EARLINET: potential operationality of a research network

Sicard, Michaël; D’Amico, Giuseppe; Comerón, Adolfo; Mona, Lucia; Alados-Arboledas, L.; Amodeo, Aldo; Baars, Holger; Baldasano, J. M.; Belegante, Livio; Binietoglou, Ioannis; Bravo-Aranda, Juan Antonio; Fernández, Alfonso; Fréville, Patrick; Garcia-Vizcaino, David; Giunta, Aldo; Granados-Muñoz, María José; Guerrero-Rascado, Juan Luís; Hadjimitsis, Diofantos G.; Haefele, Alexander; Hervo, Maxime; Iarlori, M.; Kokkalis, Panayotis; Lange, Diego; Mamouri, Rodanthi-Elisavet; Mattis, Ina; Molero, Francisco; Montoux, Nadège; Muñoz, Álvaro; Muñoz-Porcar, Constantino; Navas-Guzmán, Francisco; Nicolae, Doina; Nisantzi, Argyro; Papagiannopoulos, Nikolaos; Papayannis, A.; Pereira, S.; Preißler, Jana; Pujadas, M.; Rizi, V.; Rocadenbosch, Francesc; Sellegri, Karine; Simeonov, V.; Tsaknakis, G.; Wagner, Frank; Pappalardo, Gelsomina

doi:10.5194/amt-8-4587-2015

Cited by 41 publications

(39 citation statements)

References 74 publications

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“…Next, Sicard et al (2015) report about an experiment around the Mediterranean basin in July 2012. Eleven EARLINET stations performed continuous measurements over a period of 72 h. This experiment is used as a demonstration for the capability of EARLINET to monitor special events and to deliver SCC products in real time or near realtime.…”

Section: Validationmentioning

confidence: 99%

“…Unfortunately, the SCC was not yet finished at that time, and thus, the publication of scientifically quality assured EARLINET data of this event lasted 2 years (Pappalardo et al, 2013). Meanwhile, the SCC has become available and Sicard et al (2015) demonstrated its ability to deliver backscatter and extinction profiles in near real-time during an intense field campaign in the Mediterranean basin in summer 2012.…”

Section: Introductionmentioning

confidence: 99%

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EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products

et al. 2016

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Abstract. In this paper we present the automated software tool ELDA (EARLINET Lidar Data Analyzer) for the retrieval of profiles of optical particle properties from lidar signals. This tool is one of the calculus modules of the EARLINET Single Calculus Chain (SCC) which allows for the analysis of the data of many different lidar systems of EARLINET in an automated, unsupervised way. ELDA delivers profiles of particle extinction coefficients from Raman signals as well as profiles of particle backscatter coefficients from combinations of Raman and elastic signals or from elastic signals only. Those analyses start from pre-processed signals which have already been corrected for background, range dependency and hardware specific effects. An expert group reviewed all algorithms and solutions for critical calculus subsystems which are used within EARLINET with respect to their applicability for automated retrievals. Those methods have been implemented in ELDA. Since the software was designed in a modular way, it is possible to add new or alternative methods in future. Most of the implemented algorithms are well known and well documented, but some methods have especially been developed for ELDA, e.g., automated vertical smoothing and temporal averaging or the handling of effective vertical resolution in the case of lidar ratio retrievals, or the merging of near-range and far-range products. The accuracy of the retrieved profiles was tested following the procedure of the EARLINET-ASOS algorithm inter-comparison exercise which is based on the analysis of synthetic signals. Mean deviations, mean relative deviations, and normalized root-mean-square deviations were calculated for all possible products and three height layers. In all cases, the deviations were clearly below the maximum allowed values according to the EARLINET quality requirements.

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Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products

et al. 2016

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“…This conversion is often used to convert lidar-derived optical properties into mass concentration to test and evaluate transport models (Pérez et al, 2006;Sicard et al, 2015). Lately, POLIPHON is also used to extract the fractions of the high, moderate or low depolarizing particles from the total extinction, which can then be converted separately into mass concentration Ansmann, 2014, 2017).…”

Section: Introductionmentioning

confidence: 99%

Separation of the optical and mass features of particle components in different aerosol mixtures by using POLIPHON retrievals in synergy with continuous polarized Micro-Pulse Lidar (P-MPL) measurements

et al. 2018

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Abstract. The application of the POLIPHON (POlarizationLIdar PHOtometer Networking) method is presented for the first time in synergy with continuous 24/7 polarized MicroPulse Lidar (P-MPL) measurements to derive the vertical separation of two or three particle components in different aerosol mixtures, and the retrieval of their particular optical properties. The procedure of extinction-to-mass conversion, together with an analysis of the mass extinction efficiency (MEE) parameter, is described, and the relative mass contribution of each aerosol component is also derived in a further step. The general POLIPHON algorithm is based on the specific particle linear depolarization ratio given for different types of aerosols and can be run in either 1-step (POL-1) or 2 steps (POL-2) versions with dependence on either the 2-or 3-component separation. In order to illustrate this procedure, aerosol mixing cases observed over Barcelona (NE Spain) are selected: a dust event on 5 July 2016, smoke plumes detected on 23 May 2016 and a pollination episode observed on 23 March 2016. In particular, the 3-component separation is just applied for the dust case: a combined POL-1 with POL-2 procedure (POL-1/2) is used, and additionally the fine-dust contribution to the total fine mode (fine dust plus non-dust aerosols) is estimated. The high dust impact before 12:00 UTC yields a mean mass loading of 0.6 ± 0.1 g m −2 due to the prevalence of Saharan coarse-dust particles. After that time, the mean mass loading is reduced by two-thirds, showing a rather weak dust incidence. In the smoke case, the arrival of fine biomass-burning particles is detected at altitudes as high as 7 km. The smoke particles, probably mixed with less depolarizing non-smoke aerosols, are observed in air masses, having their origin from either North American fires or the Arctic area, as reported by HYSPLIT backtrajectory analysis. The particle linear depolarization ratio for smoke shows values in the 0.10-0.15 range and even higher at given times, and the daily mean smoke mass loading is 0.017 ± 0.008 g m −2 , around 3 % of that found for the dust event. Pollen particles are detected up to 1.5 km in height from 10:00 UTC during an intense pollination event with a particle linear depolarization ratio ranging between 0.10 and 0.15. The maximal mass loading of Platanus pollen particles is 0.011 ± 0.003 g m −2 , representing around 2 % of the dust loading during the higher dust incidence. Regarding the MEE derived for each aerosol component, their values are in agreement with others referenced in the literature for the specific aerosol types examined in this work: 0.5 ± 0.1 and 1.7 ± 0.2 m 2 g −1 are found for coarse and fine dust particles, 4.5 ± 1.4 m 2 g −1 is derived for smoke and 2.4 ± 0.5 m 2 g −1 for non-smoke aerosols with Arctic origin, and a MEE of 2.4 ± 0.8 m 2 g −1 is obtained for pollen particles, though it can reach higher or lower values depending on predomiPublished by Copernicus Publications on behalf of the European Geosciences Union. 4776 C. Córdoba...

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“…The measurements started on 9 July at 06:00 UT and continued without interruption for 72 h whenever the atmospheric conditions allowed lidar measurements. The details of this quite intensive observation period are provided in Sicard et al (2015). The main aim of the 72 h operationally exercise was to provide a large set of aerosol parameters obtained in a standardized way for a large number of stations in near-real time.…”

Section: Example Of Near-real-time Applicabilitymentioning

confidence: 99%

“…Currently, it has been used by 20 different EARLINET stations which have submitted about 2600 raw data files covering a very large time period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Moreover, more than 5000 SCC optical products (about 3600 aerosol backscatter profiles and 1400 aerosol extinction profiles) have been calculated and used for different purposes like analysis of instrument intercomparisons (Wandinger et al, 2015), air-quality model assimilation experiment (Wang et al, 2014;Sicard et al, 2015), and ongoing long-term comparisons with manually retrieved products (Voudouri et al, 2015). The large usage and the long-term plan for the centralized processing system make the SCC the standard tool for the automatic analysis of EARLINET lidar data.…”

Section: Scc Descriptionmentioning

confidence: 99%

EARLINET Single Calculus Chain – overview on methodology and strategy

et al. 2015

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Abstract. In this paper we describe the EARLINET Single Calculus Chain (SCC), a tool for the automatic analysis of lidar measurements. The development of this tool started in the framework of EARLINET-ASOS (European Aerosol Research Lidar Network -Advanced Sustainable Observation System); it was extended within ACTRIS (Aerosol, Clouds and Trace gases Research InfraStructure Network), and it is continuing within ACTRIS-2. The main idea was to develop a data processing chain that allows all EARLINET stations to retrieve, in a fully automatic way, the aerosol backscatter and extinction profiles starting from the raw lidar data of the lidar systems they operate. The calculus subsystem of the SCC is composed of two modules: a pre-processor module which handles the raw lidar data and corrects them for instrumental effects and an optical processing module for the retrieval of aerosol optical products from the pre-processed data. All input parameters needed to perform the lidar analysis are stored in a database to keep track of all changes which may occur for any EARLINET lidar system over the time. The two calculus modules are coordinated and synchronized by an additional module (daemon) which makes the whole analysis process fully automatic. The end user can interact with the SCC via a user-friendly web interface. All SCC modules are developed using open-source and freely available software packages. The final products retrieved by the SCC fulfill all requirements of the EARLINET quality assurance programs on both instrumental and algorithm levels. Moreover, the manpower needed to provide aerosol optical products is greatly reduced and thus the near-real-time availability of lidar data is improved. The high-quality of the SCC products is proven by the good agreement between the SCC analysis, and the corresponding independent manual retrievals. Finally, the ability of the SCC to provide high-quality aerosol optical products is demonstrated for an EARLINET intense observation period.

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EARLINET: potential operationality of a research network

Cited by 41 publications

References 74 publications

EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products

EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products

Separation of the optical and mass features of particle components in different aerosol mixtures by using POLIPHON retrievals in synergy with continuous polarized Micro-Pulse Lidar (P-MPL) measurements

EARLINET Single Calculus Chain – overview on methodology and strategy

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