EARLINET instrument intercomparison campaigns: overview on strategy and results

Abstract

This paper introduces the recent European Aerosol Research Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and five single-site intercomparison activities, 21 EARLINET systems from 18 EARLINET stations were intercompared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualified which served as traveling standards thereafter. EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607-nm. It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained. Mean signal deviations in predefined height ranges are typically below ±2-%. Particle backscatter and extinction coefficients agree within ±2- × -10^-4-km^-1-sr^-1 and ±-0.01-km^-1, respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deficiencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced confidence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.

Atmos. Meas. Tech., 9, 10011023, 2016 www.atmos-meas-tech.net/9/1001/2016/ doi:10.5194/amt-9-1001-2016 Author(s) 2016. CC Attribution 3.0 License.

Ulla Wandinger1, Volker Freudenthaler2, Holger Baars1, Aldo Amodeo3, Ronny Engelmann1, Ina Mattis1,a,Silke Gro2,b, Gelsomina Pappalardo3, Aldo Giunta3, Giuseppe DAmico3, Anatoli Chaikovsky4, Fiodor Osipenko4, Alexander Slesar4, Doina Nicolae5, Livio Belegante5, Camelia Talianu5, Ilya Serikov6, Holger Linn6,Friedhelm Jansen6, Arnoud Apituley7, Keith M. Wilson7, Martin de Graaf7,c, Thomas Trickl8, Helmut Giehl8, Mariana Adam9,d, Adolfo Comern10, Constantino Muoz-Porcar10, Francesc Rocadenbosch10, Michal Sicard10, Sergio Toms10,e, Diego Lange10,f, Dhiraj Kumar10,g, Manuel Pujadas11, Francisco Molero11, Alfonso J. Fernndez11, Lucas Alados-Arboledas12,13, Juan Antonio Bravo-Aranda12,13, Francisco Navas-Guzmn12,13,h,Juan Luis Guerrero-Rascado12,13,14, Mara Jos Granados-Muoz12,13,i, Jana Preiler14,j, Frank Wagner14,21, Michael Gausa15, Ivan Grigorov16, Dimitar Stoyanov16, Marco Iarlori17, Vincenco Rizi17, Nicola Spinelli18,19, Antonella Boselli19,3, Xuan Wang19,20, Teresa Lo Feudo18,19,k, Maria Rita Perrone21, Ferdinando De Tomasi21, and Pasquale Burlizzi21

1Leibniz Institute for Tropospheric Research, Permoserstr. 15, 04318 Leipzig, Germany

2Ludwig-Maximilians-Universitt, Meteorological Institute, Munich, Germany

3Consiglio Nazionale delle Ricerche, Istituto di Metodologie per lAnalisi Ambientale, Potenza, Italy

4B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus

5National Institute of Research and Development for Optoelectronics, Magurele, Ilfov, Romania

6Max Planck Institute for Meteorology, Hamburg, Germany

7Royal Netherlands Meteorological Institute, De Bilt, the Netherlands

8Institut fr Meteorologie und KlimaforschungAtmosphrische Umweltforschung, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

9European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy

10Universitat Politcnica de Catalunya, Barcelona, Spain

11Centro de Investigaciones Energticas, Medioambientales y Tecnolgicas, Department of Environment, Madrid, Spain

12Department of Applied Physics, Faculty of Sciences, University of Granada, Granada, Spain

13Andalusian Institute for Earth System Research, Granada, Spain

14Universidade de vora, Centro de Geofsica de vora, vora, Portugal

15Alomar, Andya Rocket Range, Andya, Norway

16Institute of Electronics, Bulgarian Academy of Sciences, Soa, Bulgaria

17Universit degli Studi dellAquila, CETEMPS/DSFC, LAquila, Italy

18Dipartimento di Fisica Universit Federico II di Napoli, Naples, Italy

19Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia, Naples, Italy

20Consiglio Nazionale delle Ricerche, Istituto superconduttori, materiali innovativi e dispositivi, Naples, Italy

21Universit del Salento, Dipartimento di Matematica e Fisica, Lecce, Italy

anow at: Meteorological Observatory Hohenpeienberg, Deutscher Wetterdienst, Hohenpeienberg, Germany

bnow at: Deutsches Zentrum fr Luft- und Raumfahrt, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany

cnow at: Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands

Published by Copernicus Publications on behalf of the European Geosciences Union.

EARLINET instrument intercomparison campaigns: overview on strategy and results

1002 U. Wandinger et al.: EARLINET intercomparison campaigns

dnow at: Met Ofce, Exeter, UK

enow at: Institut dEstudis Espacials de Catalunya, Barcelona, Spain

fnow at: Universidad Catlica Boliviana San Pablo, Cochabamba, Bolivia

gnow at: Universidad Carlos III de Madrid, Madrid, Spain

hnow at: Institute of Applied Physics, University of Bern, Bern, Switzerland

inow at: Table Mountain Facility, Jet Propulsion Laboratory, Wrightwood, USA

jnow at: Centre for Climate and Air Pollution Studies, School of Physics, National University of Ireland, Galway, Ireland

know at: Consiglio Nazionale delle Ricerche, Istituto di Scienze dellAtmosfera e del Clima, UOS of Lamezia Terme, Italy

Correspondence to: Ulla Wandinger ([email protected])

Received: 15 September 2015 Published in Atmos. Meas. Tech. Discuss.: 9 October 2015 Revised: 12 February 2016 Accepted: 15 February 2016 Published: 10 March 2016

Abstract. This paper introduces the recent European Aerosol Research Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and ve single-site intercomparison activities, 21 EARLINET systems from 18 EARLINET stations were inter-compared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualied which served as traveling standards thereafter.EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607 nm.It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained.Mean signal deviations in predened height ranges are typically below 2 %. Particle backscatter and extinction coef

cients agree within 2 104 km1 sr1 and 0.01 km1,

respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deciencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced condence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.

1 Introduction

The European Aerosol Research Lidar Network (EARLINET) was founded in the year 2000 with the major goal to establish an aerosol climatology for Europe (Pappalardo et al., 2014). The network has been continuously growing

and currently consists of 27 stations with about 35 individual lidar systems distributed over 16 European countries. Although all systems are specically designed for aerosol observations in the troposphere and, partly, the stratosphere, the network comprises a large variety of individual technical solutions from small laboratory-based systems to mediumsized portable lidars and large container-based instruments.Moreover, technical improvements, resulting to a large extent from exchange of expertise within the network, lead to continuous alterations of the setups. Because of this diversity, the need for a rigorous quality-assurance (QA) program was very clear right from the start of the EARLINET initiative. Consequently, great effort was put into QA activities at the instrument and algorithm levels over the years.

In the rst phase of EARLINET from 2000 to 2003, when EARLINET was implemented as a research project supported by the European Commission under the Fifth Framework Programme, QA activities were focussed on intercom-parisons of lidar systems (Matthias et al., 2004) and of data-evaluation algorithms (Bckmann et al., 2004; Pappalardo et al., 2004). In order to check the quality of the instruments within the network, an intercomparison strategy was developed based on the application of reference lidar systems that can serve as traveling standards (Matthias et al., 2004). All 19 EARLINET systems, which were part of the network at that time, had been intercompared, some in dedicated campaigns, but most of them pairwise by comparison with the mobile reference systems from the EARLINET stations in Hamburg and Munich. Comparisons were exclusively performed for the products provided to the EARLINET database, i.e., proles of particle backscatter and extinction coefcients (Matthias et al., 2004). As a general result, it was found that typical mean deviations of particle backscatter coefcients were 10 % in the planetary boundary layer (PBL) and 1 104 km1 sr1 in the free troposphere and thus well be

low the thresholds of 25 % and 5 104 km1 sr1, respec

tively, representing the predened quality criteria. Only few

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U. Wandinger et al.: EARLINET intercomparison campaigns 1003

comparisons were made for particle extinction coefcients, but mean deviations were also small in these cases with values of less than 5 % or 0.01 km1.

During EARLINETASOS (Advanced Sustainable Observation System), an Integrated Activity within the Sixth Framework Programme from 2006 to 2011, QA activities were intensied and included also the development of tools for internal tests of accuracy and temporal stability of individual lidar systems at any time, i.e., independent of dedicated intercomparisons with reference instruments (Freudenthaler et al., 2016). The QA activities have been continued in the framework of ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure; funded as Integrated Infrastructure Initiative in the Seventh Framework Programme and as Integrating Activity in Horizon 2020), part of which EARLINET has been since April 2011.

In this paper, we report on instrument intercomparison campaigns performed within EARLINETASOS and ACTRIS from 2009 to 2013. Focus of the activities was on the development and test of new reference systems, the integration of new EARLINET stations, and the test of new or considerably enhanced instruments at initial EARLINET stations. It should be noted that in the period from 2000 to 2003 the major goal of EARLINET was to provide independent measurements of particle extinction and backscatter coefcients by applying the Raman lidar method at least at one wavelength, preferably in the UV. Since then, a large number of EARLINET instruments have been upgraded to so-called 3 + 2 Raman lidar systems. The term 3 + 2 stands

for the independent measurement of three backscatter coefcients (at 355, 532, and 1064 nm) and two extinction coefcients (at 355 and 532 nm) by the use of an Nd:YAG laser with frequency doubling and tripling and the detection of elastic-backscatter signals at the three laser wavelengths and of vibration-rotation or pure rotational Raman signals of a reference gas (nitrogen and/or oxygen) at the two shorter wavelengths. With this measurement capability it is possible to retrieve not only optical but also microphysical particle properties (e.g., Mller et al., 1999; Veselovskii et al., 2002; Bckmann et al., 2005; Mller et al., 2015). In the rst EARLINET period, 11 out of the 19 EARLINET stations delivered extinction and backscatter coefcients in the UV, but only two of them were 3 + 2 systems (Matthias et al.,

2004). Currently (in 2015), there are 22 3 + 2 systems at

18 EARLINET stations, and their number is steadily growing. Many systems have polarization measurement capabilities in addition, i.e., the particle linear depolarization ratio is measured at least at one wavelength (Freudenthaler et al., 2009; Belegante et al., 2016; Bravo-Aranda et al., 2016). This quantity contains information about the presence of large, nonspherical particles and is an indispensable parameter for aerosol typing, in particular for the identication of mineral dust in the atmosphere.

The increased number and complexity of lidar systems within the network requires also an improved QA strategy.

The major challenge of the QA efforts lies in the fact that absolute calibration techniques for aerosol lidar systems do not exist and that it is practically impossible to validate aerosol lidar products by comparison with independent measurements externally, e.g., from balloon-borne in situ observations as it is done in the case of water-vapor or ozone lidars (e.g., Leblanc et al., 2011; Nair et al., 2012). Thus, the direct intercomparison of collocated instruments is the only objective and commonly accepted way to assess the overall performance of individual aerosol lidars. The general goal of such an intercomparison is to identify principal deciencies, which may lead to systematic errors of the aerosol lidar products or unreliable results in specic parts of the prole.For instance, in the near range, lidar systems may suffer from electronic saturation effects, uncertain optical overlap functions, and nonlinear signal distortions. In the far range, the limited dynamic range of data acquisition, together with electronic signal perturbance, may hinder appropriate background substraction and Rayleigh calibration. Also, principal optical misalignments or even system design errors may be discovered. Therefore, a two-step intercomparison strategy is now applied for EARLINET, starting with a comparison at signal level to detect the validity range and the uncertainties of each individual signal part, followed by the comparison of aerosol products derived from (partly combined) lidar proles.

In order to cover the larger number of network stations and to become more exible with the intercomparison strategy, it was decided within EARLINETASOS to dene several mobile systems as reference lidars. Two 3 + 2 systems with

polarization capability have been newly developed for this purpose by the EARLINET groups in Hamburg and Potenza.It was envisaged to perform, in a rst step, a specic inter-comparison campaign for the two new and three previously existing mobile reference systems (from Munich, Maisach, and Minsk), and to travel with these systems to other EARLINET stations for single-site intercomparisons afterwards.Fortunate circumstances made it possible that not only the reference lidars but eleven EARLINET systems from nine stations participated already in the rst campaign, the EARLINET Lidar Intercomparison 2009 (EARLI09) in Leipzig, Germany, in May 2009. Four more systems could be validated by comparison with one of the reference systems in a second campaign, the Spanish Lidar Intercomparison 2010 (SPALI10), which took place at Madrid, Spain, in October and November 2010. Finally, single-site intercomparisons were realized at ve EARLINET stations with six lidar systems between 2009 and 2013. The strategies developed and applied in these campaigns and their results are discussed in the following. In Sect. 2 an overview of the campaigns and a description of the involved systems is given. The measurement and data-processing strategies are outlined in Sect. 3. Results are discussed based on the comparisons at signal and at product levels in Sect. 4. Further discussion of the ndings is provided in Sect. 5. Finally, Sect. 6 summa-

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1004 U. Wandinger et al.: EARLINET intercomparison campaigns

rizes the conclusions and gives an outlook on future activities.

2 Instrument intercomparison campaigns

2.1 Overview

Figure 1 gives an overview on the stations involved in the EARLINET intercomparison campaigns between 2009 and 2013. Mobile lidars from the EARLINET stations in Hamburg, Potenza, Munich, Maisach, Bucharest, Cabauw, Minsk, Ispra, and Garmisch-Partenkirchen were moved to Leipzig and intercompared during EARLI09 in May 2009, together with a stationary and a mobile system of the Leipzig site. Afterwards, the reference lidar from Hamburg was brought to the EARLINET station at Andenes, Norway, for a single-site intercomparison in October/November 2009. The Munich system traveled to Soa to intercompare two lidars at this site in October 2010. In October/November 2010, the reference lidar from Potenza participated in the SPALI10 campaign in Madrid, where the intercomparison of the systems from the stations in vora, Barcelona, Granada, and Madrid took place. The LAquila lidar was intercompared with the Munich reference system in September 2012. Finally, in October 2013, the Potenza reference lidar was moved to Naples and Lecce for single-site intercomparisons.

In the following, we introduce the individual campaigns and the participating instruments in more detail. Table 1 lists the involved lidar systems by name and institution. Their measurement channels are detailed in Table 2. The lidar ID used here includes the ID of the EARLINET station to which the instrument belongs (two letters) and a number in order to distinguish systems from stations with more than one instrument.

2.2 EARLI09EARLINET Lidar Intercomparison 2009

EARLI09 took place at the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany, between 4 and 31 May 2009. As mentioned, this campaign was planned to compare the reference lidar systems, but several other partners took the opportunity to join, and nally 11 EARLINET lidar systems were collocated. In this way, the campaign became the largest lidar intercomparison performed so far, with challenging logistical requirements.Three container-based systems, one van, two trailers, and three stand-alone systems were placed next to the stationary Leipzig EARLINET lidar, around and on top of the institutes building, and supplied with more than 120 kW of electrical power and internet connection. The campaign also served for the implementation and test of the new EARLINET intercomparison strategy (see Sect. 3 for details). Thus, the rst week of the campaign was scheduled for preparations of hardware and software. Between 11 and 28 May, 20 measure-

Figure 1. Map of EARLINET and stations involved in the inter-comparison campaigns (station IDs: an Andenes, at Athens, ba Barcelona, be Belsk, bu Bucharest, ca Cabauw, cl Clermont-Ferrand, co Cork, ev vora, gp Garmisch-Partenkirchen, gr Granada, hh Hamburg, is Ispra, ku Kuopio, la LAquila, lc Lecce, le Leipzig, lm Limassol, ma Madrid, ms/mu Maisach/Munich, mi Minsk, na Naples, pa Payerne, pl Palaiseau, po Potenza, sf Soa, th Thessaloniki). Red colors show stations operating reference systems. Participation of instruments from stations in EARLI09 (yellow), SPALI10 (green), and single-site intercomparisons (blue) is indicated. Black dots represent stations which were not involved in the 20092013 intercom-parisons.

ment sessions of 13 h duration on 11 days were performed.

Radiosondes were launched for each session. Daily briengs, including an experts weather forecast, served for the planning of the sessions and for the discussion of results from the previous day. The latter were obtained using a new data-evaluation concept (see Sect. 3) that allowed us to handle, on a daily basis, the signals from more than 100 lidar channels provided by the 11 lidar systems which are described in the following (see also Tables 1 and 2).

The Atmospheric Raman Lidar (ARL2-mobile, ID: hh01) of the Max Planck Institute for Meteorology (MPI-MET) in Hamburg is a multiwavelength Raman lidar. With 26 measurement channels (see Table 2) it is the most extensive EARLINET lidar. The emitter is a 440 mJ Nd:YAG laser (Quantel, Brilliant B). The system has two unique features. Firstly, it covers the altitude range from about 50 m above ground up to the stratosphere by applying three separate receivers, which are ber-coupled to two Newtonian telescopes with diameters of 380 (far range) and 150 mm (near range) and a lens telescope with a diameter of 22 mm (lowest heights), re-

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U. Wandinger et al.: EARLINET intercomparison campaigns 1005

Table 1. EARLINET systems participating in the intercomparison campaigns

Lidar ID Lidar name and institution

EARLI09hh01 ARL2-mobile, MPI-MET, Hamburg, Germanyms01 MULIS, LMU Munich, Germanymu01 POLIS, LMU Munich, Germanypo01 MUSA, CNR-IMAA, Potenza, Italymi01 LMR-mobile, BISIP, Minsk, Belarusle01 MARTHA, TROPOS, Leipzig, Germanyle02 PollyXT, TROPOS, Leipzig, Germanyis01 CAML, JRC, Ispra, Italybu01 RALI, INOE 2000, Bucharest, Romaniagp01 HSRL, IMK-IFU, Garmisch-Partenkirchen, Germany ca01 CAELI, KNMI, De Bilt, the Netherlands

SPALI10 (reference system: po01)gr01 Raymetrics LR331D400, CEAMA, Universidad de Granada, Spain ev01 PAOLI, Universidade de vora, Portugalma01 LIDAR-CIEMAT, CIEMAT, Madrid, Spainba02 UPC-MRL, UPC, Barcelona, Spain

ALI09 (reference system: hh01)an01 Tropospheric Lidar, Alomar, Andya Rocket Range, Norway

SOLI10 (reference system: mu01, upgraded)sf01 Lidar with CuBr laser, IE-BAS, Soa, Bulgaria sf02 Lidar with Nd:YAG laser, IE-BAS, Soa, Bulgaria

LALI12 (reference system: mu01, upgraded)la01 UV lidar, Universit degli Studi dellAquila, Italy

NALI13 (reference system: po01)na01 MALIA, CNISM, Naples, Italy

LELI13 (reference system: po01)lc01 UNILE lidar, Universit del Salento, Lecce, Italy

spectively. Depolarization measurements at 532 nm are utilized with two detection channels, which are directly coupled to another 200 mm Newtonian telescope. The second remarkable feature of the system is its capability to detect rotational Raman signals at both 355 and 532 nm with a specic grating technique. In addition, the vibration-rotation signals at 387 nm (nitrogen) and 407 nm (water vapor) are measured. Rotational Raman signals serve for temperature measurements, but can also be used for extinction-coefcient retrievals. Signals are detected by Hamamatsu photomultiplier tubes (PMTs) in photon-counting detection mode in the UV and visible wavelength ranges and by Licel/EG&G avalanche photodiodes (APDs) in analog detection mode at 1064 nm.

The Meteorological Institute of the Ludwig-Maximilians-Universitt (LMU) in Munich participated with two instruments, which both had already served as reference systems in EARLINET. POLIS (Portable Lidar System, ID: mu01) is a small, rugged lidar system with an exchangeable detector unit. It applies a 50 mJ laser (Big Sky, Ultra GRM) and a 200 mm Dall-Kirkham Cassegrain telescope. During EARLI09 the instrument was operated as a two-channel 355 nm system, which detected either parallel- and cross-polarized elastic backscatter signals or the total elastic backscattering together with the 387 nm nitrogen Raman signal with Licel/Hamamatsu PMTs for combined analog and photon-counting detection (Freudenthaler et al., 2009). POLIS was upgraded to three channels in 2010 (see below) and

to six channels in 2013 (Freudenthaler et al., 2015). The second system, MULIS (Multichannel Lidar System, ID: ms01), is a 3 + 2 Raman lidar with polarization measurement capa

bility at 532 nm (Freudenthaler et al., 2009). This lidar performs the EARLINET observations at the station of Maisach, near Munich. The instrument applies a 1.6 J Nd:YAG laser (Continuum, Surelite II) and a 300 mm Cassegrain telescope.Hamamatsu PMTs (UV and visible wavelength range) and a Licel/EG&G APD (at 1064 nm) are used as detectors. All elastic-backscatter signals are measured in analog detection mode. For Raman signals, the combined analog and photon-counting technique (Licel) is applied. MULIS was developed as a reference lidar for the rst phase of EARLINET (20002003) and served as a prototype for MUSA (Multi-wavelength System for Aerosol, ID: po01), the reference system of CNRIMAA (Consiglio Nazionale delle RicercheIstituto di Metodologie per lAnalisi Ambientale) in Potenza.Thus, MUSA has very similar specications as MULIS, with the exception that it applies the Licel combined analog and photon-counting detectors also for the elastic-backscatter signals at 355 and 532 nm (Madonna et al., 2011).

The fth EARLINET reference system is the LMR-mobile (Lidar Multiwavelength Raman, ID: mi01) of theB. I. Stepanov Institute of Physics (BISIP), Minsk, Belarus.

It is a compact, scanning, stand-alone system, which applies a 250 mJ Nd:YAG laser (SOLAR TII, LF-114) and a 300 mm Cassegrain telescope. The system has six measurement channels in 3 + 2 conguration with polarization discrimination

at 532 nm. Analog detection with PMTs (355, 532 nm) and an APD (1064 nm) is applied for elastic-backscatter signals.Photon-counting PMTs are used for Raman signals (387, 607 nm).

MARTHA (Multiwavelength Atmospheric Raman Lidar for Temperature, Humidity, and Aerosol Proling, ID: le01) of TROPOS is the stationary EARLINET lidar at Leipzig, Germany. It works with a 1.6 J Nd:YAG laser (Spectra Physics, Quanta-Ray PRO 290) and an 800 mm Cassegrain telescope. It allows 3 + 2 Raman lidar observations and de

polarization measurements at 532 nm. In addition, the instrument has channels for rotational Raman observations at 355 (since 2011, not during EARLI09) and 532 nm, water-vapor measurements, and dual-eld-of-view observations (Mattis et al., 2004; Schmidt et al., 2013). PMTs in photon-counting mode are employed in all channels, including at 1064 nm. For routine, automatic observations, the PollyXT lidar (Portable Aerosol Raman Lidar System, extended version, ID: le02) is applied in Leipzig as well. It has a 3 + 2 Raman lidar setup,

utilizing a 450 mJ Nd:YAG laser (Continuum, Inlite III) and a 300 mm Newton telescope (Althausen et al., 2009). Hamamatsu photon-counting-only PMTs are deployed in all channels. Total and cross-polarized backscattered radiation was detected at 355 nm during EARLI09 (at 532 nm since the end of 2011).

CAML (Cloud and Aerosol Micro Lidar, ID: is01) of the Joint Research Centre (JRC), Ispra, Italy, is a commercial

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1006 U. Wandinger et al.: EARLINET intercomparison campaigns

Table 2. Overview of measurement channels of EARLINET systems participating in the intercomparison campaigns; lidar IDs are as in Table 1. Numbers indicate detection wavelengths; t total signal, c cross-polarized signal, p parallel-polarized signal, RR rotational Raman signal, RY HSRL Rayleigh signal, far far-range receiver, near near-range receiver, low low-range receiver, pol receiver for polarization measurements, a analog detection, p photon-counting detection, a+p combined acquisition channels (Licel).

Lidar Rec. 355t 355c 355p 355RR 387 407 532t 532c 532p 532RY 532RR 607 1064

hh01 far p 2p p p p 2p a near p 2p p p p 2p a low p 2p p 2ppol p p

ms01 a a+p a a a+p a mu01 (a+p)a (a+p)a (a+p)a (a+p)apo01 a+p a+p a+p a+p a+p a mi01 a p a a p a le01 p p p p p 2p p p le02 p p p a, p p p is01 pbu01 a+p a+p p a+p a+p a+p a gp01 a a a a ca01 far a+p a+p p a+p a+p a near a+p a+p p a+p a+p a pol a+p a+p

gr01 a+p p p a+p a+p p a ev01 p p p p p p ma01 a a+p a a+p a ba02 a+p a+p a+p a+p a+p a

an01 a+p a+p a+p a+p a+p a

sf01 pbsf02 a a

la01 pc pc pc

na01 a, p p, p p a, p a, p p

lc01 a+p a+p a+p p a+p a+p a

a alternative congurations; see text for details

b CuBr laser, emission wavelength at 510 nm

c XeF excimer laser, emission wavelength at 351 nm, Raman-shifted wavelengths at 382 and 403 nm

Technology) participated in EARLI09 with a newly developed 532 nm High Spectral Resolution Lidar (HSRL, ID: gp01). The 3+1 lidar (elastic-backscatter signals at 355, 532, 1064 nm and Rayleigh signal at 532 nm) applies a 0.5 J Nd:YAG laser (Quanta Ray, LAB-150-30) and a 300 mm Cassegrain telescope. The Rayleigh signal in the second 532 nm channel is separated with an iodine lter. Analog signal detection with actively stabilized Hamamatsu 7400-03 PMTs and a pin photodiode at 1064 nm (both from Romanski Sensors) is utilized.

CAELI, the CESAR (Cabauw Experimental Site for Atmospheric Research) Water Vapor, Aerosol, and Cloud Lidar (ID: ca01), was developed by the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, and is now operated by the Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands (Apituley et al., 2009). CAELI works with a 1.6 J Nd:YAG laser

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micropulse lidar supplied by Cimel Electronique. The automatic stand-alone system uses an 8 J, 4.7 kHz Nd:YAG laser and a 200 mm telescope, and it measures 532 nm elastic-backscatter light with a photon-counting APD (Barnaba et al., 2010).

RALI (Raman Aerosol Lidar, ID: bu01) of the National Institute of Research and Development of Optoelectronics, INOE 2000, Bucharest, Romania, is a commercial 3 + 2 Ra

man lidar from Raymetrics (LR331D400), including polarization discrimination at 532 nm and a water-vapor detection channel at 407 nm. It applies a 330 mJ laser (Big Sky, CFR400-10) and a 400 mm Cassegrain telescope. The detection channels are based on Licel/Hamamatsu PMTs for the UV and visible channels and on a Licel/EG&G APD at 1064 nm (Nemuc et al., 2013; Belegante et al., 2014).

IMK-IFU (Institut fr Meteorologie und Klimaforschung Atmosphrische Umweltforschung, Karlsruhe Institute of

2.4 Single-site intercomparison campaigns

Further intercomparisons were performed by moving one of the reference systems to specic EARLINET sites. Actually, this is the basic strategy applied for EARLINET intercom-parisons at instrument level. It is planned to continue these kinds of intercomparisons over the years in order to validate each EARLINET system with a reference system from time to time. Nevertheless, respective efforts are large and require appropriate funding. Five activities were carried out between 2009 and 2013.

From 22 October to 5 November 2009 the reference system ARL2-mobile of MPI Hamburg (ID: hh01) was stationed at Andya, Norway, in order to compare the Alomar Tropospheric Lidar (ID: al01). During ALI09 (Alomar Lidar Intercomparison 2009) simultaneous measurements were performed on nine days. On two days (4 and 5 November) radiosondes were launched to support the signal calibration.The Alomar Tropospheric Lidar is a 3 + 2 multiwavelength

Raman lidar with a 1 J laser (Spectra Physics, GCR-6-30) and a 175 mm Newtonian telescope. The data acquisition is based on the Licel/Hamamatsu PMT and Licel/EG&G APD concepts. The optical receiver of the Alomar Tropospheric Lidar had been changed considerably in the time before the intercomparison, and the campaign was also used to x remaining technical issues.

The lidar intercomparison SOLI10 (Soa Lidar Intercom-parison 2010) took place at the Institute of Electronics, Bulgarian Academy of Sciences (IE-BAS), in Soa, Bulgaria, between 9 and 14 October 2010. POLIS from LMU Munich (ID: mu01) served as the reference system. POLIS had been upgraded to three channels in summer 2010, including 355 nm with polarization discrimination and either 532 nm total or 387 nm, and had been intercompared with the reference lidar system MULIS (ID: ms01) in Maisach again. POLIS was transported to Soa to intercompare both lidar systems of IE-BAS, one working with a 0.1 mJ CuBr vapor laser at 510 nm, and the other with a 1 J Nd:YAG laser (EKSMA) at 532 and 1064 nm (Stoyanov et al., 2011). Both systems are elastic-backscatter lidars. The CuBr system (ID: sf01) uses a 150 mm Cassegrain telescope and a photon-counting PMT as the detector. The Nd:YAG system (ID: sf02) applies a 350 mm Cassegrain telescope and analog detection. The latter system points out of a lab window under 58 zenith angle. Thus, the intercomparisons were made separately for the two systems, using the respective scan angle for the POLIS measurements.

The LAquila Lidar Intercomparison 2012, LALI12, was performed at the EARLINET site of the Dipartimento di Fisica, Universit degli Studi dellAquila, in LAquila, Italy, between 10 and 15 September 2012. One daytime and three nighttime sessions covering one 60 min and six 30 min inter-comparison periods were carried out. Also here, POLIS (ID: mu01) served as the reference system. The lidar at LAquila (ID: la01) is a UV aerosol and water-vapor lidar, which ap-

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U. Wandinger et al.: EARLINET intercomparison campaigns 1007

(Continuum, PowerLite Precision II 9030 SI) and has two 3 + 2 setups with a water-vapor Raman channel, one coupled

to a 150 mm Newton telescope for near-range measurements and one to a 570 mm Newton telescope for far-range observations. In addition, a 50 mm lens telescope is used to measure parallel- and cross-polarized 532 nm signals. Licel data acquisition technique with Hamamatsu PMTs for the UV and visible wavelength range and EG&G APDs for 1064 nm is applied in all channels.

2.3 SPALI10 Spanish Lidar Intercomparison 2010

The second dedicated intercomparison campaign brought together the EARLINET systems of the Iberian peninsula from the stations in vora, Barcelona, Madrid, and Granada. Comparisons were made against the reference system MUSA from CNRIMAA in Potenza (ID: po01), which was successfully tested in EARLI09 before. The campaign called SPALI10 took place at the Centro de Investigaciones Energticas, Medioambientales y Tecnolgicas (CIEMAT), Department of Environment, Atmospheric Pollution Division, in Madrid, Spain, between 18 October and 5 November 2010.The campaign strategy followed the rules established in EARLI09. The rst week of the campaign was used for instrument setup and tests of the automated preprocessing of data (see Sect. 3). During the following 2 weeks, measurement sessions were regularly scheduled during day and night.All in all, 29 sessions of 13 h duration were performed.Radiosondes were launched systematically during the whole eld campaign for each measurement session.

All systems of the SPALI10 campaign are multiwave-length Raman lidars (see Tables 1 and 2). The Granada group operates a Raymetrics LR331D400 system (ID: gr01) with specication as described for the Bucharest system above (Guerrero-Rascado et al., 2008, 2009). PAOLI (Portable Aerosol and Cloud Lidar, ID: ev01) from vora is a 3 + 2

system of PollyXT type (Althausen et al., 2009) with a 450 mJ Nd:YAG laser (Continuum, Inlite III) and a 300 mm Newton telescope. Hamamatsu photon-counting-only PMTs are applied in all channels. Cross-polarized backscattered radiation, together with a total signal, is detected at 532 nm. The LIDAR-CIEMAT system (ID: ma01) from Madrid and the UPC MRL (Universitat Politcnica de Catalunya Multispectral elastic-Raman Lidar, ID: ba02) from Barcelona are both 3 + 2 systems without polarization discrimination, but with

a water-vapor channel in the case of UPC MRL. LIDARCIEMAT makes use of a 1 J Spectra Physics laser (LAB-170-30) and a 300 mm Newtonian telescope. UPC MRL applies a 365 mJ laser (Quantel, Brilliant) and a 355 mm SchmidtCassegrain telescope (Kumar et al., 2011). The detection channels of both systems are based on the Licel/Hamamatsu PMT and Licel/EG&G APD acquisition systems.

1008 U. Wandinger et al.: EARLINET intercomparison campaigns

plies a XeF excimer laser (Lambda Physik, EMG 150 MSC), a 200 mm telescope, and PMTs in photon-counting mode (Rizi et al., 2004). The emission wavelength is only slightly different from the third harmonic of a Nd:YAG laser, and thus the wavelength shift of the received elastic-backscatter (351 nm) and nitrogen Raman signals (382 nm) is neglected in the comparisons.

The lidar system MALIA (Multiwavelength Aerosol Lidar Apparatus, ID: na01) of the Consorzio Nazionale Interuniversitario per la Scienze Fisiche della Materia (CNISM) in Naples, Italy, was intercompared with the Potenza reference lidar MUSA (ID: po01) during the Naples Lidar Intercom-parison 2013, NALI13, from 14 to 18 October 2013. Two daytime and three nighttime measurement periods of 30 min to 4 h were covered. MALIA is a 10-channel system based on a 0.5 J Nd:YAG laser (Quantel, Brilliant-B) and a 0.3 m Newtonian telescope. Signals at 355 nm (total) and 532 nm (cross- and parallel-polarized) are detected with both photon-counting and analog channels. The Raman return at 387 nm is split to enter a high-signal and a low-signal photon-counting channel. Further photon-counting channels detect the Raman signals at 407 and 607 nm. Data acquisition is based on 150 MHz photon counters and 12 bit analogdigital converters.

From 21 to 25 October 2013, the Lecce Lidar Intercom-parison LELI13 took place at the Universit del Salento in Lecce, Italy. Again, the MUSA lidar (ID: po01) served as the reference system. Four daytime and ve nighttime sessions were performed. The EARLINET station of Lecce operates a multiwavelength Raman lidar (UNILE lidar, ID: lc01) with a 1.4 J Nd:YAG laser (Quantel, YG981E) and a 0.3 m Newtonian telescope (Perrone et al., 2014). The 3 + 2 system has

polarization discrimination at 355 nm and a water-vapor Raman channel at 407 nm. Licel data acquisition technique with Hamamatsu PMTs for the UV and visible wavelength range and an EG&G APD for 1064 nm is applied in all channels.

3 Measurement and data-processing strategies

The participation of a relatively large number of lidar systems in an intercomparison campaign, like EARLI09 and SPALI10, requires the development and application of coordinated observation and data-evaluation strategies. For instance, it is necessary to have preliminary comparison results at hand as soon as possible after each measurement session in order to detect and remove system faults immediately.Particular attention must be paid in the beginning of a campaign when systems had been moved before. Specic care is also necessary when systems are brand new, as was the case in EARLI09 for the new reference systems. In addition, introducing differences in the comparisons by using different analysis software should be avoided. These considerations led to the development of a special version of the Single Calculus Chain (SCC, DAmico et al., 2015, 2016) before

EARLI09 in order to preprocess the raw lidar data in a common way instantaneously. An additional piece of software, developed at LMU Munich, served for the direct comparison at signal level, i.e., necessary interpolation, smoothing, and weighting as well as visualization of signals and determination of signal deviations. Finally, a modied version of the SCC optical products module (Mattis et al., 2016) was used to calculate particle extinction and backscatter coefcients from the processed signals in order to perform comparisons at product level. The respective concepts are outlined in the following.

In all intercomparison campaigns the lidar systems were collocated on a at terrain within about 100 m distance. The laser beams pointed close to the zenith (except sf02; see above), which made it very likely that all instruments measured the same atmospheric volume within the averaging time. Several sessions were scheduled for every day of the campaigns (weather permitting), one at daytime and one at night if possible. Each session lasted several hours with the goal to nd at least a 30 min period in each session with stable atmospheric conditions and with all lidar systems up and running. In order to be as exible as possible in the selection of nal comparison periods, the raw signals were stored with 1 min resolution. The complete data sets of these raw signals from all systems had to be delivered without any preprocessing to a common database server shortly after each session.

The raw-signal formats had been predened, following standards set for the EARLINET SCC. Each data set includes a header with all information necessary for further processing of the signals. Some basic, xed parameters of each system had been collected in a system database. Using the header and database information, all signals were then preprocessed by the modied version of the SCC. The preprocessor performs trigger-delay shift, dead-time correction, background subtraction, and range correction. If requested, the preprocessor also combines near-range and far-range signals, photon-counting and analog signals (gluing), and parallel- and cross-polarized signals into a total prole using given calibration ranges or values. After this individual signal preprocessing and after selection of an appropriate comparison period, the signals were averaged, typically over 30 to 120 min, in order to improve the signal-to-noise ratio.

Figure 2 illustrates the processing steps at signal level for the example of 387 nm signals measured with nine systems in eleven channels during EARLI09 on 25 May 2009, 21:00 23:00 UTC. The channels are distinguished by color, and the legend provides the system ID (see Table 1) as well as a three-digit channel ID with the following meaning:

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f__ signal from far-range telescopen__ signal from near-range telescopel__ signal from low-range telescopex__ signal from a system with one telescope

_t_ total signal_p_ parallel-polarized signal_c_ cross-polarized signal_s_ sum of parallel- and cross-polarized signals

__a analog signal__p photon-counting signal__g analog and photon-counting glued signal (Licel).

In Fig. 2a, the individual signals are shown after preprocessing with the SCC. Here, the averaged output signals provided by the SCC preprocessor still have the original range resolutions from 3.75 to 60 m. In addition, a range offset may occur because of different lidar location altitudes above ground (e.g., when a lidar is operated in a building or on top of a building and compared against a reference system in a van or container at ground level). Furthermore, pointing angles of the systems are typically between 0 and 5 and require further altitude corrections. In order to allow for a point-by-point comparison, the signals were re-binned to a common height resolution of 60 m and to common height levels considering the individual system altitudes and the lidar zenith angles. The signal noise at higher altitudes was reduced by further stepwise progressive smoothing with up to 960 m resolution. The result is presented in Fig. 2b. In order to compare the signals quantitatively, they were normalized in the height range between 3.5 and 6.5 km, where the deviations are small and the signal-to-noise ratios are high.

Usually, comparisons should be made against a reference system for all individual wavelengths and polarization states.However, in EARLI09, none of the reference systems were considered to be proven already. Therefore, the chosen strategy was to construct a mean signal, or common reference, from the best parts of all available signals. Ideally, this common reference should be close to the unknown true signal.For this purpose, range-dependent weights are assigned to the individual signals by an experts guess reecting an assumed accuracy; see Fig. 2c. A weight of zero means that the respective part of the signal, e.g., the range of incomplete overlap, is omitted. A weight of 1 is assigned to ranges that appear trustworthy. Then, a weighted mean signal is calculated as a rst guess of the common reference. Afterwards, the experts weights are successively decreased by a factor commensurate with the range-dependent signal deviation from the rst-guess mean signal; see Fig. 2d. In this way, highest weights are assigned to the best signal parts, and the nal common reference is calculated. In the stratosphere, where an aerosol-free range can be assumed, the mean signal is replaced by a calculated signal from actual radiosonde

data (pure molecular Rayleigh or Raman signal), tted to the common reference at an appropriate height (usually at about 15 km). The radiosonde data were taken from local radiosonde ascents during the experiment.

The approach of a common reference was applied in EARLI09 only. In all other campaigns, the reference system was considered as the standard to which the other systems were compared. Point-by-point deviations as well as mean deviations in certain height ranges are used to assess the quality of the signals.

If Pref(zi, ) is the reference signal at wavelength (either the common reference or the signal from the reference system), the relative deviation of an individual signal P (zi, )

from this reference signal is calculated for each individual height zi (to which the signals were commonly binned) as

[Delta1]P (zi, ) =

P (zi, ) Pref(zi, )

Pref(zi, ) . (1)

The relative deviations are shown in Fig. 2e for the example case of 25 May 2009.

The mean relative systematic deviation (relative bias) of an individual signal from the reference signal over a height range [Delta1]z = zL zK, i.e., L K + 1 height bins, is dened

as

[Delta1]P ([Delta1]z, ) =

L

Pi=K

[Delta1]P (zi, )

L K + 1

. (2)

The mean relative systematic deviation is used to assess the quality of signals in certain atmospheric height ranges (e.g., boundary layer, free troposphere, stratosphere).

For the comparison at product level, aerosol optical parameters were computed using a special version of the SCC optical products module (Mattis et al., 2016). This version is able to treat the preprocessed, re-binned, and normalized signals, and also the common reference, on the common height grid (with 60 m vertical resolution in EARLI09). Thus, point-by-point comparisons and the calculation of mean deviations are possible for the products in the same way as for the signals. We use the absolute deviation,

[Delta1]c(zi, ) = c(zi, ) cref(zi, ), (3) of a coefcient c (either extinction or backscatter coefcient)

from the reference coefcient cref at individual heights and the mean absolute systematic deviation (absolute bias) in certain height ranges,

[Delta1]c([Delta1]z, ) =

L

Pi=K [Delta1]c(zi, )

L K + 1

, (4)

to investigate the quality of optical products.

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1010 U. Wandinger et al.: EARLINET intercomparison campaigns

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Figure 2. Illustration of signal processing for comparison purposes. The measurement was taken during EARLI09 on 25 May 2009, 21:00 23:00 UTC. (a) Range-corrected signals at 387 nm with individual range resolutions (3.75 to 60 m). (b) Range-corrected lidar signals at 387 nm binned to common height resolution (60 m) and to common height levels, progressively smoothed (60 m up to 3 km, 120 m from 36 km, 240 m from 69 km, 480 m from 912 km, 960 m above), and normalized between 3.5 and 6.5 km. The thick gray line represents the common reference. A pure molecular signal at 387 nm calculated from radiosonde data (rs09052503) is tted to the common reference at10.3 km (shown below 12 km) and, additionally, at 15.3 km (shown above 12 km). (c) Initial weights assigned to the signals for calculation of a weighted mean signal. (d) Final weights assigned to the signals for calculation of the common reference. (e) Relative deviations of individual signals from the common reference. For the sake of conspicuity the weights in panels (c) and (d) are successively shifted by a value of 1 along the y axis.

4 Results

In the following, we present comparison results at signal and product level. We focus on signals at the wavelengths of 355 (total), 387, 532 (total, parallel-, and cross-polarized), and 607 nm and respective aerosol products, i.e., particle extinction and backscatter coefcients at 355 and 532 nm. We do

not discuss observations at 1064 nm, since there is a separate paper on technical solutions, calibration issues, and intercomparison results for the infrared wavelength in this special issue by Engelmann et al. (2016). Furthermore, we do not show results at product level for the particle depolarization ratio. Depolarization ratio measurements require specic calibration procedures, which are discussed in de-

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of view reach a complete overlap at a few hundred meters above ground, channels based on a large-sized telescope and a small eld of view obtain full geometrical overlap between 1 and 3.5 km. The latter channels are usually well suited for observations in the lower stratosphere up to 2030 km height, whereas the near-range channels are typically limited to measurements in the troposphere, as can be seen in the gures from the large uctuations due to low signal-to-noise ratios above the cirrus layer. In order to account for the different observation ranges, for each channel a valid range is dened within which the mean deviations from the reference are calculated. The minimum valid range is the height below which the signal has a systematic relative deviation of > 0.1 from the reference prole, usually due to incomplete overlap. The maximum valid range is the height above which the mean relative deviation from the reference prole is > 0.1 over a height interval of 2 km, usually when the detection limit is reached. This upper boundary is determined by the instrument parameters as well as by the actual atmospheric conditions, in particular the optical depth. In the present case, the attenuation of the signals by the cirrus cloud deck leads to generally lower maximum valid ranges compared to observations under clear conditions.

Tables 3 and 4 show the valid range and the mean relative signal deviation for different height ranges for the EARLI09 case of 25 May 2009 as well as for all other comparison campaigns. The height ranges are dened from the lowest valid range to 2.5 km (R1, typically covering the planetary boundary layer), from 2.5 to 6 km (R2, representing the lower troposphere), from 6 to 12 km (R3, representing the upper troposphere), and from 12 km to the highest valid range (R4, indicating the system performance in the lower stratosphere).If the lower valid range is above 2.5 km and/or the upper valid range is below 12 km, the averaging is applied accordingly to the respective valid ranges, and the excluded ranges (R1 . . . R4) are indicated as not valid (n.v.). As mentioned above, the concept of a common reference was applied only in EARLI09. For all other campaigns the deviations are calculated with respect to the reference system or, for stratospheric heights and when the reference system was at the detection limit, with respect to the Rayleigh prole derived from radiosonde observations.

Regarding EARLI09 Figs. 3 and 4 and Tables 3 and 4 show a good agreement for almost all systems. Within the valid range the mean systematic signal deviations are, with few exceptions, well below 5 % and typically in the range

of 2 %. Best agreement is found in the lower troposphere

(R2). In this range, the mean deviations are mostly below 1 %. Largest deviations are obtained in the lowest and highest ranges, close to the boundaries which dene the valid range, and can thus be attributed to the effects of incomplete overlap or low signal-to-noise ratio. A clear bias due to obvious system misalignment was found for the CAML micropulse lidar from Ispra (is01, see Fig. 4 and Table 4). Since this commercial system is sealed, no technical corrections by the

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U. Wandinger et al.: EARLINET intercomparison campaigns 1011

tail in this special issue by Bravo-Aranda et al. (2016) and Freudenthaler (2016). Rotational Raman lidar signals at 355 and 532 nm and the 532 nm HSRL Rayleigh signal are shown in conjunction with the respective vibration-rotation Raman signals at 387 and 607 nm, respectively, if available. We do not compare signals at 407 nm (water-vapor Raman signals), neither do we show water-vapor and temperature retrievals, since these observations are currently not within the scope of EARLINET.

Quantitative comparisons are presented for selected measurement periods from each campaign. The periods were chosen such that the instruments showed a satisfactory performance; i.e., teething troubles as typical in the beginning of a campaign had already been solved. Mainly nighttime cases were considered, so that a complete evaluation of all measurement channels and all delivered products was possible. Note that the focus of EARLINET is on the provision of aerosol extinction and backscatter proles independently derived with high accuracy from Raman lidar measurements during nighttime, since most of the Raman channels in the network cannot be operated in the presence of strong daylight. It was ensured that the atmospheric conditions had been stable over the measurement period, which is most likely the case during nighttime, and allow for unambiguous comparisons. Therefore, the proles were also checked for the presence of a considerable amount of particles over a large height range as well as clear-air signatures in the far range representing Rayleigh conditions. Generally, cases with optically thick clouds were excluded. Figures illustrating point-by-point comparisons are presented for EARLI09 only, whereas tables provide results of mean systematic deviations in selected height ranges for all intercomparison campaigns. Interested readers may nd additional information on different intercomparison campaigns in the ACTRIS QA reports; see, e.g., Deliverables D2.5 and D2.11 of the ACTRIS FP7 project provided at http://fp7.actris.eu/language/en-GB/Members/Deliverables.aspx

Web End =http://fp7.actris.eu/language/en-GB/ http://fp7.actris.eu/language/en-GB/Members/Deliverables.aspx

Web End =Members/Deliverables.aspx .

4.1 Comparisons at signal level

Figures 3 and 4 show comparison results for the EARLI09 session of 25 May 2009, 21:0023:00 UTC. On that day, Saharan dust layers were present up to about 6.5 km height and provided a good opportunity for detailed comparisons of aerosol products over a large height range. A cirrus cloud layer occurred between 11 and 13.5 km height. The left panels of Figs. 3 and 4 show the signals at 355, 387, and 607 nm and the total, cross-polarized, and parallel-polarized 532 nm signals, respectively. The right panels of both gures present the relative signal deviations from the common reference.The applied methodology follows the explanations in Sect. 3 exactly (see Fig. 2).

The different geometrical overlap functions of the various systems and channels are clearly visible. Whereas near-range channels based on a small telescope and a wide eld

1012 U. Wandinger et al.: EARLINET intercomparison campaigns

Table 3. Valid range and mean systematic deviation of signals at 355, 387, and 607 nm in four height ranges: R1 (lowest valid range2.5 km), R2 (2.56 km), R3 (612 km), and R4 (12 kmhighest valid range). n.v. denotes not valid. Rec. denotes receiver range.

Lidar Rec. Valid Mean systematic deviation, % Valid Mean systematic deviation, % Valid Mean systematic deviation, % range, 355 nm (total) range, 387 nm range, 607 nmkm R1 R2 R3 R4 km R1 R2 R3 R4 km R1 R2 R3 R4

hh01 far 2.814.4 n.v. 1.2 1.7 8.5 2.514.4 n.v. 0.5 3.8 12.0

near 0.714.4 1.9 +0.0 0.5 +2.8 0.714.4 1.7 +0.1 +1.3 7.5

low 1.014.4 +3.0 +0.2 +0.1 +5.8

ms01 0.312.5 0.3 0.9 +4.9 n.v. 0.318.0 +2.5 +0.4 0.8 +2.0 0.318.0 +0.7 +0.2 0.5 +1.1

mu01 0.216.0 +2.3 +0.4 +3.4 0.6 0.216.0 +1.3 +0.3 1.5 3.6

po01 0.330.0 1.7 0.0 0.2 0.4 0.330.0 0.4 +0.0 0.3 4.2 0.312.0 +4.4 +0.5 1.5 n.v.

mi01 0.420.0 +0.8 +0.3 +0.5 +7.2 0.714.0 3.5 +0.2 3.5 4.2 0.515.0 2.8 0.2 0.6 +2.1

le01 1.330.0 +0.3 +0.4 1.1 0.1 1.330.0 3.5 0.2 0.9 1.9 1.530.0 4.9 0.6 +0.3 1.6

le02 0.815.0 +1.2 +1.1 +0.4 +0.4 0.815.0 +1.8 +0.8 1.2 7.4 0.815.0 +2.1 +0.7 1.6 5.0

bu01 0.525.0 1.1 0.4 +0.8 1.5 0.525.0 +0.0 +0.2 +1.4 0.4 0.415.0 +9.1 +1.8 1.3 7.0

gp01 0.67.0 6.1 2.6 +9.2 n.v.

ca01 far 1.930.0 5.7 0.7 +2.1 1.9 1.930.0 5.3 0.6 0.6 0.3 1.330.0 3.5 0.2 +1.2 1.5

near 0.628.0 1.5 0.3 +1.1 2.9 0.828.0 6.4 1.4 +3.2 +1.6 0.312.0 1.4 0.5 +2.9 n.v. gr01 1.230.0 +3.3 0.2 +0.1 +0.3 0.330.0 +2.6 0.8 +0.9 +0.1 1.320.0 7.3 2.2 +0.3 +1.6

ev01 2.830.0 n.v. +3.8 +1.6 0.7 2.130.0 +7.8 +3.5 0.1 1.2 1.030.0 1.4 2.3 +0.2 0.7

ma01 0.712.0 +1.6 +2.2 3.7 n.v. n.v. n.v. n.v. n.v. n.v. n.v. n.v. n.v.

ba02 0.530.0 +7.0 +3.0 +0.5 0.3 0.930.0 2.2 2.1 0.1 0.7 0.830.0 3.7 3.4 0.3 1.9 an01 0.38.0 1.4 0.3 4.0 n.v. 0.57.0 1.7 0.5 +0.2 n.v. n.v. n.v. n.v. n.v.

la01 0.313.0 +0.9 +0.2 1.1 +2.7 0.313.0 +2.0 +0.0 0.2 +2.0 na01 0.718.0 0.4 0.8 1.0 1.4 0.718.0 1.7 0.1 1.6 8.1 0.712.0 3.0 1.5 +8.6 n.v.

lc01 0.913.0 +0.2 +0.3 +0.7 3.6 0.315.0 1.4 0.4 +0.6 2.0 0.312.0 3.4 0.7 +5.2 n.v.

XeF excimer laser; the wavelengths are 351 nm and 382 nm.

Table 4. Valid range and mean systematic deviation of signals at 532 nm (total), 532 nm (cross-polarized), and 532 nm (parallel-polarized) in four height ranges: R1 (lowest valid range2.5 km), R2 (2.56 km), R3 (612 km), and R4 (12 kmhighest valid range). n.v. denotes not valid. NA denotes not available. Rec. denotes receiver range.

Lidar Rec. Valid Mean systematic deviation, % Valid Mean systematic deviation, % Valid Mean systematic deviation, % range, 532 nm (total) range, 532 nm (cross-polarized) range, 532 nm (parallel-polarized) km R1 R2 R3 R4 km R1 R2 R3 R4 km R1 R2 R3 R4

hh01 far 2.012.5 0.6 +2.6 8.5 n.v.

near 1.614.4 +4.7 +0.7 +0.3 +1.1

low 2.014.4 +8.9 +1.9 1.7 +5.6

pol 0.514.4 +16.0 +10.0 38.0 140.0 0.514.4 +10.0 +1.7 6.2 40.0

ms01 0.313.5 +1.5 0.0 0.1 +2.6 0.318.0 +5.2 +6.2 21.0 30.0 0.314.0 +1.1 0.7 0.3 3.5

po01 0.320.0 1.2 0.1 0.2 +5.9 0.320.0 +3.8 +2.7 4.3 10.0 0.320.0 1.6 0.8 +0.4 +3.1

mi01 0.420.0 2.0 0.8 +0.4 1.2 0.420.0 +0.3 +0.7 0.7 +0.3

le01 1.228.0 +2.0 0.3 +2.6 +5.4 1.330.0 11.0 6.5 +31.0 +2.0

le02 0.815.0 +0.5 0.2 0.6 7.3

is01 (1.512.0) +5.1 +3.6 15.0 n.v.

bu01 0.425.0 +4.3 0.3 +0.1 3.6 0.430.0 +9.3 6.1 +34.0 1.9 0.420.0 +4.7 +0.5 0.8 5.6

gp01 2.512.0 n.v. +2.6 0.1 n.v.

ca01 far 1.426.0 3.9 0.3 +0.4 5.2

near 0.225.0 0.3 0.1 +1.6 +0.8

pol 0.525.0 +3.8 1.8 +16.0 7.5 0.325.0 5.8 1.4 +1.7 +1.2 gr01 0.530.0 4.3 1.7 +1.0 0.4 1.030.0 3.4 +3.8 +0.1 0.4 0.530.0 2.6 0.7 0.2 0.3

ev01 1.130.0 1.2 +1.1 +1.5 1.9 NA NA NA NA

ma01 0.325.0 3.3 0.3 +0.3 2.9

ba02 2.030.0 8.3 3.3 +1.6 0.6 an01 0.57.0 +0.9 10.0 +3.7 n.v. 1.012.0 5.7 0.1 8.4 n.v.

sf01 1.310.0 +0.4 5.0 7.7 n.v.

sf02 0.212.0 +0.5 +0.3 +7.5 n.v. na01 1.015.0 0.3 0.2 2.0 3.3 1.013.0 +2.6 2.2 +0.9 +9.1 1.015.0 0.8 0.1 1.9 +2.9 lc01 1.215.0 +3.9 +0.4 +0.2 +5.6

CuBr laser; the wavelength is 510 nm.

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Figure 3. Comparison of range-corrected signals at (a) 355 nm, (c) 387 nm, and (e) 607 nm and their deviations from the common reference (b, d, f). The measurement was performed during EARLI09 on 25 May 2009, 21:0023:00 UTC.

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1014 U. Wandinger et al.: EARLINET intercomparison campaigns

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Figure 4. Same as Fig. 3, but for (a, b) total, (c, d) cross-polarized, and (e, f) parallel-polarized signals at 532 nm.

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surement periods on 4 November 2009, between 09:00 and 16:30 UTC, have been investigated. The optical receiver of the Alomar system had been changed considerably before the campaign. It turned out that the setup was not stable throughout the ALI09 campaign. Readjustments were necessary for each session, and it was not possible to obtain a good performance of all channels at the same time. In addition, signal offsets both in analog and photon-counting channels were observed temporarily, which hint to external electronic disturbances in the laboratory environment. From Tables 3 and 4 it can be seen that reasonable agreement with the reference system could be achieved for the 355, 387, and 532 nm channels up to the mid-troposphere during selected time periods.The 607 nm signal could not be validated. Consequences are discussed in Sect. 5.

The systems at Soa were compared during different sessions of SOLI10. Because of the different pointing angles of the lidars, the reference system (mu01) was operated under a zenith angle of 0 on 10 October 2010, 17:3417:56 UTC, to compare the sf01 system, and under a zenith angle of 60

on 10 October 2010, 20:0620:46 UTC, to compare the sf02 system. Good comparison results were achieved. A minor height shift in the signal of the sf02-system was attributed to a wrong trigger-delay correction. Deviations in the upper troposphere for both systems are due to low signal-to-noise ratios caused by the low system power in the case of sf01 and the large zenith angle in the case of sf02.

The intercomparison period selected for the LALI12 campaign is 15 September 2012, 22:3123:39 UTC. Excellent performance of the LAquila UV lidar (la01) was obtained.Mean systematic deviations from the signals of the reference system (mu01) were 2 % throughout the entire observa

tional range (see Table 3).

For NALI13, the selected measurement period to calculate the numbers presented in Tables 3 and 4 is 1718 October 2013, 23:0300:06 UTC. The MALIA system (na01) compared very well to the reference system (po01), and no signicant deviations in any of the channels were obtained.

The Lecce system (lc01) was compared to the reference system (po01) during LELI13 on 22 October 2013, 19:01 20:01 UTC. In principle, also here the results were satisfactory. Some smaller biases were identied in the near range and attributed to geometrical effects due to focussing of the signals onto the photocathodes. Further discussion of system deciencies found in all campaigns and proposed solutions is provided in Sect. 5.

4.2 Comparisons at product level

Figures 5 and 6 show comparison results for particle extinction and backscatter coefcients at 355 and 532 nm, respectively, for the EARLI09 session of 25 May 2009, 21:0023:00 UTC, derived from the signals presented in Figs. 3 and 4. Particle extinction coefcients were calculated after the Raman method (Ansmann et al., 1990) from

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U. Wandinger et al.: EARLINET intercomparison campaigns 1015

operators were possible, and the lidar could not be validated during the campaign. The reason for the misalignment is a temperature sensitivity of the telescope, which implies defocusing and thus different overlap functions with changing temperature.

Other deviations seen in Figs. 3 and 4 are not considered as major quality decits, since they are usually known and considered in the data evaluation procedures. For instance, the rotational Raman signals (curves with symbols) deviate because they obtain a larger attenuation than the vibration-rotation signals (due to the shorter wavelength of the backscattered light) and have a temperature dependence.The spread of the 532 nm cross-polarized signals in Fig. 4c and d is caused by the different suppression of co-polarized radiation due to different polarizers applied in the systems. In this case, the common reference is probably not closest to the truth. The effects are accounted for in the polarization calibration (see Bravo-Aranda et al., 2016 and Belegante et al., 2016). Regarding the somewhat larger deviations within the cirrus cloud, we have to consider that inhomogeneities may inuence the signals due to the slightly different pointing of the systems. Nevertheless, polarization-dependent transmission effects are also visible as in the case of the PollyXT sys

tem from Leipzig (le02) at 355 nm (see Fig. 3a and b). Such effects need to be quantied and corrected for as explained by Mattis et al. (2009) and Freudenthaler (2016).

The results provided for SPALI10 in Tables 3 and 4 are taken from two observational periods on 25 October 2010, 22:1523:59 UTC (systems ev01, ma01, ba02), and 4 November 2010, 20:0020:30 UTC (gr01), because an alignment problem of the Granada system (gr01) could be solved only late during the campaign. Nevertheless, the more favorable conditions during the longer measurement period on 25 October 2010 were chosen for the comparison of the other systems. In general, the mean systematic deviations are somewhat larger for SPALI10 than for EARLI09. The campaign suffered from bad weather conditions and thus a limited number of suitable comparison periods. Misalignment errors which often occur in the beginning of the campaigns, in particular when systems had been transported before could not be completely solved during SPALI10. In the case of the PAOLI system from vora (ev01; see Tables 3 and4) the reason for the large deviations in the height ranges R1 and R2, which are due to a very large range of incomplete overlap, could be identied only when the system was back at vora. It was found that the eld stop was not exactly positioned on the receiver optical axis, possibly because of damage during transport. In addition, it was not possible to obtain successful intercomparisons for all channels during SPALI10. In particular, the signals of the CIEMAT lidar from Madrid (ma01) showed electronic disturbances, varying from day to day, which prevented the Raman channels at 387 and 607 nm from being veried.

For comparing the Alomar Tropospheric Lidar (an01) with the reference system (hh01) during ALI09, several mea-

1016

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Figure 5.

Comparison of particle extinction coefcients (a) and particle backscatter coefcients derived using the Raman (c) and Fernald methods

(e), respectively, at 355 nm and their absolute deviations from the common reference (b, d, e). The measurement was performed during

EARLI09 on 25 May 2009, 21:0023:00 UTC.

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U. Wandinger et al.: EARLINET intercomparison campaigns 1017

Table 5. Valid range and mean systematic deviation of particle extinction coefcients at 355 and 532 nm in four height ranges: R1 (lowest valid range2.5 km), R2 (2.56 km), R3 (612 km), and R4 (12 kmhighest valid range). n.v. denotes not valid. Rec. denotes receiver range.

Lidar Rec. Valid Mean systematic deviation, Valid Mean systematic deviation, range, 103 km1, 355 nm extinction range, 103 km1, 532 nm extinction km coefcient km coefcient

R1 R2 R3 R4 R1 R2 R3 R4

hh01 far 3.611.3 n.v. +2.6 +5.9 n.v. n.v. n.v. n.v. n.v.

near 1.511.3 7.1 1.1 +1.4 n.v. 1.211.3 6.5 +0.2 +2.9 n.v.

ms01 far 0.416.5 +9.5 +3.6 0.6 +4.7 0.816.5 7.6 +0.8 +0.1 +6.4

mu01 0.414.5 +0.6 +3.6 +3.0 5.1

po01 0.817.5 +1.1 +1.8 0.4 +3.5 0.811.3 +1.8 +6.7 +8.3 n.v.

mi01 1.210.0 13.0 +3.5 +0.3 n.v. 1.011.3 17.0 4.3 +8.5 n.v.

le01 1.920.0 13.0 0.3 +0.4 +0.6 3.022.5 n.v. 4.8 +0.9 +2.3

le02 1.715.0 1.9 +6.1 +0.8 +7.8 1.715.0 1.4 +3.9 +2.1 +5.8

bu01 1.122.0 +4.7 +2.4 2.1 +5.8 1.015.0 +14.0 +12.0 2.6 1.6

gp01 2.510.8 n.v. +2.0 0.3 n.v.

ca01 far 2.922.0 n.v. 2.5 1.8 +2.5 2.622.0 n.v. 2.8 1.5 +0.8

near 1.222.0 10.0 9.6 2.0 +3.9 0.611.3 8.3 4.5 3.7 n.v. gr01 1.014.0 +8.1 +1.1 +16.0 +24.0 1.510.0 +0.3 6.2 8.2 n.v.

ev01 2.315.0 +9.3 +6.6 +2.4 +1.9 1.58.0 +5.9 3.0 +24.0 n.v.

ma01 n.v. n.v. n.v. n.v. n.v. n.v. n.v. n.v. ba02 1.013.0 +3.2 +5.7 +0.3 +8.9 1.26.0 14.0 19.0 n.v. n.v.

la01 0.513.0 +7.5 +2.1 +2.1 +5.6 na01 1.614.0 4.3 +1.9 +0.8 +6.7 1.58.0 6.4 +9.3 0.1 n.v.

lc01 1.08.0 3.8 1.6 1.2 n.v. 0.96.0 11.0 1.9 n.v. n.v.

XeF excimer laser; the wavelength is 351 nm.

vibration-rotation Raman signals at 387 and 607 nm, respectively, or from HSRL Rayleigh and rotational Raman signals if available. In the latter case, the two temperature-dependent signals were added in order to get a prole that is nearly temperature-independent. Particle backscatter coefcients were calculated using both the Raman (Ansmann et al., 1992) and the Fernald methods (Fernald, 1984). The reference height range is 710 km for the Raman solutions and 9.410 km for the Fernald solutions. The proles were calibrated in these height ranges to mean values of 4 105

and 2.5 105 km1 sr1 at 355 and 532 nm, respectively.

A lidar ratio of 55 sr was chosen in the Fernald algorithm. A gliding average with increasing window length over height was applied in both extinction and backscatter retrievals. For extinction, the window length increases from 180 m below 1 km height to 2.7 km in the stratosphere. For backscatter, the resolution is 60 m up to 3.4 km and increases to 2.7 km in the stratosphere. All retrievals and comparisons were done without any correction of the individual overlap function.

Tables 5 and 6 show the valid range and the mean absolute deviations from the reference of particle extinction and backscatter coefcients, respectively, for the predened height ranges for all comparison campaigns. For the backscatter coefcients, the Raman solutions are considered

whenever possible. Otherwise, the Fernald solutions are used (italic numbers in the table). Proles are considered to be valid when they systematically deviate from the reference by < 0.01 km1 at the low end and by < 0.025 km1 at the far end of the prole in the case of extinction. For the backscatter coefcients, the limit is set to 3 104 km1 sr1 at both

ends. These values are of the order of the statistical measurement errors (see uctuation of the deviations in the right panels of Figs. 5 and 6) and typically about 1020 % of the particle extinction and backscatter coefcients measured in distinct aerosol layers (see left panels of Figs. 5 and 6). If cirrus clouds were present, e.g., in the case of the EARLI09 example, these height ranges were excluded from the averages in Tables 5 and 6 because of the heterogeneity of the products due to different measurement geometry. Because of different pointing and various elds of view, not only different volumes are detected, but also the inuence of specular reection and multiple scattering on the products varies from instrument to instrument.

Extinction retrievals (see Figs. 5a, b and 6a, b) clearly show the inuence of the different overlap functions. The curves are cut at the lower valid range dened for Raman signals from which they were derived (see Table 3). The lower valid range for the particle extinction coefcient is clearly

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Figure 6. Same as Fig. 5, but for 532 nm.

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Table 6. Valid range and mean systematic deviation of backscatter coefcients at 355 and 532 nm in four height ranges: R1 (lowest valid range2.5 km), R2 (2.56 km), R3 (612 km), and R4 (12 kmhighest valid range). Italicized numbers indicate Fernald retrievals; all other numbers belong to the Raman method. n.v. denotes not valid. Rec. denotes receiver range.

Lidar Rec. Valid Mean systematic deviation, Valid Mean systematic deviation, range, 105 km1sr1, 355 nm backscatter range, 105 km1sr1, 532 nm backscatter km coefcient km coefcient

R1 R2 R3 R4 R1 R2 R3 R4

hh01 far 3.513.4 n.v. 12.0 +0.1 +4.6

near 0.713.4 +0.1 +7.6 +1.7 +1.4

ms01 far 0.311.3 85.0 42.0 0.1 n.v. 0.317.0 +9.3 +1.3 +0.7 12.0

mu01 0.319.0 3.0 3.3 +0.7 +5.0

po01 0.330.0 21.0 1.8 +0.2 +6.6 0.330.0 11.0 1.6 +3.4 14.0

mi01 0.810.0 +40.0 +3.7 +10.0 n.v. 0.511.0 4.2 6.5 +0.6 n.v.

le01 1.328.0 +18.0 +2.3 0.1 +5.3 1.330.0 +15.0 6.4 0.8 +0.6

le02 0.817.0 0.5 +2.4 +1.5 +9.7 0.815.0 9.4 6.4 1.2 1.5

is01 (1.511.5) + 34.0 +33.0 +7.3 n.v.

bu01 0.530.0 22.0 +1.6 0.1 +2.5 0.515.0 12.0 6.7 +1.1 +4.9

gp01 0.62.5 9.0 n.v. n.v. n.v. 2.511.3 n.v. 5.5 0.4 n.v.

ca01 far 1.030.0 14.0 4.2 +0.6 0.1 1.427.0 +3.8 +1.5 +0.4 0.6

near 0.828.0 +64.0 +21.0 +1.4 +0.1 0.430.0 +13.0 +3.6 +0.7 5.2 gr01 0.730.0 2.6 0.3 6.3 4.0 0.415.0 +4.7 +0.0 1.8 5.7

ev01 0.225.0 +13.0 +2.3 +0.0 +6.9 0.214.0 2.2 +0.9 4.0 +17.0

ma01 1.08.0 0.5 +2.1 13.0 n.v. 0.325.0 3.0 0.7 0.2 0.3

ba02 0.528.0 +0.1 +5.2 0.6 +3.2 0.214.0 2.2 +0.9 2.7 +19.0 sf01a 1.510.0 +14.0 +3.6 +1.0 n.v.

sf02 0.512.0 0.7 +5.6 +2.2 n.v. la01b 0.213.0 +8.5 +0.2 6.3 0.3

na01 0.918.0 +12.0 +2.8 +0.7 +6.7 0.712.0 +6.6 +1.5 2.3 n.v. lc01 0.813.0 +13.0 +5.2 +2.0 +8.2 1.012.0 +12.0 +0.1 1.3 n.v.

a CuBr laser; the wavelength is 510 nm,

b XeF excimer laser; the wavelength is 351 nm.

higher and above 0.8 km for most systems, even when the receiver is optimized for the near range. When complete overlap is reached, the mean systematic deviations of the particle extinction coefcients are small and typically well below 0.01 km1 throughout the troposphere. Signal noise

is the dominating source of uncertainty then, in particular at 532 nm above 34 km height, where several curves show large uctuations (see Fig. 6a, e.g., mi01, bu01).

Backscatter coefcients can be derived closer towards the ground than extinction coefcients. In the Raman retrieval the overlap effect cancels out when both the elastic-backscatter signal and the Raman signal have the same geometrical overlap function. However, since differences in optical imaging and signal nonlinearities may occur in the near range, this compensation does not work in all cases, as can be seen from Figs. 5c, d and 6c, d. Another reason for the spread of the curves towards the ground is the identical calibration in a common reference range. When the signals are disturbed in this range, the calibration fails and the whole prole is corrupted. For instance, the particle backscatter co-

efcient at 355 nm of the MULIS system (ms01) is shifted towards values that are too low because of an analog signal distortion (positive offset) in the calibration range (710 km).

The Fernald solutions for the particle backscatter coefcients at 355 and 532 nm are shown in Figs. 5e, f and 6e, f, respectively. The curves are cut at the lower end according to the valid range of the elastic-backscatter signals provided in Tables 3 and 4, i.e., when the relative signal deviation from the reference becomes > 10 %. It can be seen that close to this lower boundary, the particle backscatter coefcients are derived with an acceptable absolute deviation of < 7 104 and < 3 104 km1 sr1 at 355 and 532 nm,

respectively. Analog signal distortions in the reference range lead to offsets here as well (see, e.g., gp01 in Fig. 5e, f). In Fig. 6e, f the result of the retrieval for the CAML lidar from Ispra (is01) is also shown. The misalignment discussed above leads to a bias of up to 5 104 km1 sr1 in the free tropo-

sphere. Beside those few exceptions, for which the reasons could be identied, the mean systematic deviation of the particle backscatter coefcients, from Raman as well as Fernald

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1020 U. Wandinger et al.: EARLINET intercomparison campaigns

retrievals, is < 2 104 km1 sr1 above the dened mini

mum valid range (see Table 6).

5 Discussion

The EARLINET intercomparisons performed between 2009 and 2013 provided a broad insight into the level of quality that has been reached in the network after nearly 15 years of operation. The decision to perform comparisons not only at product level as done before (Matthias et al., 2004) but also at signal level, based on a common preprocessing, allowed for a much deeper analysis of individual measurement channels and potential system failures. For the majority of the detection channels, mean systematic deviations from the reference over predened height ranges were below 12 % in the up

per PBL and the free troposphere. Particle backscatter and extinction coefcients could then be retrieved with an accuracy of better than 2 104 km1 sr1 and 0.01 km1,

respectively. These values are well below the quality margins of 5 104 km1 sr1 and 0.05 km1 dened by

Matthias et al. (2004) and of the order of 10 % of typical particle backscatter and extinction values observed in the PBL.

Some of the signals showed higher systematic biases, which were further investigated. Typical reasons were mis-alignment errors. Such errors were often observed in the beginning of the campaigns, in particular when the systems had been transported before. This nding depicts a shortcoming of centralized intercomparison campaigns for which many systems have to be moved and operated outside of their normal environments. Personnel that are used to working with stationary systems under well-dened conditions usually need some time to gain experience with the challenges of a eld campaign. Such problems do not occur when on-site comparisons with a well-characterized traveling standard are performed instead. Some of the systematic biases obtained could be attributed to signal distortions in analog detection channels or problems with the gluing of analog and photon-counting signals due to incorrect setting of acquisition parameters. These facts also call for expert site visits and individual training in order to check systems in their laboratory environment and advise personnel, in particular new network members, in specic quality-checking procedures.

Further shortcomings observed during the intercomparison campaigns, which could not be solved on-site, led to consequences regarding system upgrades or replacement. After the failure of CAML (is01) in the EARLI09 campaign, this system was removed from the network and the station at Ispra was upgraded with a new 3 + 2 lidar system manu

factured by Raymetrics (even if the performance of CAML could be improved after EARLI09 by operating the lidar at a controlled temperature of 35 4 C). The new lidar has

been in operation at Ispra since March 2013. Several systems with low performance in the PBL have been upgraded with near-range receivers, among them the MARTHA sys-

tem from Leipzig (le01). Also, the newest generation of PollyXT lidars (here represented by le02) is equipped with additional channels that allow measurements down to about 50 m height (Engelmann et al., 2015). In the CIEMAT lidar system (ma01), which suffered from mechanical instability and electronic disturbances in the two Raman channels during SPALI10, the respective PMTs (model Hamamatsu R928) were replaced. The new data acquisition is based on a Licel/Hamamatsu PMT R7400P-20 for the 607 nm channel and a Licel/Hamamatsu PMT R9880 U-110 for the 387 nm channel, and combined analog and photon-counting detection is applied. Moreover, mechanical modications for a better robustness of the system were implemented. After the LELI13 campaign in Lecce, during which some biases in the near range of the UNILE system (lc01) had been detected, the receiver of the multiwavelength lidar at this station was modied and the single focussing lens in front of each detector was replaced with a collimator in order to avoid geometrical effects due to inhomogeneities of the detector surfaces.

Regarding the Alomar Tropospheric Lidar (an01), which showed major deciencies during ALI09, a number of measures, implemented after discussion with EARLINET experts, resulted in distinct improvements of the system. The electrical noise, induced by the laser, could be reduced by using a ber coupling to achieve a galvanic separation of the data acquisition electronics and the light source. The cause for the poor quality of the 607 nm channel was identied as the combination of an interference lter that is too broad and a photomultiplier with poor quantum efciency at this wavelength. During a system refurbishment, this channel has been removed, the main mirror was recoated, and the crosstalk of the depolarization channels was minimized with additional polarizing sheet lters. In addition, an automated polarization calibration unit was installed (Freudenthaler et al., 2009).

In general, dedicated intercomparison campaigns as discussed in this paper require large efforts and can thus only be performed sporadically. Nevertheless, because of the lack of external calibration standards for aerosol lidar observations, any instrument intercomparison is of great value for quality assurance. Therefore, following the principle of best scientic practice, every opportunity of cross-checking the quality of measurements by a direct comparison of results from collocated observations should be used. Within EARLINET and in collaboration between EARLINET and other research projects, direct instrument intercomparisons are performed whenever possible. Regular intercomparisons take place at sites where more than one system is available, e.g., because the groups own one of the reference systems in addition to their stationary lidar (Potenza, Minsk, Hamburg, Munich/Maisach) or apply other lidars in experiments outside of EARLINET (Leipzig, Naples). Other opportunities are related to dedicated eld campaigns in which several lidars often participate. In this context, comparisons with downlook-

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U. Wandinger et al.: EARLINET intercomparison campaigns 1021

ing airborne lidars may also be used to check the system performance in the near range.

6 Conclusions and outlook

In this paper, we have presented results of the EARLINET instrument intercomparison campaigns between 2009 and 2013. During this period, about two-thirds of the EARLINET systems performed comparison measurements with one or more reference systems. In two dedicated campaigns, EARLI09 and SPALI10, 15 instruments underwent this quality-assurance procedure. EARLI09 also served to qualify the reference systems that are used as traveling standards within the network. With these reference instruments, six other systems were checked during direct station visits. Altogether, more than 100 individual measurement channels were examined, based on a common strategy of signal preprocessing and evaluation following the principles of the EARLINET Single Calculus Chain. In most cases, a very good agreement of signals as well as derived aerosol products with the dened reference could be obtained. The inter-comparisons have reinforced condence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.

EARLINET is a living network that is continuously in development, both regarding the instrument level and the network distribution. Most of the stations regularly upgrade their systems by adding new measurement capabilities based on recent experience, technological developments, and available funding. Thus, a complete assessment of all systems at any time in any specic setup through intercomparison with a reference system is not possible. Therefore, complementary quality-assurance concepts need to be applied. EARLINET requires regular internal system check-ups in addition to the sporadic intercomparisons. Specic internal check-up tools, such as the telecover test for the near range and the Rayleigh t for the far range, have been developed and must be applied at least once per year as well as after each major system upgrade. These activities are discussed in detail by Freudenthaler et al. (2016).

Within the ACTRIS-2 project, which started in May 2015, the quality-assurance efforts of EARLINET will be further improved. In order to provide a long-term, sustainable infrastructure that can serve the lidar community even beyond EARLINET, the Lidar Calibration Centre, LiCal, will be established. LiCal is a common effort of the EARLINET groups at INOE (Bucharest, Romania), CNR-IMAA (Potenza, Italy), and LMU (Munich, Germany). It will provide lidar calibration services from the characterization and optimization of single components to the assessment of complete systems through intercomparison with reference systems. The POLIS (mu01) and MUSA (po01) reference lidars will be further upgraded and used exclusively for this

purpose in the future. LiCal will also host a lidar training laboratory in order to educate station personnel in applying lidar check-up tools and maintaining lidar calibration. In this way, the long-term quality-assurance strategy of EARLINET, which has been successfully established over the past 15 years, will be further consolidated.

Acknowledgements. The nancial support for EARLINETASOS by the European Commission in the Sixth Framework Programme under grant RICA-025991, for ACTRIS in the Seventh Framework Programme under grant agreement no. 262254, and for ACTRIS-2 in HORIZON 2020 under grant agreement no. 654109, as well as the ESA nancial support under the ESRIN contract no. 22202/09/I-EC, are gratefully acknowledged. We thank Julia Fruntke, Christian Herold, and the technical staff of TROPOS for the logistical support, radiosonde launches, and weather forecast during EARLI09, as well as the Atmosphere Group of INTA-Madrid for their support with the radiosounding during SPALI10. The Universitat Politcnica de Catalunya wishes to acknowledge the technical and logistic support of Joaquim Giner and Rubn Tardo. The work of the INOE team was supported by grant no. 229907 FP7-REGPOT-2008-1. The Universitat Politcnica de Catalunya group received support from the Spanish Ministry of Economy and Competitiveness (project TEC2012-34575) and of Science and Innovation (project UNPC10-4E-442), as well as from the Department of Economy and Knowledge of the Catalonia autonomous government (grant 2014 SGR 583).

Edited by: R. Ferrare

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