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Atmospheric Environment 47 (2012) 519e532

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

A regional model of European aerosol transport: Evaluation with sun photometer,lidar and air quality data

J. Meier a,*, I. Tegen a, I. Mattis a, R. Wolke a, L. Alados Arboledas b,c, A. Apituley d,e, D. Balis f, F. Barnaba g,A. Chaikovsky h, M. Sicard i, G. Pappalardo j, A. Pietruczuk k, D. Stoyanov l, F. Ravettam, V. Rizi n

a Leibniz Institute for Tropospheric Research, Leipzig, GermanybApplied Physics Department Sciences Faculty, University of Granada, SpaincCEAMA-Andalusian Center for Environmental Research, University of Granada, Andalusian Regional Government, SpaindRIVM-National Institute for Public Health and the Environment, Bilthoven, The NetherlandseKNMI-Royal Netherlands Meteorological Institute, De Built, The Netherlandsf Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, GreecegConsiglio Nazionale delle Ricerche-Istitutio di Szienze dell’ Atmosfera e del Clima (CNR-ISAC), Rome, ItalyhB.I. Stepanov Institute of Physiscs, Minsk, BelarusiUniversitat Politècnica de Catalunya (UPC), Barcelona, SpainjConsiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Potenza, Italyk Institute of Geophysics, Polish Academy of Sciences, Warsaw, Polandl Laser Radars Laboratory, Institute of Electronics, BAS, Sofia, BulgariamCentre National de la Recherche Scientifique-Institut Pierre Simon Laplace, Paris, FrancenCETEMPS, Dipartimento di Fisica, Università degli Studi dell’Aquila, LAquila, Italy

a r t i c l e i n f o

Article history:Received 10 May 2011Received in revised form12 September 2011Accepted 14 September 2011

Keywords:Regional transport modelAerosolEuropePM2.5

AODVertical backscatter coefficientEvaluation

* Corresponding author.E-mail address: jessica.meier@tropos.de (J. Meier)

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.09.029

a b s t r a c t

Aerosol transport simulations within Europe were performed with the regional transport model COSMO-MUSCAT for two different time periods, July 19e26, 2006 and February 16e26, 2007. Simulated PM2.5,backscatter profiles and aerosol optical depths (AODs) were compared to observations, showing goodagreements in magnitude, shape and day-to-day variations. Maximum AODs (>0.4) were found overMiddle Europe and minimum AODs (<0.13) over the ocean during both time periods, corresponding toregions of high (PM2.5 > 10 mg m�3) and low (PM2.5 < 4.0 mg m�3) concentration near the surface. Verticalaerosol distributions were evaluated with lidar measurements from the EARLINET ground network andCALIPSO satellite retrievals. The characteristic vertical distribution and the differences for the summer andthe winter cases were represented well by the regional model. Mean differences between �5.0 � 10�7

to�2.0 � 10�7 m�1 sr�1 (summer case) and �2.3 � 10�6 to 1.0 � 10�6 m�1 sr�1 (winter case) from 0.0 to2.5 km altitude were found between observed (space-based lidar) and simulated backscatter coefficients.For the cases that were investigated in this study different prescriptions of the vertical distribution at thelateral model boundaries resulted in only small differences in aerosol distributions within the interior ofthe model region.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Aerosol forcing can be estimated by using satellite informationto initialize an aerosoleclouderadiation model with data fromseveral satellite instruments and surface radiation flux measure-ments (Kim and Ramanathan, 2008). Therefore assumptions aboutaerosol properties have to be made, which include e.g. aerosolchemical composition, particle size and their vertical distribution.

.

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In satellite data a differentiation between natural or anthropogenicsources of aerosols that would be a necessary information to enablethe estimate of their influence on climate is mostly not possible(Matthias et al., 2004; Wendisch et al., 2006).

The relative contributions of the different aerosol types to theatmospheric aerosol mixture can be determined by models ofatmospheric aerosol transport and transformation processes onglobal and regional scales. Several studies estimate radiativeforcing at certain locations within Europe by the determination ofaerosol column optical properties or the characterization ofchemical compounds by in-situ or ground-based measurements(e.g. Meloni et al., 2003; Horvath et al., 2002; Iorga et al., 2007).

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532520

While the global model intercomparison study AeroCom (AerosolComparisons between Observations and Models; http://nansen.ipsl.jussieu.fr/AEROCOM/aerocomhome.html; (e.g. Kinne et al.,2006)) showed good agreements between AOD distribution fromglobal models and observations, it also highlighted considerabledifferences in estimates of the aerosol forcing by the individualmodels due to different descriptions of the mixture of certainaerosol species. Regional model studies of European aerosoldescribe atmospheric aerosol load and comparewith observed AOD(Hodzic et al., 2006), and describe interaction processes betweenparticles and the atmosphere (Vogel et al., 2009). Simulationsdemonstrate strong dependencies of aerosol distribution on season(Matthias, 2008; Péré et al., 2010) and meteorological conditions(Marmer and Langmann, 2007).

A correct description of vertical distributions of aerosol opticalproperties is necessary to describe their radiative effects, which canmodify atmospheric stabilities, convective processes and formationand lifetime of clouds (Redemann et al., 2000). Only few measure-ments of vertical profiles of chemical compounds were performedwithin the recent years (e.g. Morgan et al., 2009) as air-bornemeasurements are expensive and temporally and spatially limited.Lidarmeasurements provide an insight into the vertical distributionof atmospheric particles and their optical properties (Mattis et al.,2008; Müller et al., 2007). Within the EARLINET (European Aero-sol Research NETwork (Pappalardo et al., 2010)) network data fromabout 25 ground-based lidar stations are available. Since April 2006the space-based lidar on the CALIPSO (Cloud-Aerosol Lidar andInfrared Pathfinder Satellite Observations) satellite also providesinformation about the vertical distribution of aerosols (Winkeret al., 2009). Comparisons between model simulation results andobserved parameters, like AOD (Ayash et al., 2008), verticalextinction or backscatter coefficients (Guibert et al., 2005) aremostly performed for weekly, monthly or annual averages. Whilethe agreements in long-term averages are often satisfying, it wasalso shown by Guibert et al. (2005) that global models do haveproblems to represent individual measurements, like individualaerosol extinction profiles, for individual events.

In this study, a regional aerosol transport model was used toperform simulations for specific time periods. Information fromlidar profiles was used for model validation and the characteriza-tion of initial vertical aerosol distributions at the lateral modelboundaries. Additionally, sun photometer measurements wereused to obtain information about the particle optical depth formodel initialization at the boundaries and model evaluation.

2. Model system

Model simulations were performed with the regional modelsystem COSMO-MUSCAT (COSMO: Consortium for Small-scaleModeling; MUSCAT: MultiScale Atmospheric Transport Model).

COSMO is the operational weather forecast model of theGerman Weather Service (DWD) (Steppeler et al., 2003). It isa 3-dimensional non-hydrostatic meteorological model, which usesreanalyzed input data from the global model GME (also developedby the DWD).

The 3-dimensional chemistry transport model MUSCAT (Wolkeet al., 2004) includes emission, transformation processes andtransport of aerosol species up to 2.5 and 10 mm diameter. Includedare primary aerosol particles (PPM2.5 and PPM10, including primaryorganic material and elemental carbon (EC), ammonium nitrate(NH4NO3), sulfuric acid (H2SO4) and ammonium sulfate ((NH4)2SO4).PM2.5 is the sum of PPM2.5, NH4NO3 and (NH4)2SO4. Horizontaladvection processes are computed with an impliciteexplicit timeintegration scheme (Wolke and Knoth, 2000). The gas-phasechemistry in MUSCAT is described by the chemical model RACM

(Regional Atmospheric Chemistry Mechanism; (Stockwell et al.,1997)). MUSCAT computes total mass concentration of chemicalcompounds and aerosols in a bulk formulation, size distributions arenot explicitly simulated. Local emissions of chemical compoundsfrom EMEP/CORINAIR (European Monitoring and Evaluation Pro-gramme (http://www.emep.int/)/Co-ordinated Information on theEnvironment in the European Community-AIR (http://www.eea.europa.eu/publications/EMEPCORINAIR3)) are used as input forMUSCAT. Formation and transport processes of secondary inorganicaerosols are simulated as described by Renner andWolke (2010). Thesimulation of PM10 was tested as part of an intercomparison study(Stern et al., 2008).

COSMO is coupled with MUSCAT providing actual atmosphericconditions for simulating aerosol formation and transport (Rennerand Wolke, 2010). In this study, 40 vertical layers were used, thelowest layer extending to 67 m. The model top for MUSCAT was setto 8000 m. The horizontal extension of the model domain was136�156 grid cells (lower left corner of themodel domain: 10.1� Wand 27.5� N) with a grid spacing of 28 km. The meteorologicalmodel COSMO was re-initialized every 24 h and run for 48 h. After24 h of each COSMO cycle MUSCAT was restarted using the aerosoldistribution of the previous cycle and both models run parallel for24 h. This ensures that the deviations in meteorological fields in themodel from reality remain small.

2.1. Calculation of optical properties

From the mass of the modeled aerosol species PPM2.5 (whichincludes e.g., EC and primary organic particles but no mineralmaterial), NH4NO3, H2SO4 and (NH4)2SO4 aerosol optical propertiescan be calculated. Since EC that is part of PPM2.5 is stronglyabsorbing at solar wavelength it is treated separately. For thispurpose we define PPM*

2:5 ¼ PPM2:5 � EC. Hygroscopic growthbehavior that influences extinction coefficients is described for theindividual compounds by empirical parameterizations (based onmeasurements; (Tang and Munkelwitz, 1994)). As example, thegrowth factor GF; (Meier et al., 2009) for NH4NO3 is given:

GFNH4NO3¼ 0:0004$expðRH=12:1Þ þ 1:2 (1)

where RH is the relative humidity in %. The hygroscopic behavior atlarger relative humidities (e.g. RH � 98%) is very difficult to deter-mine (Wex et al., 2006). Therefore aerosol models differ in theirtreatment of hygroscopic growth at high relative humidities (Textoret al., 2006). In this study the growth of hygroscopic aerosolparticles is limited up to 95% relative humidity, and held constant athigher humidities. PPM*

2:5 and EC particles are assumed to behydrophobic and do not absorb water (Weingartner et al., 1997;Massling et al., 2005). Based on the dry volume Vdry

i of the chem-ical substance, the density rmix

i of the mixture of this substance andthe absorbed water, and the mass extinction efficiency (MEEi[m2 g�1]) of the named chemical substance i the extinction for eachsubstance (Exti [m�1]) is calculated by:

Exti ¼ rmixi $Vdry

i $GF3i $MEEi (2)

It was shown by Kinne et al. (2006, Table 4) that the range ofavailable values of MEEi for different aerosol species used indifferent models can be very broad. In addition to the dependencyon the chemical substance (Mallet et al., 2003), MEEi depends onthe wavelength (Takemura et al., 2002), the relative humidity(Penner et al., 2002) and the size of the particles (Penner et al.,2002). If the aerosol species are mixed, like, e.g. a mixture ofcarbonaceous compounds and sulfate, the value of the MEEi fromthe values for the individual compounds is used (Kim et al., 2008).

Table 1EARLINET stations which were used for the model initialization and validation.Within the brackets, the number of available profiles is presented.

Date July, 2006 February, 2007

14 Potenza; 15.8� E, 40.6� N (1)15 Ispra; 8.6� E, 45.8� N (23)

Saint Michel; 5.7� E, 43.9� N (3)Palaiseau; 2.2� E, 48.7� N (11)

16 Granada; 3.6� W, 37.2� N (4)17 Thessaloniki; Leipzig; 12.4� E, 51.4� N (1)

23.0� E, 40.7� N (1)Belsk; 20.8� E, 51.8� N (2)L’Aquila; 13.3� E, 42.4� N (1)Minsk; 27.6� E, 53.9� N (1)

19 Thessaloniki; Palaiseau; 2.2� E, 48.7� N (2)23.0� E, 40.7� N (2)

Saint Michel; 5.7� E, 43.9� N (4)20 Thessaloniki; Barcelona; 2.1� E, 41.4� N (1)

23.0� E, 40.7� N (1)Madrid; 3.7� W, 40.5� N (1)

21 Thessaloniki; Leipzig; 12.4� E, 51.4� N (1)23.0� E, 40.7� N (2)

Naples; 14.2� E, 40.8� N (1)22 Leipzig; 12.4� E, 51.4� N (1)

Ispra; 8.6� E, 45.8� N (12)23 Thessaloniki;

23.0� E, 40.7� N (22)24 Thessaloniki;

23.0� E, 40.7� N (13)Barcelona; 2.1� E, 41.4� N (1)Granada; 3.6� W, 37.2� N (1)Bilthoven; 5.2� E, 52.1� N (1)Leipzig; 12.4� E, 51.4� N (2)Naples; 14.2� E, 40.8� N (1)Potenza; 15.8� E, 40.6� N (1)Sofia; 24.4� E, 42.7� N (14)

25 Thessaloniki;23.0� E, 40.7� N (5)Leipzig; 12.4� E, 51.4� N (1)Sofia; 24.4� E, 42.7� N (4)

26 Ispra; 8.6� E, 45.8� N (1)Palaiseau; 2.2� E, 48.7� N (5)Saint Michel; 5.7� E, 43.9� N (1)

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 521

For this study values for MEEi based on the publication of Kinneet al. (2006) are used (PPM*

2:5 ¼ 5:7 m2 g�1, EC ¼ 8.9 m2 g�1,(NH4)2SO4 (dry) ¼ 8.5 m2 g�1, NH4NO3 (wet) ¼ 1.24 m2 g�1, H2SO4(dry)¼ 8.5 m2 g�1). A value of 1.24m2 g�1 (representing wet nitrateunder urban conditions (Mallet et al., 2003)) was chosen forMEENH4NO3

for the model simulations.The calculated individual extinction coefficients Exti of PPM*

2:5,EC, (NH4)2SO4, NH4NO3 and H2SO4 are summarized to obtain totalextinction (representing the extinction of PM2.5) for each modelgrid cell. The vertical integration of the total extinction provides theAOD of the atmospheric column:

AOD ¼Xl

X5i¼1

Exti (3)

where l is the atmospheric layer and i the number of species. Thebackscatter coefficient BSC [m�1 sr�1] is computed from thesummarized extinction coefficient

P5i¼1 Exti and the extinction-

to-backscatter (lidar) ratio LR [sr]:

BSC ¼P5

i¼1 ExtiLR

(4)

Lidar measurements provide profiles of aerosol backscattercoefficients,which are comparedwith simulated vertical backscatterprofiles. For this study amean LR of 50 sr is used, which correspondsto measurements within Central Europe (Müller et al., 2007).

3. Observations used in this study

Measurements of chemical composition of the atmosphericaerosol at the surface (EMEP) were used for model evaluation.Ground- (EARLINET) and space-based (CALIPSO) lidarmeasurementsas well as sun photometer (AERONET) measurements were used forcomparisons between simulated and measured aerosol parameters.

3.1. PM2.5

Surface measurements of particulate matter up to 2.5 mm and10 mm particle diameter (PM2.5, PM10) are available from severalEMEP stations within Europe that regularly measure atmosphericparticle concentrations and their composition (EMEP, 2009). Atsome stations information about ion concentration, like SO2�

4 , arealso available. These measurements give an overview about thehorizontal distribution of aerosols and chemical compounds withinEurope at surface level. However, these data do not include anyinformation about the vertical distribution of chemical compounds.In most cases they are available as 24 h averages.

3.2. Lidar profiles

3.2.1. Ground-based lidar measurementsCurrently, about 25 EARLINET (http://www.earlinet.org/)

stations in Europe carry out lidar measurements three times aweekto obtain information about vertical aerosol distributions(Pappalardo et al., 2010). The large amount of multi-wavelengthaerosol backscatter data is often combined with Raman lidarmeasurements that provide information about aerosol extinction.The measurements at these stations are performed at mainly 355,532 and 1064 nm wavelength. Data from 14 EARLINET stationswere used for this study (Table 1).

3.2.2. Space-based lidar measurementsSince April 2006 the lidar CALIOP (Cloud-Aerosol Lidar with

Orthogonal Polarization) carries out measurements of profiles of

backscatter coefficients (at 532 nm and 1064 nm) on board theCALIPSO satellite (http://www-calipso.larc.nasa.gov/). Measure-ments of vertical aerosol and cloud distributions are continuouslyperformed between 82� N and 82� S, providing a near-globalcoverage. A detailed description of the CALIPSO instrument aswell as the various available results is given byWinker et al. (2009).

Total backscatter signals at 532 nm of CALIPSO Lidar Level 2(Version Releases 3.01) were used. In that version individual totalbackscatter profiles are horizontally averaged over 5 km. Only thoselidar profiles were used were aerosols and clear air were found.Additionally, aerosol backscatter values with confidence levelbetween �100 (100% confidence) and �80 (80% confidence) weretaken into account.

3.3. Sun photometer measurements

Within AERONET (Aerosol Robotic Network, Holben et al.(1998)), measurements of optical properties of atmospheric aero-sols are obtained automatically by sun photometers. Data of AODare only available during daytime and for cloud-free conditions. Themeasurements are performed at wavelengths between 340 nm and1020 nm. Level 2.0 data (cloud-screened and quality-assured) from30 stations were used for this study.

Many EARLINET stations perform their measurements at532 nmwavelength. However the AOD is often notmeasured at thiswavelength with the AERONET sun photometers. By means of

Table 2AERONET stations which were used for the determination of the mean weightingfactors for each simulation period and lateral model boundary.

Boundary July 19e26, 2006 February 16e26, 2007

East Minsk (27.6� E, 53.9� N) MinskChisinau (28.8� E, 47� N) Zvenigorod (36.8� E, 55.7� N)Moscow (37� E, 55� N) MoscowSevastopol (33.5� E, 44.6� N) Sevastopol

West Cabo da Roca (9� W, 38� N) Cabo da RocaChilbolton (1.4� W, 51.1� N) ChilboltonEvora (7� W, 38� N) EvoraLe Fauga (1� E, 43� N) Caceres (6.3� W, 39.5� N)

El Arenosillo (6.7� W, 37.1� N)North Gustav Dalen Tower Belsk

(17.5� E, 58.6� N) (20.8� E, 51.7� N)Helsinki (24.9� E, 59.9� N) Cabauw (4.9� E, 52� NSMHI (16.2� E, 58.6� N) Hamburg (10� E, 53.6� N)Toravere (26.5� E, 58.3� N) Toravere

South Epanomi (23� E, 40.4� N) Barcelona (2.1� E, 41.4� E)Crete (25.3� E, 35.3� N) CreteGranada (3.6� W, 37.2� N) Messina (15.6� E, 38.2� N)Lecce (18.1� E, 40.3� N) Potenza (15.7� E, 40.6� N)Thessaloniki (23� E, 40.6� N) Thessaloniki

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532522

a second order polynomial fit the AOD at 532 nm was estimatedfrom measurements at the other wavelengths for this study withthe uncertainty of w0.01e0.02, as suggested by Eck et al. (1999).

4. Case studies

The aerosol model simulations were performed for two timeperiods, July 19e26, 2006 and February 16e26, 2007, respectively.Since computations of meteorological parameters start 24 h beforesimulation of chemical and aerosol parameters, the simulation ofthe two periods was started on July 18, 2006 and February 15, 2007.The two periods were selected such that they represent differentweather situations.

4.1. July 19e26, 2006

This summer period was characterized by nearly constantweather conditions. An anticyclonewas situated over almost wholeof Europe, and Middle Europe was strongly influenced bysubtropical air masses. Disturbances were caused by weak short-wave troughs and convergence zones with induced thunderstormswith rainfall and wind gusts. On July 21 a convergence zone passedGermany and caused weak rainfall on its way to Eastern Europe,and the southwestern regions of France were influenced by rain onJuly 22. In general, the changes of the predominant weather situ-ation were very weak during this period. Thus only a weak trans-port of atmospheric particles from outside into the model area isexpected. During this time period aweak dust event occurred in thesouthern regions of Europe. Dust transport is not included in themodel study presented here, therefore model-data comparisons ofoptical properties in regions influenced by the dust are difficult tointerpret.

4.2. February 16e26, 2007

During this winter period strong temperature differencesbetween the northern and southern regions of Europe occurred.The passage of low pressure systems mainly determined theweather in Europe. In particular, at the end of the period (February22e26) radiosonde measurements show strong disturbancesoccurring in Europe and causing high wind speeds, especially onFebruary 22 from the eastern direction. During the whole periodseveral snow- and rainfall events occurred. Rainfall occurred inItaly, Spain, Portugal and Ireland at the beginning of the selectedtime period (February 17e19), whereas snowfall events occurredover Russia, Denmark and Germany (February 20e23).

5. Model setup

The vertical distribution of chemical compounds and aerosolparticles can be prescribed at the lateral model boundaries ofCOSMO-MUSCAT. In this study, a combination of lidar and sunphotometer measurements was used to improve the description ofthe vertical profiles of atmospheric particles at the lateral modelboundaries. This included prescription of vertical distributions ofprimary aerosol species PPM2.5 and PPM10 that are characteristic forthe European aerosol at the lateral model boundaries. Secondaryaerosol (sulfate and nitrate species) is not prescribed at the modelboundaries as we assume that they are formed entirely within themodel domain from European sources. Surface concentrations forPPM2.5 and PPM10 were determined from measurements at allavailable EMEP stations. A PPM2.5 concentration near the surface of8.0 mg m�3 and 6.0 mg m�3 were determined for the periods July18e26, 2006 and February 15e26, 2007, respectively. A maximumsurface concentrations of PPM10 � PPM2.5 ¼ 2.0 mg m�3 was

determined for both periods. These concentrations stay constant ateach day of the simulation period. The decrease of these concen-trations with increasing altitude is difficult to determine, due to thelack of observations of vertical distributions of chemicalcompounds. Therefore a decrease to 10% in the free tropospherewas assumed. The uncertainty regarding this behavior is notknown. Lidar profiles were used to describe the height of theboundary layer.

European lidar measurements for the years 2000e2002 werecompiled by Wandinger et al. (2004). At the EARLINET stationin Aberystwyth, Wales, (52.4� N, 4.1� W) lidar measurements(at 355 nm) represent aerosol conditions at the western boundaryof the European model domain. These lidar measurements repre-sent clean atmospheric conditions resulting from northerly andwesterly flows. Based on those individual backscatter profiles meanprofiles were calculated, representing the summer and the winterperiods.

For the summer period (July 19e26, 2006) the vertical distri-bution of PPM2.5 and PPM10 is prescribed as follows: The maximumconcentration remains constant from bottom to 700 m heightabove ground, then decreases from 700 to 2000 m to the minimumvalue, which remains constant up to 8000 m altitude. This profilewas used for all four lateral model boundaries. For the winter case(February 16e26, 2007) the vertical distribution for the namedsubstances is described by maximum concentration from thesurface level up to 300 m above ground. From 300 m to 2000 m theconcentration decreases to the minimum value and remainsconstant from 2000 m to 8000 m.

5.1. Weighting factors based on sun photometer measurements

As mentioned above, the determination of the correct concen-tration profiles for certain compounds is difficult due to the lack ofobservation data. Here, lidar profiles were used to describe theshape of aerosol extinction profiles and to determine the boundarylayer height. The concentrations profiles can then be adjusted bya weighting factor to obtain realistic aerosol optical thicknesses atthe lateral model boundaries. Theweighting factors are determinedby the ratios of measured and modeled AODs at the modelboundaries. Sun photometer measurements at European AERONETstations, located next to the boundaries (Table 2) were used for thedetermination of these weighting factors. To determine the

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 523

weighting factors, model simulations with the different boundarydescriptions and a weighting factor of 1 at all four model bound-aries were performed first. The mean ratio of simulated andobserved AOD, separated for each boundary, were calculated foreach simulation period. Simulated values of AOD are available ateach full hour and observed values of AOD were used that wereobtained within 15 min before and after the full hour. The calcu-lated weighting factors (July 19e26, 2006: East ¼ 0.6, West ¼ 1.0,South ¼ 0.9, North ¼ 0.6; February 16e26, 2007: East ¼ 0.6,West ¼ 0.7, South ¼ 0.6, North ¼ 0.6) were used to adjust theprimary aerosol concentration profiles at the lateral modelboundaries.

6. Results

Simulations with COSMO-MUSCAT were performed for the twodifferent time periods July 19e26, 2006 and February 16e26, 2007.The simulation results are evaluated by comparisons with obser-vation data from EMEP, AERONET, EARLINET and CALIOPmeasurements.

6.1. PM2.5

A comparison between daily average values of observed surfacePM2.5 and simulated results (simulated PM2.5 ¼ PPM2.5 þNH4NO3 þ (NH4)2SO4) is shown in Fig. 1 for both time periods. Atthose stations with information about SO2�

4 concentrations thesimulated daily average concentration of SO2�

4 is plotted as well.While the model is mostly able to represent the day-to-day

variation of PM2.5 and SO2�4 concentrationswell, often differences in

magnitude occur. Simulation results underestimate observationdata in most cases, while the magnitude and the day-to-day varia-tions in PM2.5 concentrations is represented correctly for both timeperiods atmany stations.Measurements represent large fractions ofunknownmaterial included in PM2.5 and PM10 (Spindler et al., 2010),which may include secondary organic aerosol species or mineraldust which is not simulated in this model version, thereforeunderestimation of PM in the model is to be expected. During July19e26, 2006, model simulation underestimate PM2.5 observationdata at nearly all stations. Themean relative bias (MRB) differs fromday-to-day with lowest MRB (�31%) on July 26 and highest MRB(�50%) on July 21. Differences during July 2006, especially at thosestations located in the South (e.g. Barcarolla, Montelibretti, OSaviñao, Penausende and Viznar) may be caused by a Saharan dustevent. Dust particles cause higher particle concentration that is notcomputed in this model version of COSMO-MUSCAT. Enhancedlevels of Saharan dust from a dust forecast model (BSC/DREAM;http://www.bsc.es/projects/earthscience/DREAM/; (Pérez et al.,2006)) within the lowest model level indicate that modeled PM2.5concentrations should be increased in regions influenced by thedust. Results from the DREAM model show that <3e80 mg m�3

(Barcarolla), <3e40 mg m�3 (Montelibretti), <3e160 mg m�3

(O Saviñao), <3e160 mg m�3 (Penausende) and 10e160 mg m�3

(Viznar) of dust concentration has to be added for the summerperiod.

Both over- and underestimation of observed PM2.5 data arefound during February 2007 at different stations. On February 17,MRB was only �3% whereas on February 18, the highest differencewas reached (MRB ¼ �35%). Only on February 26, most stationssignificantly overestimate the observation data (MRB ¼ 13%). Asshown in Fig. 1, in Illmitz (Austria) very large discrepancies occur,which may be due to local characteristics. Illmitz is located 117 mabove sea level and therefore is the lowest place in Austria. Themodel is not able to resolve this specific topography.

Regarding the SO2�4 concentration, a mean absolute bias (MAB)

of 1.1 mg m�3 (root mean square error (RMSE) ¼ 1.9 mg m�3, avail-able data: 98) for the summer period and MAB ¼ 0.9 mg m�3

(RMSE ¼ 1.4 mg m�3, available data: 126) was determined. Exceptfor Barcarolla, Spain (summer period) at nearly all stations themodel simulation overestimates the observed SO2�

4 concentration(Fig. 1).

In Fig. 2 the average horizontal distribution of simulated PM2.5for both time periods in the lowest atmospheric level is shown. Thesimulation for the summer period yields low (<4 mg m�3) PM2.5over the ocean and at the northern boundary. The winter caserepresents larger areas of such low particle concentrations, only thecenter and the northern part of the model domain show largervalues. For both time periods larger concentrations (>8 mg m�3) ofPM2.5 are found in Central Europe. Hot spots of high PM2.5concentrations (e.g. Paris, London, Milan, Moscow, regions of Ben-elux and surroundings and the Balkan) are clearly shown in bothconcentration maps. Here, PM2.5 of >16 mg m�3 is simulated.

6.2. Aerosol optical depth

The simulated average horizontal distribution of AOD for July21e26, 2006 and February 18e26, 2007 is shown in Fig. 3. For bothperiods lowest values of AOD (<0.13) are simulated over the oceanand at the lateral model boundaries. Average AODs of 0.21 and 0.12are computed for the summer and winter periods, respectively. ForJuly 21e26, 2006 higher values (AOD > 0.40) are found in CentralEurope. During this time period atmospheric particles accumulatedand caused larger AOD values within Europe compared to theFebruary case, e.g. over Germany, Great Britain, France and Italy.Highest values (AOD > 0.54) are reached in the region of Croatia,Hungary, Bosnia and Herzegovina and Romania. High AOD valuesoccur in Northern Italy. In contrast, during February 18e26, 2007snow- and rainfall events caused a wet removal of atmosphericparticles, which explains the lower average values of AODcompared to the summer period. Higher values of AOD (for bothtime periods) occur in the southeastern model region, which iscaused by particles from local emissions (compare also Fig. 4).

The representation of the transport of atmospheric particles intothe model domain is shown in Fig. 4. The mean relative differencesof AOD for each model grid cell (AODrel. diff.) are determinedaccording to:

AODrel: diff : ¼Result 1� Result 2

Result 2(5)

where Result 1 ¼ AOD of simulation with individual weightingfactors at the lateral model boundaries and Result 2 ¼ AOD ofsimulationwith no transport through the lateral model boundaries.Here, the difference maps illustrate the influence of the transport ofprimary aerosol species from the outside of the model domain onthe AOD distribution. For both time periods the transport affects allregions next to the lateral model boundaries. At the westernboundary the influence is strong, the AODrel. diff. is larger than 0.8,due to the transport by westerly winds. Large areas next to thewestern boundary are stronger influenced by the transport than theother boundaries, caused by weather systems moving fromwest toeast. With increasing distance from the lateral boundaries theimpact of the transport from outside the model domain decreases.During July 2006 the transport of atmospheric particles fromoutside the domain is strongly limited to the lateral modelboundaries. The large area within Central, South and East Europeremains unaffected by the transport. Here, a fraction of less than 0.2of the AOD can be expected to be caused by lateral transport, AOD ismainly controlled by local emissions in these regions. A similar

Fig. 1. Daily average values of observed PM2.5 (black open triangles) and SO2�4 (blue open squares) and simulated PM2.5 (black filled triangles) and SO2�

4 (blue filled squares)concentration at eight EMEP stations. Location of the EMEP stations, presented here: Schauinsland (47.9� N, 7.9� E), Illmitz (47.8� N, 16.8� E), Iskrba (45.6� N, 14.9� E), O Saviñnao(43.2� N, 7.7� W), Montelibretti (42.1� N, 12.6� E), Penausende (41.3� N, 5.9� W), Barcarolla (38.5� N, 6.9� W), Viznar (37.2� N, 3.5� W). (For interpretation of the references to color inthis figure legend, the reader is referred to the web version of this article.)

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532524

Fig. 2. Average horizontal distribution of simulated PM2.5 [mg m�3] performed for July 21e26, 2006 and February 18e26, 2007, respectively within the near ground level.

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 525

pattern is found for the February 2007 time period. Again, largeareas within Europe remain unaffected by the lateral transport.Although the weather situation was not as stable as during thesummer period, rain and snowfall events removed the particlesfrom the atmosphere, reducing the importance of aerosol transportfrom the outside of the domain.

Time series of observed and simulated AOD at several AERONETstations are shown in Fig. 5. Over- and underestimation of themodeled AOD compared to the observed AOD can be caused bysimulation of too large/small amounts of chemical compounds anda misinterpretation of the hygroscopic growth and thus of theoptical properties in the model. The daily average Ångströmexponent (determined from measurements at 500 and 870 nm) inGranada (July 19e26, 2006) shows average values between 0.3 and0.7, which can be an indication for the presence of large particleslike dust (not considered in this model experiment). But in general,for both time periods a good agreement between observation andsimulation is found. The day-to-day variability is well characterizedby the model simulations. To determine some statistical parame-ters, we note that simulated AODs represent hourly values incontrast to observed AOD. To compare simulated and observed AODonly those observed values were used which were registeredwithin a half hour around the model output time. If there was morethan one observed value available, a mean value was determinedbefore calculating the difference. In Fig. 6 normalized frequencies ofabsolute differences between simulated and observed AOD areshown. Here those observation data measured at stations that were

Fig. 3. Average horizontal distribution of simulated AOD (at 550 nm) perf

also used for model initialization (Table 2; Fig. 5, marked withasterisks) are included. For both time periods the distributionsindicate little MAB (July 19e26, 2006: 0.01 (RMSE ¼ 0.12, availabledata: 1623); February 16e26, 2006: �0.03 (RMSE ¼ 0.19, availabledata: 541)). In February the model results tend to be biased lowcompared to the observation, which may indicate too strong wetdeposition of aerosol particles in the model. When excluding thosedata measured at stations that were used for model initializationthe MAB only slightly improves (MAB ¼ 0.0, RMSE ¼ 0.13, availabledata: 910) for the summer period. For the winter period a newMABof �0.06 (RMSE ¼ 0.22, available data: 225) was determined. Thisindicates that the agreement between simulation and observationat stations used for model initialization is not necessarily betterthan for other stations.

Simulations with weighting factors of 1.0 at all four lateralmodel boundaries (MAB ¼ 0.02, RMSE ¼ 0.13, during July 19e26,2006; MAB ¼ �0.01, RMSE ¼ 0.2 during February 16e26, 2006),only show low differences compared to simulations with individ-ually calculated weighting factors.

6.3. Vertical backscatter coefficient

Lidar measurements performed at EARLINET stations (Fig. 7)and on board the satellite CALIPSO (Fig. 8) are compared to thesimulation results.

The measurement time ranges at the EARLINET stations differfor each station and can vary from several minutes to hours. To

ormed for July 21e26, 2006 and February 18e26, 2007, respectively.

Fig. 4. Average horizontal distribution of relative AOD difference (Result 1 � Result 2/Result 2) for the two time periods July 21e26, 2006 and February 18e26, 2007. Result 1: AODof simulation with individual weighting factors AOD, Result 2: AOD of simulation with weighting factors of 0. At each lateral model boundary.

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532526

compare to observation profiles an average profile for each simu-lation case based on hourly model results was calculated (Fig. 7).The vertical distribution of all simulation results for both timeperiods shows a successful representation of magnitude and shapeof the observed profiles. The representation of typical layers withstrong vertical gradients is well represented by the regional model.

For the summer period, several lidar measurements were per-formed on July 24, 2006. The vertical gradients are successfullyrepresented by the simulations. In contrast to July, the profilesobserved during February do not show sharp gradients but rathera continuously decrease of the backscatter coefficients withincreasing height. Only at the station in Leipzig (Germany, February22, 2007) the backscatter profile is not well simulated betweensurface and 1 km.

Space-based lidar measurements performed on board the CAL-IPSO satellite were also used to evaluate simulated backscatterprofiles. Because of the 5 km horizontal resolution of CALIOPbackscatter profiles, it can be that more than one measurementprofile was situated in one model grid cell. Therefore, an averageCALIOP backscatter profile was determined. The absolute differencebetween simulated and observed backscatter coefficients for each500 m layer was determined. In Fig. 8 MAB and median absolutebias (MdAB) as well as RMSE are shown. For both time periods,MAB and MdAB represent an underestimation of observed back-scatter coefficients for all altitude levels by the model simulation.The MdAB is often closer to zero than the MAB (except between 6.0and 7.0 km (July 19e26, 2006) and 6.0e7.5 km (February 16e26,2007)), whereas the range of the MdAB differs between1.6 � 10�8 m�1 sr�1 (RMSE ¼ 2.5 � 10�8 m�1 sr�1) and �9.5 � 10�7

m�1 sr�1 (RMSE ¼ 8.3 � 10�7 m�1 sr�1) (July 19e26, 2006) andbetween �1.2 � 10�6 m�1 sr�1 (RMSE ¼ 5.0 � 10�6 m�1 sr�1)and �3.4 � 10�7 m�1 sr�1 (RMSE ¼ 9.8 � 10�7 m�1 sr�1) (February16e26, 2007), respectively. The magnitude of the RMSE indicatesa large variability between observed and simulated backscattercoefficients, especially near the surface. It is likely that the influenceof the surface is not completely corrected for CALIOP backscatterprofiles and therefore significant backscatter signals may beobserved near the surface. Certainly a misinterpretation of thehygroscopic growth resulting in too low or too high values ofbackscatter coefficients in the model is also possible. Regarding thesummer period the magnitudes of both MdAB and MAB increasewith height in contrast to the winter period. As mentioned before,Saharan dust was transported into the southern regions of themodel domain, which was not simulated by the model. Theincrease of MAB and MdAB at higher altitudes can therefore becaused by missing Saharan dust in the model.

7. Additional model simulations

To investigate the role of the aerosol distribution at the lateralmodel boundaries, two further approaches e a so-called Standardprofile and Individual lidar profiles e to describe the verticaldistribution of chemical compounds at the lateral model bound-aries were tested and also compared to observation data.

Standard profile

In the Standard profile setup, model simulations with COSMO-MUSCAT are performed with a fixed profile for PPM2.5 and PPM10at the lateral model boundaries. This has also been the default setupin previous simulations with this model. The structure of this fixedprofile does not vary with season or region. This Standard profile isdescribed by a constant concentration (the maximum value of thechemical substance) from ground to 700 m height above ground.Between 700 and 900 m above ground the concentration decreasesto the minimum value and remains constant again from 900 to8000 m (model top) above ground.

Individual profile

Individual lidar profiles measured one day before the start of themodel simulations were used to describe the vertical distribution ofPPM2.5 and PPM10 at the lateral model boundaries.

During July 19e26, 2006 lidar profiles, measured at EARLINETstations on July 17 (because of the 24 h forerun of COSMO) wereused. Measurements at four stations were available: Thessaloniki(Greece, 13:28e14:01 UTC; 355 and 532 nm), Belsk (Poland,12:06e12:15 UTC and 18:27e18:33 UTC; 532 nm), Minsk (Belarus,16:06e16:59 UTC; 355, 532 and 1064 nm) and L’Aquila (Italy,20:24e20:53 UTC; 351 nm). Based on these measured profilesa composite profile was estimated, which describes the verticaldistribution of the atmospheric substances representing theatmospheric conditions of the time period when the model simu-lations were performed. For this case, the maximum concentrationranges here from bottom to 2000 m, concentrations decreasebetween 2000 and 4000 m above ground and the lowest concen-tration occurs above 4000 m.

The model simulations for the winter case February 16e26,2007 were started on February 15, for model initialization lidarmeasurements from February 14 were used. Only one EARLINETstation performed measurements on this day, therefore lidarprofiles measured on February 15 were used additionally. Lidarmeasurements were available for Potenza (Italy, February 14

Fig. 5. Time series of observed (black crosses) and simulated AOD (black line) at several AERONET stations during July 19e26, 2006 and February 16e26, 2007. Asterisks indicatethose stations that were also used for model initialization.

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 527

Fig. 6. Distribution of absolute difference between simulated and observed AOD for July 21e26, 2006 and February 18e26, 2007, respectively. The black bar represents theestimated error of w0.01e0.02 (Eck et al., 1999).

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532528

00:50e12:00 UTC; 355, 532 and 1064 nm), Ispra (Italy, February 1512:12e23:47 UTC, 532 nm), Saint Michel (France, February 1518:30e20:00 UTC, 532 nm) and Palaiseau (France, February 1510:00e16:00 UTC, 532 nm). The concentration profile wasdescribed at the model boundaries as follows: Maximum concen-tration constant from bottom to 600 m above ground, decreasingconcentration from 600 m to 2000 m and minimum concentrationfrom 2000 m to 8000 m.

7.1. Evaluation of three model initializations

7.1.1. PM2.5

Results of simulations performed with the Standard profile andthe Individual profiles are very similar to those performed with the

Fig. 7. Comparison between observed vertical backscatter coefficient (observation wavelengfor July 2006 (upper panel) and February 2007 (lower panel) are shown (continuous line:picture station, wavelength and measurement time range are written.

Climatological profile. Fig. 9 represents the minimum, maximumand median relative bias (MdRB) between observed and simulatedPM2.5 at all available EMEP stations (July 2006: 175 data available;February 2007: 225 data available).

Daily variability occur during both time periods but whereas theMdRB between the individual model cases during July 19e26, 2006are remarkable, differences during February 16e26, 2007 remainsmall (Table 3). During the summer period theMdRB is significantlylarger when using the Standard profile and smaller when using theIndividual profile at the lateral model boundaries. Nearly the samebehavior is found for the winter period, differences among theindividual simulation results are much more similar to eachother compared to the summer period cases. But the range ofrelative differences (from minimum to maximum) is smaller for

th depends on the station) and simulated backscatter profiles (550 nm) at four stationsObserved backscatter profile, dashed line: Simulated backscatter profile). Above each

Fig. 9. MdRB (represented by circles and triangles) based on daily averages between observation and simulation PM2.5. Minimum and maximum relative differences are presentedfor each model simulation by the gray bars for summer and winter period.

Fig. 8. MAB (red line), MdAB (blue line) and RMSE (green line) of backscatter coefficients for each 500 m level. The numbers at the right axis of each graph represents the number ofavailable values for each height level for July 19e26, 2006 and February 16e26, 2007. (For interpretation of the references to color in this figure legend, the reader is referred to theweb version of this article.)

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 529

simulations with the Standard profile for the summer as well forthe winter period. Again, during February 2007 the discrepanciesbetween the individual simulation results regarding the minimumto maximum range are very similar in contrast to July 2006.

7.1.2. Aerosol optical depthSimulations performed with the two additional cases give very

similar results. The values of the MAB between simulated andobserved AOD for summer and winter period is shown in Table 4.The general MAB for the individual simulations during the summerperiod is positive, whereas during the winter period a negative biasis found. Largest discrepancies (MAB ¼ 0.06, July 21e26, 2006) aredetermined for simulations with Individual profiles at the lateralmodel boundaries. During February 18e26, 2007 all MAB are equal.

Table 3Daily MdRB [%] determined for all three simulation cases and both time periods.

Day July 2006

Climatological profile Standard profile Individual profile

16 e e e

17 e e e

18 e e e

19 �50 �59 �4120 �55 �62 �4121 �56 �62 �4222 �54 �57 �4023 �47 �51 �3924 �46 �50 �4025 �49 �50 �3726 �30 �33 �24

Additional simulations with weighting factors of 1.0 at each lateralmodel boundary were also carried out with the Standard profileand Individual profile, respectively. On the one hand no change(Standard profile) or improved results are achieved by usingweighting factors for summertime simulations. But on the otherhand the quality of model results decreased slightly when usingweighting factors for the winter period for all simulation cases.Again, these small differences can be explained by the specificweather situations.

7.1.3. Vertical backscatter coefficientIn Fig. 10 absolute differences between observed (only ground-

based lidar stations) and simulated backscatter coefficients foreach 500 m height level are shown.

February 2007

Climatological profile Standard profile Individual profile

�16 �19 �13�30 �33 �30�41 �45 �40�49 �49 �49�32 �33 �32�49 �49 �49�29 �32 �29�26 �30 �24�49 �55 �49�38 �44 �37�22 �27 �19

Table 4MAB of simulated and calculated AOD during July, 21e26, 2006 and (available data:1623) and February 18e26, 2007 (available data: 541).

July 21e26,2006

February 18e26,2007

Climatological profile 0.01 �0.02Climatological profile (weight: 1.0) 0.02 �0.01Standard profile 0.00 �0.02Standard profile (weight: 1.0) 0.00 �0.01Individual profile 0.03 �0.02Individual profile (weight: 1.0) 0.06 �0.01

Table 5MAB and MdAB as well RMSE between simulated and observed (CALIOP) back-scatter values for summer and winter period for all three simulation cases. Units:Altitude [km], Model mean, Model median, MAB, MdAB, RMSE [�10�6 m�1 sr�1].

Altitude July 19e26, 2006 February 16e26, 2007

0.0e2.5 2.5e8.0 0.0e2.5 2.5e8.0

Climatological profile

Model mean 0.8e1.6 0.1e0.7 0.3e1.2 0.1e0.2Model median 0.5e1.3 0.1e0.5 0.2e0.9 0.1e0.1MAB �0.5 to �0.2 �0.9 to �0.5 �2.3 to �1.0 �1.7 to �0.5MdAB �0.4 to 0.02 �1.0 to �0.4 �1.2 to �0.5 �0.8 to �0.3RMSE 1.5e2.5 7.6e1.4 2.6e5.0 6.0e3.7

Standard profile

Model mean 0.8e1.5 0.2e0.6 0.3e1.2 0.1e0.2Model median 0.5e1.3 0.2e0.4 0.2e0.9 0.1e0.1MAB �0.5 to �0.3 �0.9 to �0.6 �2.3 to �1.1 �1.7 to �5.4MdAB �0.4 to �0.1 �0.9 to �0.4 �1.2 to �0.6 �0.8 to �0.3RMSE 1.5e2.5 0.8e1.4 2.6e5.0 0.6e3.7

Individual profile

Model mean 1.0e1.2 0.1e0.8 0.3e1.3 0.1e0.2Model median 0.7e1.5 0.1e0.6 0.2e0.9 0.06e0.1MAB �0.3 to 0.0 �0.9 to �0.4 �2.2 to �1.0 �1.7 to �0.5

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532530

During July 2006, significant vertical gradients were detected bythe lidar, which were also represented by the model simulations(see Fig. 7). Therefore, the range between minimum and maximumabsolute difference is very variable with the height (significantbetween 500 m and 1 km, and 1.5 km and 2.0 km), whereas duringFebruary 2007 a trendwith a larger differences near the ground andsmaller differences at higher altitudes is found. The MdAB differsbetween�7.2�10�7m�1 sr�1 (1.0e1.5 km) and 5.0�10�7m�1 sr�1

(7.0e7.5 km) for the summer and between �2.2 � 10�8 m�1 sr�1

(4.5e5.0 km) and�7.0� 10�6 m�1 sr�1 (0.0e0.5 km) for the winter

Fig. 10. Maximum, minimum and MdAB between individual simulation results andobservation (EARLINET) of backscatter coefficients for each 500 m level for July 19e26,2006 (71 profiles; upper panel) and February 16e26, 2007 (34 profiles; lower panel).Solid line and plus sign: Climatological profile, dotted line and cross: Standard profile,dashed line and square: Individual profile. Numbers located on the right axis representthe number of available data for this altitude range.

MdAB �0.22 to 0.2 �1.0 to �0.3 �1.1 to �0.5 �0.8 to �0.3RMSE 1.4e2.5 0.8e1.4 2.5e5.0 0.6e3.7

period when simulations were performed with the Climatologicalprofile. The minimum, maximum andMdAB is very similar for bothtime periods and altitude ranges between the individual simulationcases, especially during February 2007. The magnitude of thedifference values is often in the order of the observed backscattercoefficients. MRB discrepancies of �26% to �36% are determinedfrom 0.0 to 2.5 km (July 2006) and also from 1.5 to 3.0 km (�12%to �49%, February 2007).

The differences between space-based (CALIOP) and simulatedbackscatter coefficients of the individual simulation cases remainalso small (Table 5). Similar MAB and MdAB, respectively are yiel-ded for all altitude ranges and both time periods, especially thewinter period. In general, all three simulation cases representa slight underestimation of CALIPSO backscatter values by themodel during July 2006 and February 2007. The magnitude of MABand MdAB is often in the same order of simulated backscattercoefficients.

8. Conclusions

Regional transport simulations for two different time periods(July 19e26, 2006 and February 16e26, 2007)were performedwithCOSMO-MUSCAT. Climatological profiles were used to describe thevertical distribution of PPM2.5 and PPM10 at the lateral modelboundaries. Additionally, measurements of AOD (at EuropeanAERONET stations located next to the lateral model boundaries)were used to scale the results to obtain comparable and realisticAODs.

Simulated and measured (EMEP) particle concentrations at thesurface level agree to some extent. For both time periods therepresentation of magnitude and the day-to-day variability isdifficult, while the modeled concentrations are underestimated atmany stations. Transport processes during July 21e26, 2006 andFebruary 18e26, 2007 are similar, but caused by different meteo-rological conditions. During both time periods lower AOD valueswere simulated over the oceans, whereas larger values weresimulated within Europe and especially in the region of Milano,Romania, Croatia, Hungary, Bosnia and Herzegovina, highlighting

J. Meier et al. / Atmospheric Environment 47 (2012) 519e532 531

the importance of local aerosol sources. Comparisons of AOD(AERONET) did also show a low bias and good representation of theday-to-day variability. Vertical backscatter profiles from modelsimulations compared well with ground- (EARLINET) and space-based (CALIPSO) lidar measurements regarding magnitude andshape for both time periods. Sharp vertical gradients, which wereoften measured during July 2006, were also well reproduced.Comparing simulated aerosol distributions with a range of availableobservations e particle concentration near the surface, opticalthickness and vertical distribution e is helpful to evaluate thedifferent aspects of the model and highlight possible shortcomings.In particular evaluating the model results with ground- or space-based lidar measurements is useful for characterizing the modelability to reproduce aerosol vertical distributions.

Lidar profiles can also be used to describe vertical profiles ofaerosol compounds at the lateral model boundaries to includerealistic initial conditions into the model. However, in the cases thatwere investigated in this study other descriptions of the verticalaerosol distribution at the lateral model boundary yielded similarresults. This indicates that for this large model domain the weathersituation described by these cases the aerosol transport from thelateral boundaries into the model domain is of less importance. Anexception is the transport of Saharan dust to southern Europeleading to increased optical thicknesses and particle concentrationsin this region. The neglect of Saharan dust transport in this modelstudy is reflected by the discrepancies in southern European stationsbetween model results and measurements in certain time periods.For the cases shown here, the usage of sun photometer AOD obser-vations to adjust the concentration profiles at the lateral modelboundaries improved the results compared to simulations withoutthis adjustment. The calculation of individual weighting factors,which also characterizes the situation at the lateral model bound-aries can be useful for simulations with strong aerosol transport andlow rain and snowfall events. Smaller model domains withincontinental regions are also expected to bemore strongly influencedby lateral aerosol transport compared to the cases studied here.

Apart from prescribing aerosol distributions at the lateral modelboundaries with fixed profiles or from measurements such aerosoldistributions can also be extracted from large scale (global) aerosoltransport models or aerosol distributions computed with GeneralCirculation Models or chemistry transport models (CTMs). Thatmethod has the advantage that actual atmospheric conditions areconsidered and in contrast to use of data such aerosol fields do notcontain gaps. However the quality of such aerosol fields alsodepends on the quality of the aerosol emissions and transport inthe global models. A comprehensive test of the differentmethods todescribe aerosol distribution at the lateral model boundariesincluding the use of modeled global aerosol fields with differentweather situations and model domains will be a useful exercise toobtain information about the influence of model initialization atthe lateral boundaries.

Acknowledgments

We gratefully acknowledge to the EARLINET community and theAtmospheric Sciences Data Center (ASDC) for providing us thevertical backscatter profiles of ground- and space-based (CALIPSOdata (Lidar Level 2, Version Releases 3.01)) lidar measurements.Data of chemical surfacemeasurements and AODwithin Europe arekindly provided by EMEP and AERONET.

Appendix A

Statistical parameters within the manuscript are determined asfollows. Mean absolute bias:

MAB ¼ 1N

XNi¼1

ðSi � OiÞ (A.1)

Mean relative bias:

MRB ¼ 1N

XNi¼1

�Si � Oi

Oi

�$100% (A.2)

Median absolute bias:

MdAB ¼

8>>>><>>>>:

ðS� OÞNþ12

ðfor odd NÞ12

�ðS� OÞN

2þ ðS� OÞN

2þ1

�ðfor even NÞ (A.3)

Root mean square error:

RMSE ¼"1N

XNi¼1

ðSi � OiÞ2#1

2

(A.4)

with N as number of all individual values i, Si as simulated and Oi asobserved result.

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