, while aerosol extinction coeffi cient were retrieved from simultaneous N2 Raman backscatter signals at 386.6 nm. The observations were performed on a regular schedule of two night time measurements per week (around sunset) and one daytime measurement per week (around 13:00 UTC). Furthermore, special observations concerning Saharan dust outbreaks have been carried out. Starting in May 2000 the lidar measurements performed in Tito Scalo have been collected and analysed. Preliminary results regarding the fi rst year of measurements are reported. In particular, the evolution of the aerosol integrated backscatter and extinction as well as of the mean value of the lidar ratio in the whole aerosol layer is reported. Results show clear evidence of seasonal variation of the observed parameters, with higher values and greater variability during summertime

An automatic aerosol classification for earlinet: application and results

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented. © The Authors, published by EDP Sciences, 2018.Peer ReviewedPostprint (published version

An EARLINET early warning system for atmospheric aerosol aviation hazards

A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector. © 2020 Author(s)