De Mej a., 2015, The Effect of SRTM and Corine Land Cover Data on Calculated Gas and PM10...

8/10/2019 De Mej a., 2015, The Effect of SRTM and Corine Land Cover Data on Calculated Gas and PM10 Concentrations in WRF-Chem

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The effect of SRTM and Corine Land Cover data on calculated gas andPM10 concentrations in WRF-Chem

A. De Meij a , * , E. Bossioli b , C. Penard a , J.F. Vinuesa a , I. Price aa Noveltis, Sustainable Development, 153, Rue du Lac, F-31670 Labege, Franceb National and Kapodistrian University of Athens, Department of Physics, Division of Environmental Physics and Meteorology, Greece

h i g h l i g h t s

Differences are found in the land cover classes between Corine and USGS data sets. T2 and winds are different between WRF-Chem with SRTM Corine LC and with USGS. SH, LH and PBLs are different between WRF-Chem with SRTM Corine LC and USGS. WRF-Chem with SRTM Corine LC calculates higher gas concentrations than with USGS. WRF-Chem with SRTM Corine LC calculates higher PM10 concentrations than with USGS.

a r t i c l e i n f o

Article history:Received 29 July 2014Received in revised form12 November 2014Accepted 14 November 2014Available online 15 November 2014

Keywords:WRF-ChemCorine Land CoverSRTMUSGS Land Cover

a b s t r a c t

The goal of this study is to investigate the impact of the high resolution Shuttle Radar TopographyMission (SRTM) 90 m 90 m topography data, together with the 100 m 100 m resolution Corine LandCover 2006 on the simulated gas and particulate matter (PM10) concentrations by WRF-Chem. Wefocused our analysis on the well-known highly urbanized region of the Po Valley. Large differences arefound in the geographical distribution of the land cover classes between Corine Land Cover and 30 arcseconds USGS. The simulation with the SRTM and Corine Land Cover increases modelled temperature at2 m and reduces wind speeds due to more friction at the surface induced by the Corine Land Cover.Latent and sensible heat uxes show large differences between the two simulations and the relatedboundary layer development and depth. The simulation with the SRTM and Corine Land Cover favoursthe precipitation amount over a large of part the Alps and follows the pattern of the difference intopography between the two topography data sets. In term of air quality indicators, impacts are also largeand geographical dependent. Monthly average of CO, NO and SO 2 concentrations over a large part of thePo Valley are higher when using Corine Land Cover, up to ~20, ~50 and ~55%, respectively. With respectto PM10, the impacts are also geographical dependent. Over the Po valley area, calculated PM10 con-centrations are in general higher using Corine Land Cover (up to 6.7 ug/m 3 [~26%] westerly of Milan)while differences are smaller over the Alps (~0.25ug/m 3 [~20%]). Although the scope of this work is not toevaluate the model performance in calculated meteorological parameters and gas and PM10 concen-trations, calculated values by the simulation with SRTM and Corine Land Cover show a better agreementwith the observations than the simulation with the USGS topography and land cover data sets. Aquantitative comparison between modelled and observed monthly average PM10 concentrations showsthat both simulations underestimate the observed PM10 concentrations by a factor ~4. The agreement is

much better during episodes for the simulation with the SRTM and Corine Land Cover. For CO, SO 2 andNO x, the modelled monthly mean concentrations are similar for the two simulations. Larger differencesare found during some episodes and regions with the SRTM and Corine LC simulation being in betteragreement with the observations.

2014 Elsevier Ltd. All rights reserved.

* Corresponding author.E-mail address: [email protected] (A. De Meij).

Contents lists available at ScienceDirect

Atmospheric Environment

j o u rn a l h o mep ag e : www.e l sev i e r. com/ l o ca t e / a t mo senv

http://dx.doi.org/10.1016/j.atmosenv.2014.11.033

1352-2310/

2014 Elsevier Ltd. All rights reserved.

Atmospheric Environment 101 (2015) 177 e 193
mailto:[email protected]://www.sciencedirect.com/science/journal/13522310http://www.elsevier.com/locate/atmosenvhttp://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://dx.doi.org/10.1016/j.atmosenv.2014.11.033http://www.elsevier.com/locate/atmosenvhttp://www.sciencedirect.com/science/journal/13522310http://crossmark.crossref.org/dialog/?doi=10.1016/j.atmosenv.2014.11.033&domain=pdfmailto:[email protected]


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1. Introduction

A common problem in air quality modelling is the underesti-mation of simulated particulate matter (PM) concentrations by airchemistry transport models (ACTMs). Many model studies ( Tsyro,2003; De Meij et al., 2007; Vautard et al., 2007; Stern et al., 2008;De Meij et al., 2008; De Meij et al., 2009; Vautard et al., 2009 ),and coordinated modelling activities such as Citydelta [ http://aqm. jrc.ec.europa.eu/citydelta/ ], AEROCOM [http://aerocom.met.no/aerocomhome.html ] and Eurodelta [ http://aqm.jrc.ec.europa.eu/eurodelta/ ] showed that ACTMs in general underestimateobserved PM concentrations over Europe. The City Delta exercise(http://aqm.jrc.ec.europa.eu/citydelta/ , Cuvelier et al., 2006; Thuniset al., 2007; Vautard et al., 2006 ) showed that simulated PM con-centrations are underestimated by the ACTMs for Milan (Italy),especially for winter time episodes. There are several reasons forthe underestimation of PM10 e.g. unaccounted sources of emis-sions, vertical and temporal distribution of the emissions and thatthe Po Valley is characterized by very lowwind speeds and frequentweak circulation conditions. These stagnant meteorological con-ditions are dif cult to simulate by prognostic and diagnosticmodels over complex areas ( Dosio et al., 2002; Minguzzi et al.,2005; Carvalho et al., 2006; Stern et al., 2008; De Meij et al.,2009; Ritter et al., 2013 ), which contribute to the underestima-tion of simulated PM concentrations.

Aerosol formation processes are known to be non-lineardependent on gas concentrations ( West et al., 1998 ) and meteoro-logical variables ( Haywood and Ramaswamy, 1998 ). Recent studieshave shown the impact of meteorological parameters on gas andaerosol calculated concentrations by altering, for example theplanetary boundary layer scheme, micro-physics scheme, landsurface physic schemes ( De Meij et al., 2009; Appel et al., 2010;Zabkar et al., 2013; Forkel et al., 2014 ).

A recent study by De Meij and Vinuesa (2014) showed theimpact of the high resolution (100 100 m) Corine Land Cover oncalculated meteorological parameters (wind speed, temperature

and precipitation) during a winter and a summer period, bycomparing with a simulation using the standard 30-arc seconds(~1 1 km) USGS Land Cover and observations. They found largedifferences in the fraction of urban built-up area between theCorine Land Cover and USGS Land Cover for the Lombardi province(Italy), which impacted the calculated meteorological parameters.The simulation with the Corine Land Cover resulted in lower windspeeds and showed a better agreement with the observations. Theaccuracy of land-use classi cations in meteorological modellingaffects some of the meteorological parameters. Urban built-upareas are more likely to trap solar radiation and reduce windspeeds than open rural areas, which may impact the temperatures,buoyancy and wind directions and wind speeds. A good estimate of meteorological variables such as wind speed is therefore crucial for

calculating gas and aerosol impacts on air quality and climatechange, and evaluating coherent reduction strategies.

To our knowledge the impact of topography and Corine LandCover in WRF-Chem on calculated aerosol (precursor) concentra-tions have not yet been reported.

In this study, we investigate the impact of the high resolutionShuttle Radar Topography Mission (SRTM) 90 90 m topographydata ( Farr et al., 2007; Jarvis et al., 2008 ) together with the100 100 m resolution Corine Land Cover 2006 ( Heymann et al.,1994; Bttner et al., 1998, 2002 ) on the simulated gas and partic-ulate matter (PM10) concentrations by WRF-Chem. In order toinvestigate the impact of the SRTM and Corine Land Cover wecompare the simulated concentrations with the results of the WRF-Chem simulation using the standard 30-arc seconds United States

Geological Survey (USGS) Land Cover and topography ( Anderson

et al., 1976 ) and with observations of the ARPA network. Wefocus on the southern region of the Alps, more speci cally theLombardy region, because (i) De Meij and Vinuesa (2014) used thisarea to study the impact of Corine Land Cover on meteorologicalparameters, (ii) this area showed to be problematic in previousaerosol modelling studies (iii) the Po valley is one of the mostpolluted, industrialized and densely populated areas in Europe and(iv) this area shows a large diversity in land cover (LC). It containsseveral big lakes, mountains and the region is highly populated(ISTAT, 2012 ). More details about WRF-Chem and Corine LandCover are given in Section 2.

2. Methodology

The WRF model is used over a part of the Italian/Swiss Alps andthe Po Valley area (northern Italy) to study the impact of highresolution SRTM topography and Corine Land Cover on the simu-lated gas and PM10 concentrations. More details regarding WRF-Chem are given in Section 2.1. WRF-Chem operates on the10 10 km and 2 2 km resolution. Fig. 1 presents thegeographical position of the model grid domains The10 km 10 km domain (approximately 1100 km 950 km centredat 8.603 longitude and 45.916 latitude) covers the Eastern part of France, southern part of Germany, Switzerland, a large part of Italyincluding a part of the Adriatic Sea and Mediterranean Sea. Domain2 (approximately 300 km 200 km) covers a part of the Po Valleyin North Italy and the Southern part of Switzerland.

The two simulations were performed with no nudging to theobservations of the meteorological stations. The rst simulationuses the SRTM and Corine Land Cover data for January 2010. Thesecond simulation uses the standard USGS 30-arc seconds land-usedata (~1 1 km) and topography data for the same period. Thesimulation with SRTM and Corine Land Cover is further denoted asWRF_CLC and the simulation with 30-arc seconds is furtherdenoted as WRF_USGS. For the simulations, a spin-up time of four

days is applied in order to initialize the model. WRF uses meteo-rological initial conditions and lateral boundary conditions from 6 hanalyses from the National Center for Environmental Protection(NCEP; Kalnay et al., 1996 ), and the Climate Forecast SystemReanalysis (CFSR; Saha et al., 2010 ).

We start our study by evaluating the meteorological parameters(temperature, wind speed and precipitation) calculated byWRF_CLC and WRF_USGS. For more details we refer to De Meij andVinuesa (2014) . Then we evaluate the calculated gas concentrationsof CO, SO2 and NO x and PM10 concentrations of both simulations.Furthermore, we analyse the monthly mean calculated values of the meteorological parameters and chemical species, together witha more detailed evaluation of some selected episodes. For theevaluation of the meteorological parameters and gas and PM10

concentrations we use observations of the Agenzia Regionale per laProtezione dell' Ambiente (ARPA) network ( www.arpalombardia.it ). An overview of the stations used and their geographical loca-tion is given in Table 1 a. Through this website the following pa-rameters can be downloaded: precipitation, temperature,atmospheric pressure, wind speed and wind direction, relativehumidity, global irradiation and net irradiation. The statisticalanalysis of the simulated meteorological values and gas concen-trations in this work is based on hourly values. For PM10 thatanalysis is based on daily mean concentrations. Depending on theamount of observations available, the number of observed-modelled pairs differ from one station to the other.

The SRTM ew aboard the Space Shuttle Endeavour, which ob-tained terrain elevation data during an eleven day mission in

February of 2000 to generate the high-resolution (~90 m) digital

A. De Meij et al. / Atmospheric Environment 101 (2015) 177 e 193178
http://aqm.jrc.ec.europa.eu/citydelta/http://aqm.jrc.ec.europa.eu/citydelta/http://aerocom.met.no/aerocomhome.htmlhttp://aerocom.met.no/aerocomhome.htmlhttp://aqm.jrc.ec.europa.eu/eurodelta/http://aqm.jrc.ec.europa.eu/eurodelta/http://aqm.jrc.ec.europa.eu/citydelta/http://www.arpalombardia.it/http://www.arpalombardia.it/http://-/?-http://-/?-http://www.arpalombardia.it/http://www.arpalombardia.it/http://aqm.jrc.ec.europa.eu/citydelta/http://aqm.jrc.ec.europa.eu/eurodelta/http://aqm.jrc.ec.europa.eu/eurodelta/http://aerocom.met.no/aerocomhome.htmlhttp://aerocom.met.no/aerocomhome.htmlhttp://aqm.jrc.ec.europa.eu/citydelta/http://aqm.jrc.ec.europa.eu/citydelta/


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topographic database of Earth. High resolution SRTM data can befound at: http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp .

The USGS land-use dataset was created in 1993, since then ur-ban areas have changed dramatically for some regions in Europe.The Corine Land Cover is a European Commission program, startedin 1985 by the European Commission DG Environment, intended toprovide consistent, localized geographical information on the land

cover of the Member States of the European Community. The Cor-ine Land Cover is recognised by decision-makers as an essentialreference dataset for spatial and territorial analyses on differentterritorial levels ( Bttner et al., 2002 ). To make the Corine LandCover categories (44) compatible with WRF Pre-processing System(WPS) the Corine Land Cover is reclassi ed to the USGS category(24 land-use categories, Pineda et al., 2004 ). The Corine Land Coverdataset is projected on the European Terrestrial Reference System1989 (ETRS89) Lambert Azimuthal Equal Area (LAEA), which is notcompatible with the WRF system. Therefore the Corine Land Coveris re-projected to the World Geodetic coordinate System 1984(WGS84) Arnold et al. (2010) . The Corine Land Cover dataset can befound on: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2 .

2.1. Description of the model

The Chemistry Weather Research Forecasting model (WRF-Chem) version 3.6.1 is used in this study ( Grell et al., 2005; Fastet al., 2006 ). The WRF-ARW system is a non-hydrostatic model(with a hydrostatic option) using terrain-following vertical coor-dinate based on hydrostatic pressure. The terrestrial data sets forthe WRF model are built using the NCEP geographical data. Theseconsist of global data sets for soil categories, land-use, terrainheight, annual mean deep soil temperature, monthly vegetationfraction, monthly albedo, maximum snow albedo and slopes. Theresolution used for implementing land-use in the proposed study isbased on (i) the Corine 2006 Land Cover data set (100 m 100 m)

and (ii) the 30-arc seconds United States Geological Survey (USGS)

database. WRF-Chem uses the USGS 24-category land-use data,which are available for different horizontal resolutions (10 0, 50, 20,30 00; 00 denotes arc seconds and 0denotes arc minutes). The highesthorizontal resolution used in this study corresponds to~1 km 1 km. The horizontal resolution is set by the user in thepre-processing step in WPS.

The gas phase chemistry is calculated using the Goddard Global

Ozone Chemistry Aerosol Radiation and Transport model (GOCART,Chin et al., 2000 ) coupled with the Regional Atmospheric Chem-istry Mechanism ( Stockwell et al., 1997 ) Kinetic Pre-Processor(RACM-KPP) mechanism ( Damian et al., 2002; Sandu et al., 2003;Sandu and Sander, 2006 ). GOCART applies the bulk-schemeapproach for the distribution of the aerosols. This means that theaerosol size is kept constant, only the aerosol mass is calculated.GOCART does not account for secondary organic aerosol and doesnot include the interaction with radiation (direct effect) and cloudprocesses (indirect effect). Aerosol species in GOCART are PM2.5,PM10, sulphate, black carbon, organic carbon, sea salt, dimethylsulphide (DMS), methanesulfonic acid (MSA) and natural dust(DMS and MSA are not considered in this work). The cloudmicrophysics is calculated using the WSM 5-class scheme with

vapour, rain, snow, cloud ice and cloud water. The model uses theNoah land surface model scheme( Chen and Dudhia, 2001 ) with soiltemperature and moisture in four layers, fractional snow cover andfrozen soil physics and provides heat and moisture uxes for thePBL. The Yonsei University PBL scheme is used to set up the model(Hong et al., 2006 ) and the radiation is calculated by the RRTMscheme ( Mlawer et al.,1997 ). The short wave radiation is simulatedby the Goddard scheme ( Chou and Suarez, 1999 , NASA Tech Memo).For domain 1 (10 10 km) the convective New Grell scheme byGrell and Devenyi (2002) is activated. The cumulus scheme is notactivated for domain 2 (2 2 km). It is recommended to activatethe cumulus scheme on coarser grids e.g. > 10 10 km, when it isnot resolved by the model ( http://www.dtcenter.org/wrf-nmm/users/docs/user_guide/V3/users_guide_nmm_chap5.pdf ). The ver-

tical discretization of WRF-Chem involves 37 levels up to about

Fig. 1. Google Earth view of the two domains (D1 10 km 10 km, D2 2 km 2 km).

A. De Meij et al. / Atmospheric Environment 101 (2015) 177 e 193 179
http://srtm.csi.cgiar.org/SELECTION/inputCoord.asphttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2http://www.dtcenter.org/wrf-nmm/users/docs/user_guide/V3/users_guide_nmm_chap5.pdfhttp://www.dtcenter.org/wrf-nmm/users/docs/user_guide/V3/users_guide_nmm_chap5.pdfhttp://www.dtcenter.org/wrf-nmm/users/docs/user_guide/V3/users_guide_nmm_chap5.pdfhttp://www.dtcenter.org/wrf-nmm/users/docs/user_guide/V3/users_guide_nmm_chap5.pdfhttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp


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18.5 km. The most relevant settings of WRF-Chem for domain 2 aregiven in Table 1 b.

WRFoperates on two resolutions (10 10 km[D1] and 2 2 km[D2]) following the NDOWN methodology (one way nesting). Thismethodology involves using the coarse grid model simulation asinput for the ner model. The initial and lateral boundary condi-tions for the ner grid simulation are used from the coarse gridsimulation (ARW Users's Guide, http://www.mmm.ucar.edu/wrf/users/docs/user_guide/ARWUsersGuide.pdf ). In this work D2 uses

the lateral and boundary conditions from D1.To investigate the impact of Corine Land Cover on calculated gasand PM10 concentrations in WRF-Chem we run the D2 simulationtwice; once with the Corine Land Cover and once with the 30 arcseconds USGS Land Cover.

The anthropogenic emissions are constructed for each domainwith the anthro_emis tool (available at http://www.acd.ucar.edu/wrf-chem/download.shtml ). The emissions used in the study arefrom the EC-JCR/PBL (2010) Emissions Database for Global Atmo-spheric Research (EDGAR) inventory version 4.2 for the year 2008,source: European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). EmissionDatabase for Global Atmospheric Research (EDGAR), releaseversion 4.2, http://edgar.jrc.ec.europa.eu , 2011. EDGAR v4.2 con-tains the following species CO

2, CH

4, N

2O, HFCs, PFCs, SF6, CFCs,

HCFCs, CO, NO x, NMVOC, SO2, NH3 , BC, OC, PM10 and PM2.5 on0.10 0.10 (Olivier et al., 2001; Janssens-Maenhout et al., 2012 ).

The emissions depend on type of activities, seasonal andgeographical distribution of the emissions and are based on infor-mation including urban/rural population density, animal density,power and industrial plants, oil and gas elds, shipping and aircraftroutes, coal mines, road network, rice elds, crop and grass lands.Sources of abrasive emissions in road transport and construction

are not included in the emission inventory, which are two impor-tant diffusive emission sources for PM10. Also absent are particu-late matter emissions by wood burning for residential heatingpurposes in northern Italy, and also biogenic emissions and sec-ondary organic aerosol (SOA) formation. This is deemed acceptablebecause the objective of this paper is to investigate the impact of the high resolution SRTM topography and Corine Land Cover oncalculated air pollutants, and not to evaluate the model perfor-mance in air pollutant concentrations by comparison withobservations.

For our study we use PM10, PM2.5, SO 2, NO x (as NO), NH 3, CO,CO2, OC, BC and CH4 total emissions. The emissions are distributedin the lowest model layer, which is around 27 m. In De Meij et al.(2009) two different emission inventories were used (EMEP and

AEROCOM) to study the impact of the vertical and temporal dis-tribution of the emissions, it was found that most of the anthro-pogenic emissions are distributed in the rst model layer.

2.2. Differences in land-use between Corine Land Cover and USGS Land Cover

Toillustrate the differences in the land cover categories betweenthe Corine Land Cover and the 30s USGS data bases we show inFig. 2 the distribution of the 24 land cover classes.

A big fraction of the Po Valley in USGS is classi ed as Drylandcropland and pasture , while in the Corine dataset several landcover classes are represented, such as urban and built-up, decidu-ous broadleaf forest and cropland/woodland. Another difference is

found in the irrigated cropland and pasture, which in the Corine

Table 1aOverview of the stations used in this work and their geographical location inlongitude and latitude.

Station name Longitude (degree) Latitude (degree)

Pavia 9.15 45.05Gallarate 8.79 45.66Osio Sotto 9.58 45.62Treviglio 9.59 45.52

Bergamo via Meucci 9.88 45.61Bergamo via Garibaldi 9.66 45.69Calusco 9.47 45.69Arese 9.07 45.55Cassano d'Adda 9.51 45.52Cinisello Balsamo 9.21 45.55Legnano 8.91 45.59Corsico 9.11 45.53Limito 9.33 45.50Milan viale Marche 9.19 45.49Pero 9.08 45.51Rho 9.04 45.53Turbigo 8.73 45.53Busto Arsizo, via Magenta 8.85 45.61Somma Lombardo MXP 8.70 45.68Varese via Vidoletti 8.80 45.84Lallio 9.63 45.66Costa Volpino 10.10 45.83Cantu 9.13 45.73Como 9.08 45.80Erba 9.22 45.81Mariano Comense 9.18 45.70Olgiate Comasco 8.96 45.78Colico 9.36 46.13Lecco 9.39 45.85Valmadrera 9.36 45.85Moggio 9.48 45.93Ospitaletti Brescia 10.07 45.55Cormano 9.16 45.54Saronno via Santuario 9.02 45.62Casirate 9.56 45.49Montanaso 9.46 45.33Broletto 10.21 45.54Darfo 10.18 45.88Magenta 8.88 45.46Sarezzo 10.20 45.65Lodi via Vignati 9.50 45.31Varese Voghera 9.01 44.99

Table 1bOverview of the WRF-Chem parameterisations, which are used for the 2 km 2 kmdomain.

Parameter WRF-Chem

Integration time step [s] 6Radiation calculation

frequency [min]10

Snow cover effects Yes (Noah)

Cloud effec t on radia tion Yes (Goddard)Radiation Chou and Suarez (1999 ,

NASA Tech Memo)Microphysics WRF Single-Moment 5-class

scheme ( Hong and Lim, 2006 )Cumulus scheme NonePBL YSU (MRF successor) ( Hong et al., 2006 )LSM Noah ( Chen and Dudhia, 2001 )Surface Layer Monin-ObukhovChemistry option GOCART coupled with RACM-KPPChemistry time step 1.5Photolysis scheme Madronich photolysis (TUV)Gas dry deposition YesAerosol dry deposition YesGas chemistry YesAerosol chemistry YesWet scavenging NoneVertical mixing YesSubgrid convective transport YesBiomass burn ing emissions NoneAerosol radiation feedback

(direct and indirect)None

http://-/?-http://www.mmm.ucar.edu/wrf/users/docs/user_guide/ARWUsersGuide.pdfhttp://www.mmm.ucar.edu/wrf/users/docs/user_guide/ARWUsersGuide.pdfhttp://www.acd.ucar.edu/wrf-chem/download.shtmlhttp://www.acd.ucar.edu/wrf-chem/download.shtmlhttp://edgar.jrc.ec.europa.eu/http://edgar.jrc.ec.europa.eu/http://www.acd.ucar.edu/wrf-chem/download.shtmlhttp://www.acd.ucar.edu/wrf-chem/download.shtmlhttp://www.mmm.ucar.edu/wrf/users/docs/user_guide/ARWUsersGuide.pdfhttp://www.mmm.ucar.edu/wrf/users/docs/user_guide/ARWUsersGuide.pdfhttp://-/?-


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meteorological variables (wind speed at ten metres height, tem-perature at 2 m height [T2] and precipitation) in WRF was inves-tigated. They compared the results with the WRF simulation usingthe standard 30-arc seconds USGS Land Cover and topography, andwith observations of the ARPA network, with the focus on theLombardy region (north Italy) for a winter and a summer period in2010. They used the same model con guration in WRF as this study,

with the only difference being that the domain of interest in thisstudy is largerand the horizontal resolution is 2 2 km, while in DeMeij and Vinuesa (2014) the domain had a horizontal resolution of 1 1 km.

3.1.1. Temperature at 2 m (T2)De Meij and Vinuesa (2014) showed that during the winter

period large differences are found in calculated T2 between the twosimulations outside the city of Milan. WRF_USGS underestimatedthe observed temperatures for seven (out of 20 stations), whileWRF_CLC underestimated the observations for 4 stations. In allcases the relation RMSE mod < STDEV obs is valid, which is one of theconditions for good quality modelling results ( Barna and Lamb,2000 ). In our study we nd similar differences in temperature

outside the city of Milan i.e. higher temperatures by WRF_CLC(Fig. 3) due to the higher fraction urban built up area in the CorineLand Cover, which is less present in the 30-arc seconds USGS(Fig. 2). De Meij and Vinuesa (2014) found a large difference in thefraction of urban built up for this area (~17 times higher inCorineLC).

Over the Alps T2 by WRF_CLC are in general higher (up to~3 C),but there are also some areas for which WRF_CLC calculates lowerT2 than WRF_USGS (up to 3.5 C). Analysing the differences in theterrain height between WRF_CLC (with SRTM) and WRF_USGS wesee that the differences in T2 follow the pattern of the differences inthe terrain height ( Fig. 3c). Over the Alps, WRF_CLC calculateshigher temperatures (indicated in green, yellow and red) in areaswhere the SRTM height is lower than USGS topography. Similar but

opposite, the areas within the black contour lines represent areas of

lower surface temperatures (indicated in blue) and higher topog-raphy by SRTM.

This suggests that the difference in the heights and slopes of themountains between the SRTM and USGS topography is responsiblefor the differences in the temperatures over the Alps.

Higher T2 is calculated by WRF_USGS over the Po and Ticinorivers than by WRF_CLC. This is related to the differences in latentand sensible heat uxes, which will be explained hereafter. The strange feature at the bottom of the plot is probably related thepresence of high steep mountains at the Ligurian coast line(topography better resolved by SRTM) and the border effect. Theborder effect is a common strange feature in WRF using multiplenesting over complex terrains.

To understand better the differences in simulated T2 betweenWRF_CLC and WRF_USGS we analyse in Fig. 4 the upward sensible(SHF) and latent (LH) heat uxes by the two simulations. Thelatent (moist) and sensible (dry) heat uxes regulate the groundtemperature and the planetary boundary layer (PBL) development.When the ratio between sensible and latent heat ux (Bowenratio) is large the PBL is deeper than when the Bowen ratio issmall. Over most land surfaces the sensible heat uxes determinethe convection of air in the atmosphere (Ball 1960) and thereforethe PBL. When the latent heat uxes are higher than the sensibleheat uxes, the temperature near the surface is lower compared tothe areas where the sensible heat uxes and PBL heights arehigher ( Fraedrich et al., 1999 ). Unfortunately we do not have ob-servations available of the sensible, latent heat uxes and PBL heights to compare with calculated heat uxes and PBL heightsby WRF_CLC and WRF_USGS. However, comparing the heat

uxes and PBL heights between WRF_CLC and WRF_USGS helpsus to understand the differences in calculated meteorologicalparameters.

Analysing the monthly average SHF in Fig. 4 we see that higheruxes are calculated over urban areas by WRF_CLC than by

WRF_USGS (indicated in light blue). These areas of higher SHFcorrespond to the higher fraction of the urban built-up class in the

Corine Land Cover, which is less represented in the USGS asmentioned before. Around Turin, Gallarate, Novara, Osio Sotto,Milan and Piacenza higher SH uxes are calculated by WRF_CLC,due to the larger fraction of urban built-up class in the Corine LC.Conversely larger SH uxes are calculated by WRF_USGS for thoseareas which correspond to the location of rivers (indicated in or-ange) that pass nearby Pavia and Piacenza. Analysing the waterbodies class in the two LC datasets, we see that the rivers in theUSGS dataset are more clearly represented than in the Corine LC.These differences are related to the higher spatial resolution of theCorine LC that resolves better the geographical distribution of theland cover (LC) classes in the domain. For example, the Poriver is onmany places less than 300 m wide. Hence the Corine LC (resolutionof 100 100 m) resolves the rivers better than the 30 arc seconds

USGS (resolution of ~1 1 km), and therefore does not showelevated heat uxes over the river Po and Ticino river as WRF_USGSdoes.

In Fig. 5 the monthly average latent heat (LH) uxes by WRF_CLCand WRF_USGS are presented. Clear differences are present in LH

uxes between the two simulations. Higher LH uxes are found byWRF_USGS over a large part of the Po Valley, especially over thoseareas for which the LC class urban built-up is missing or less rep-resented in the USGS dataset. Higher LH uxes over land result inlower temperatures at the surface, as shown in Fig. 3. This con rmsthe nding by De Meij and Vinuesa (2014) . Higher LH uxes are alsofound by WRF_USGS over the river Po and the Ticino river (indi-cated in red), for which the simulation with the Corine LC calculateslower LH uxes. The differences in LH uxes are related to higher

details of the LC classes in Corine LC as described before.

Table 2USGS 24 land usecategories together with thenumberof cells perlanduse, inmodeldomain 2, for USGS and Corine Land cover.

USGS Landuse category

Land use descrip tion # Cel ls in theUSGS land use

# Cells in thecorine Landcover

1 Urban and built-up land 66 2772 Dryland cropland and pasture 5541 2412

3 Irrigated cropland and pasture 141 5964 Mixed dryland/irrigated

cropland and pasturee e

5 Cropland/grassland mosaic 40 e6 Cropland/woodland mosaic 256 6157 Grassland 241 5088 Shrubland 95 e9 Mixed shrubland/grassland 10 4210 Savanna 2 e11 Deciduous broadleaf forest 608 95312 Deciduous needleleaf forest 1 e13 Evergreen broadleaf e e14 Evergreen needleleaf 1116 60215 Mixed forest 566 18616 Water bodies 267 12217 Herbaceous wetland e e18 Wooden wetland e e19 Barren or sparse ly vegeta ted e 131720 Herbaceous tundra e e21 Wooded tundra 932 e22 Mixed tundra 3 e23 Bare ground tundra e e24 Snow or ice 393 235



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Fig. 3. Average temperatures at 2 m height (degrees Celsius) simulated by WRF_CLC (a) and WRF_USGS (b) for January 2010. Temperatures lower than 10 are indicated in blue(minimum temperature is around 22 C for the two simulations) and temperatures higher than 6.5 are indicated in red. For WRF_USGS the maximum temperature is 6.9 C. InFig. 3c is the difference in monthly mean 2 m temperature (degrees Celsius) between WRF_CLC and WRF_USGS shown, together with the difference in height between SRTM andUSGS topography datasets (black contour lines). (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)



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We have seen that over the Po Valley region higher SH uxes arein general calculated by WRF_CLC, due to the higher fraction of theurban built-up class in the Corine Land Cover. Larger SH uxesresult in larger PBL heights ( Van den Hurk, 1995 ) over the urban

areas, which corroborates the monthly average PBL heights byWRF_CLC and WRF_USGS in Fig. 6 . PBL heights are in general higherfor WRF_CLC than for WRF_USGS. This corresponds with the resultsby De Meij and Vinuesa (2014) . Higher PBL heights are calculatedover the Po and Ticino rivers by WRF_USGS, because higher sen-sible heat uxes are calculated for these areas as described earlier.

3.1.2. Wind speedIn Fig. 7 clear differences are found in the average simulated

wind speeds at ten meters (10 m) height between WRF_CLC andWRF_USGS over a large area in the Po Valley (larger by WRF_USGS).For large areas in WRF_CLC, which are classi ed as urban areas inthe Corine Land Cover data ( Fig. 2), lower wind speeds are calcu-

lated. Since the Corine Land Cover includes more urban area in the

model domain than the 30-arc seconds USGS data the wind speedsare reduced due to more friction. This con rms the nding by DeMeij and Vinuesa (2014) . They found that the calculated averagewind speeds are in general lower by WRF_CLC than by WRF_USGS.

WRF_CLC predictions are in better agreement with the observa-tions. For example, the bias of the average wind speeds predicted byWRF_CLC is lower (0.70 0.33 m/s) than that predicted byWRF_USGS (0.78 0.46 m/s). Similarly the STDERR values, basedon 8 stations, are on average lower by WRF_CLC (1.30) than byWRF_USGS (1.38). All RMSE values are lower than the standarddeviation of the observed wind speeds.

SRTM topography shows slightly higher mountain peaks (up to4015 m) than USGS topography (maximum is ~4009 m). The dif-ferences in terrain heights between SRTM and USGS are shown inFig. 8. Clearly visible from this gure are the differences in terrainheight over the Alps. Higher resolution of the topography by SRTM(90 90 m) resolves better the mountains peaks, slopes and val-leys, which impact the wind speeds (and direction) over complex

Fig. 4. Monthly average sensible heat uxes by WRF_CLC (a) and WRF_USGS (b) in W/m 2. The red colour represents SH uxes higher than 200 W/m 2 and purple lower than 34 W/m 2. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)



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mountainous terrains. For this reason WRF_CLC with SRTMtopography calculates in general higher wind speeds over the Alpsthan WRF_USGS.

3.1.3. PrecipitationOver urban areas (e.g. city of Turin, Gallarate, city of Milan, and

the Bergamo area (Orio Sotto)) higher monthly total precipitationare calculated by WRF_CLC ( Fig. 8). The differences in higher pre-cipitation over urban areas by WRF_CLC are related to the differ-ences in the heat uxes and the related cloud liquid water. Cloudliquid water over the Po Valley is larger for WRF_CLC varying be-tween ~5 and 60% and over the Alps up to 70 e 80 % higher for someplaces leading to more precipitation by WRF_CLC.

Fig. 8 shows also the differences in precipitation over thedomain. Analysing the differences in precipitation over the Alps byoverlaying the differences in height between the SRTM and USGStopography (black contour lines) we see a clear pattern between

the differences in precipitation and those of height.

In general, higher precipitation by WRF_CLC (indicated in green,yellow and red (in web version)) is calculated for those areas overthe Alps where the terrain height by SRTM is higher (solid blacklines). Lower precipitation by WRF_CLC (indicated in blue) is foundfor which USGS represents higher topography. Overall higher cloudliquid waterquantities are found by WRF_CLC over the Alps than byWRF_USGS (not shown), which implies that the SRTM topographyincreases the production of cloud liquid water and hence precipi-tation. Unfortunately there are no free observations of precipitationavailable for the Alp region.

In De Meij and Vinuesa (2014) a statistical analysis was per-formed of the hit rates of the precipitation for the stations in the PoValley. They used ve threshold values for the precipitation amountaccumulated over the day: 0.1 mm/day, 0.2 mm/day, 0.5 mm/day,1 mm/day, and 2 mm/day. The selected threshold values describethe amount of precipitation in different bins for that region and forwhich the hit rate statistics still give reasonable values. They foundthat the probability of detection of the precipitation event issomewhat higher (on average 1%) by the simulation with the SRTM

Fig. 5. Monthly average sensible heat uxes by WRF_CLC (a) and WRF_USGS (b) in W/m 2. The red represents LH uxes higher than 100 W/m 2 and purple lower than 10 W/m 2.(For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)



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and Corine Land Cover than simulation with USGS, while the falsealarms values are in general similar. The frequency bias for thethreshold values 0.5 mm and 1.0 mm are larger than 1 for both thesimulations, indicating that they both overestimate the number of precipitation events for these thresholds. Interesting is that for thethreshold value of 2 mm, lower FBI values are found for WRF_CLC atthe mountain stations (e.g. Como Villa Geno, Como Villa Gallia andCaslino d'Erba). This result indicates a better timing in the precip-itation events at these areas and is attributed to the implementa-tion of the SRTM topography.

The Hansen-Kuipers score, which summarizes the model'sability to correctly time both the precipitation events and to avoidthe false alarms, is in general 1% higher for WRF with SRTM andCorine Land Cover. For the stations surrounded by complex terrain,the maximum difference in HKS is found for Como Villa Gallia (31%,2 mm threshold level). This indicates that WRF_CLC performsbetter than WRF_USGS for the heavier precipitation events for thisstation. The majority of the stations used in this work are located inthe Po Valley for which the differences in monthly precipitationquantities between WRF_CLC and WRF_USGS are ~5 mm. Theaverage HKS is higher by WRF_CLC at six stations (out of 18) and byWRF_USGS at 2 stations (out of 18). The larger monthly mean

differences in precipitation are found over the Alps, up to 15 mm(Fig. 8). Unfortunately we had no precipitation data available tocompare our model results over the Alps, which would make ourstatistical comparison more robust.

For 7 (out of 18) stations, the land use type classi cationchanged. For example, Olgiate Comasco is classi ed as Drylandcropland and pasture in USGS while as Urban built-up land in theCorine LC. Analysing the HKS for the 2 mm threshold at this stationwe nd that it is higher by 9% for the WRF_CLC simulation. Themaximum difference in the HKS is found for Como Villa Gallia (29%,WRF_CLC higher). This station is classi ed as Dryland cropland andpasture in USGS and as Cropland/woodland mosaic. The maximumdifference in topography between USGS and SRTM around thisstation is ~75 m, which impact the cloud liquid water content asmentioned earlier. Higher monthly average cloud liquid watercontent is found for WRF_CLC (~63% higher) than WRF_USGS forthis area.

3.2. Gas pollutants

Dry deposition is responsible for a large amount of removal of

gases and aerosols from the atmosphere. In contrast to wet

Fig. 6. Monthly average PBL heights (m) by WRF_CLC and WRF_USGS.



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Fig. 7. Monthly average wind speed (m/s) calculated by WRF_CLC (a) and WRF_USGS (b) for January 2010. The white pixels in WRF_USGS represent wind speeds up to 7.7 m/s.

Fig. 8. The differences in total monthly precipitation quantities between WRF_CLC and WRF_USGS. The black contour lines represent the differences in height between the SRTM

topography and the 30 arc seconds USGS topography (at 50 m).


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Fig. 9. Difference of monthly average CO (a), NO (b) and SO 2 (c) concentrations (ppmv) between WRF_CLC and WRF_USGS.



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deposition, which occurs in speci c events, dry deposition is acontinuous process, occurring at the surface. In this section weanalyse the impact of SRTM and Corine LC on gas pollutantconcentrations.

In general, monthly average CO, NO and SO 2 concentrations arehigher for WRF_CLC over a large part of the domain ( Fig. 9), espe-cially over the Po Valley (indicated in green, yellow and red (in webversion)). The reason for this is related to the larger fraction of urban built-up areas in the Corine Land Cover, which reduce thewind speeds due to increased friction and therefore increased thedry deposition. The distribution of the anthropogenic emissionssuch as CO, NO, SO

2 corroborates with the location of the urban

areas, as represented in the Corine Land Cover. These urban areasare less represented in the USGS dataset. For example, in Fig. 2 wehave seen that the area north-west of Milan is dominated by theurban built-up area class in the Corine Land Cover, while in theUSGS dataset this area is dominated by deciduous broadleaf forest,cropland/woodland and dry cropland. The highest emission

quantities of CO, SO 2 and NO are found over urban areas such asTurin, Milan, Pavia, Novara and north-west of Milan (not shown).

For CO, maximum differences in calculated monthly concen-trations (WRF_CLC > WRF_USGS) are found around Turin, Milanand Pavia (up to 0.038 ppmv, ~20% higher) and negative differences(WRF_CLC < WRF_USGS) are found up to 0.026 ppm (~10% lower)north of Turin. Comparing with observations, calculated monthlymean CO concentrations are in general underestimated by a factorof ~3.6 by both the simulations, respectively (based on 18 stations,see Table 3 ). For both simulations, the temporal correlation co-ef cients are in general low, on average ~0.19.

A possible explanation for this large underestimation could berelated to frequent wood burning for heating purposes in NorthernItaly during the winter time, which are not accounted for in theemission inventory. Uncertainties in the emission factors for CO inthe emission inventory and unaccounted sources of CO whichcontribute to the underestimation of CO in the inventory could beheld responsible for the underestimation of CO in a winter period.We emphasize that the objective of this study is to investigate theimpact of the high resolution SRTM topography and Corine LandCover on calculated air pollutants and not to evaluate the modelledperformance in calculated air pollutant concentrations bycomparing with observations.

For SO2 maximum differences are found also between Gallarateand Novara up to 0.020 ppmv (~55%) and negative difference upto 1.10 4 ppmv (~ 15%) between Pavia and Piacenza. Comparingwith observations ( Table 4 ), calculated monthly mean SO 2 con-centrationsare in general underestimatedby a factor of ~2.9 and3.6by WRF_CLC and WRF_USGS, respectively For the stations whichare characterized as urban (e.g. Arese, Cassano d'Adda, CiniselloBalsamo, Corsico and Legnano), the monthly mean observed valuesare a factor of 9 and 10 higher than the calculated ones by WRF_CLCand WRF_USGS, respectively. Interesting to note is that for theselocations, the monthly average SO 2 calculated concentrations forWRF_CLC is ~10% higher than for WRF_USGS. The large over-estimation by the two simulations at Turbigo might be related to

the presence of the power plant in this area and the horizontaldistribution of the emissions in the emission inventory. Therepresentativity of the measurement location relative to the gridcell of the model is always an issue when model results arecompared with ground-based measurements ( De Meij et al., 2006 ).The highest temporal correlation coef cients are found for VareseVidoletti; 0.45 for WRF_CLC and 0.33 for WRF_USGS. For the otherstations, the temporal correlation coef cients are low, on average0.12. Excluding the urban stations and Turbigo from the compari-son results in an underestimation by a factor of 1.5 and 1.8 forWRF_CLC and WRF_USGS, respectively.

For NO, maximum differences are found between Gallarate andNovara up to 0.017 ppmv (~50%) with negative differences (~ 20%)found north of Turin. Compared to observations, calculated

monthly mean NO x concentrations are in general underestimatedby a factor of ~3.5 by the two simulations (based on 31 stations,Table 5 ). On average, WRF_CLC calculates higher monthly meanNO x values (~5%) than WRF_USGS for 12 (out of 29) stations, whileWRF_USGS calculates higher monthly mean NO x concentrations fortwo stations. In general the temporal correlation coef cients by thetwo simulations are low (~0.28) with the highest temporal corre-lation coef cient found for Colico by WRF_CLC (0.53).

As described earlier, the monthly mean CO concentrations bythe two simulations are very similar. However, analysing thecalculated CO concentrations for speci c time periods we see largerdifferences between WRF_CLC and WRF_USGS.

For example, in Fig. 10 a the measured CO concentrations (ppm)for Areseare shown, together with the calculated concentrationsby

WRF_CLC and WRF_USGS. Arese is classi ed as urban built-up in

Table 3Monthly mean observed and calculated CO concentrations (ppm) by WRF_CLC andWRF_USGS.

Sta tion name Observed monthlymean CO (ppm)

WRF_CLC monthlymean CO (ppm)

WRF_USGSmonthlymean CO (ppm)

Gallarate 0.59 0.47 0.24 0.10 0.24 0.09Pavia 0.65 0.25 0.15 0.05 0.14 0.05

Milan Verziere 1.78 0.50 0.43 0.20 0.42 0.20Arese 1.31 0.48 0.37 0.17 0.36 0.15Treviglio 1.65 0.42 0.22 0.11 0.22 0.10Calusco d'Adda 0.85 0.33 0.22 0.08 0.22 0.09Bergamo via Garibaldi 1.93 0.51 0.22 0.08 0.21 0.08Cassono d'Adda 1.49 0.29 0.24 0.12 0.24 0.12Corsico 1.66 0.47 0.39 0.19 0.39 0.18Limito 1.06 0.36 0.34 0.18 0.33 0.17Milan, viale Marche 1.99 0.46 0.45 0.21 0.44 0.21Rho 1.60 0.50 0.34 0.15 0.35 0.15Busto Arsizo, via

Magenta1.12 0.49 0.25 0.10 0.25 0.10

Somma Lombardo MXP 0.55 0.37 0.21 0.09 0.21 0.09Saronno, via Marconi 0.63 0.44 0.31 0.14 0.31 0.12Como 2.20 0.72 0.22 0.09 0.21 0.08Pavia via Folperti 1.00 0.22 0.12 0.03 0.11 0.11Voghera 0.66 0.24 0.13 0.04 0.12 0.03 Average 1.26 0.42 0.27 0.12 0.27 0.12

Table 4Monthly mean observed and calculated SO 2 concentrations (ppb) by WRF_CLC andWRF_USGS.

Station name Observed monthlymean SO 2 (ppb)

WRF_CLC monthlymean SO 2 (ppb)

WRF_USGSmonthly mean

SO2 (ppb)Gallarate 1.16 1.31 3.87 3.90 2.99 3.01Lallio 3.53 0.45 3.91 3.06 3.68 2.65Bergamo via Garibaldi 4.38 0.94 4.13 3.06 3.91 2.83Treviglio 3.19 0.27 3.23 3.20 2.92 2.82Colico 3.04 0.72 2.36 1.41 2.14 1.25Como 1.15 0.59 5.87 4.07 5.16 3.13Erba, via Battist i 1.16 0.44 5.25 3.42 4.87 2.86Varese Vidole tti 2.41 0.71 4.38 3.11 3.84 2.56Arese 70.2 54.2 9.47 5.80 8.78 4.67Cassano d'Adda 50 .7 30.9 3.65 3.46 3.30 3.17Cinisello Balsamo 92.7 57.5 12.8 7.54 11.6 6.07Corsico 52.6 32.2 5.64 4.29 5.04 3.62Legnano 61.4 44.4 7.59 5.39 6.76 4.54Cormano 1.80 1.89 12.5 7.29 11.4 5.80Turbigo 2.68 1.87 33.8 22.9 22.6 13.4 Average 23.5 15.2 7.90 5.46 6.60 4.16



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WRF_CLC and as irrigated cropland and pasture in WRF_USGS.Monthly average observed concentration is 1.31 0.48 ppm and0.37 0.17 by WRF_CLC and 0.36 0.15 ppm by WRF_USGS. Thetemporal correlation coef cients are 0.33 and 0.29, respectively. Asshown in Fig. 7 WRF_CLC calculates lower wind speeds over the PoValley area (where Arese is situated). Lower wind speeds will in-crease the drydeposition of gas pollutants, hence increasing the gaspollutant concentrations at ground level. In Fig. 10b, we show thecalculated wind speeds by WRF_CLC and WRF_USGS together withthe calculated CO concentrations. Clearly visible is that when thewind speeds decrease the calculated CO concentrations increaseand vice versa. For example, on 23 January higher wind speeds arecalculated by WRF_USGS (dashed red line) than by WRF_CLC (blackdashed line). The resulting CO concentrations by WRF_CLC are afactor of ~2 higher than by WRF_USGS.

3.3. PM10

Fig. 11 shows the difference in monthly average PM10 concen-trations between WRF_CLC and WRF_USGS. The white linesrepresent the height of the topographic from the SRTM dataset.

For the Po Valley area WRF_CLC calculates in general highermonthly average PM10 concentrations, varying between ~1 mg/m 3

(~3% higher) northerlyof Milan to 2 mg/m 3 (~7% higher) southerly of Milan and 6.7 mg/m 3 (~26% higher) westerly of Milan. Over a largepart of the Alps (indicated with the white terrain height contourlines) WRF_USGS calculates higher monthly average PM10 con-centrations (indicated in blue (in web version)) than WRF_CLC, butthe differences are small on average ~0.25 mg/m 3 (up to ~20% lower

by WRF_CLC). An important removal mechanism for particulate

matter of the atmosphere is wet deposition. As mentioned in Sec-tion 3.1 large areas over the Alps show larger precipitation byWRF_CLC, which removes the aerosol and precursors more effec-tively leading to lower monthly average PM10 concentrations thanby WRF_USGS. Similar to the distribution of the emissions of thegas species, the highest PM10 emissions are found over Turin,Milan, Pavia, Novara and the area north-west of Milan, which cor-roborates the presence of urban built-up area in the Corine LandCover, as mentioned previously.

Analysing the calculated PM10 concentrations in more detail,we nd large differences between WRF_CLC and WRF_USGS,especially during the night. In Fig. 12, we show for Busto Arsizio thecalculated PM10 concentrations by WRF_CLC and WRF_USGS(dashed black and red lines, respectively) together with the PBL heights for the two simulations (solid black and red lines, respec-tively). The city of Busto Arsizio (located ~ < 5 km south-easterly of Gallarate) is classi ed as urban built-up in WRF_CLC and as drycropland and pasture in WRF_USGS.

The reason for the differences in PM10 concentrations is relatedto the differences in PBLheights, especially during the night. For theperiods that WRF_CLC calculates higher PM10 concentrations thePBL heights are lower than by WRF_USGS. For example, on 23rd January higher PM10 concentrations are calculated by WRF_CLCthan by WRF_USGS (up to ~13 mg/m 3 difference around 07:00GMT).This difference is related to the lower PBL heights by WRFC_CLC.The average PBL height during the night of 23 January (i.e. between18:00 22nd January and 06:00 23rd January) by WRF_CLC is ~45 mand ~143 m by WRF_USGS with a maximum difference of ~270 maround 01:00 (GMT) on 23 January, see solid black and red lines inFig. 12 . The differences in PBL heights are caused by the differentland cover classi cations and the related heat uxes. As describedbefore, sensible heat uxes are responsible for the development of the PBL heights. Corresponding average sensible heat uxesare 0.02 W/m2 for WRF_CLC and 7.3 W/m2 for WRF_USGS duringthis period, which are related to the differences in land coverclassi cations as mentioned before.

The difference in PBL heights is responsible for the differences inaerosol concentrations between WRF_CLC and WRF_USGS. Duringthe night of 23 January the vertical mixing by WRF_USGS is betterthan by WRF_CLC, because of the higher PBL height. A deeper PBL leads to lower aerosol concentrations at ground level. Daily averagePM10 observed concentration for Busto Arsizio on 23rd January is66 mg/m 3 and calculated averages are 32.6 mg/m 3 by WRF_CLC and23.5 mg/m 3 by WRF_USGS.

A quantitative comparison of the monthly average PM10 con-centrations with the observations shows that calculated PM10concentrations are underestimated by a factor ~4 by the two sim-ulations (see Table 6 ). As mentioned in Section 2 the absence of abrasive road and construction diffusive PM10 emissions in theEDGAR emission inventory, the absence of PM emissions by wood

burning for residential heating purposes in northern Italy, theabsence of biogenic emissions and the absence of SOA formationcontribute to the underestimation of the calculated PM10 concen-trations. Nevertheless, on this day WRF_CLC calculates higherPM10concentrations than by WRF_USGS due to the impact of theimprovedresolution brought by the Corine Land Coverand is closerto the observations. A study by De Meij et al. (2009) found also alarge underestimation of calculated PM10 concentrations over thePo Valley during a winter period. Monthly mean calculated PM10concentrations were underestimated by a factor of ~3 for January2005 when compared to observations (based on 5 stations).

On average WRF_CLC calculates higher monthly mean averagePM10 concentrations (3.5%) than WRF_USGS for 15 (out of 25)stations. The largest differences are calculated for Turbigo (13.8%),

Voghera (14.2%) and Montanaso (8%). For 12 (out of 25) stations, the

Table 5Monthly mean observed and calculated NO x concentrations (ppm) by WRF_CLC andWRF_USGS.

Station name Observedmonthlymean NO x (ppm)

WRF_CLC monthlymean NO x (ppm)

WRF_USGSmonthly meanNO x (ppm)

Pavia 0.06 0.02 0.01 0.01 0.01 0.01Gallarate 0.09 0.07 0.02 0.01 0.02 0.01

Osio Sotto 0.07 0.04 0.02 0.01 0.02 0.01Treviglio 0.06 0.03 0.02 0.01 0.02 0.01Bergamo via Meucci 0.08 0.06 0.01 0.00 0.01 0.01Bergamo via Garibaldi 0.10 0.07 0.02 0.01 0.02 0.01Calusco 0.11 0.07 0.02 0.01 0.02 0.01Arese 0.10 0.08 0.04 0.02 0.03 0.02Cassano d'Adda 0.07 0.04 0.02 0.02 0.02 0.01Cinise llo Balsamo 0.13 0.08 0.05 0.03 0.05 0.02Legnano 0.09 0.06 0.03 0.01 0.03 0.01Corsico 0.07 0.05 0.03 0.02 0.03 0.02Limito 0.08 0.05 0.03 0.02 0.03 0.02Milan viale Marche 0.12 0.07 0.04 0.02 0.04 0.02Pero 0.13 0.09 0.04 0.02 0.04 0.02Rho 0.10 0.07 0.03 0.02 0.03 0.02Turbigo 0.06 0.04 0.04 0.02 0.03 0.02Busto Arsizo, via Magenta 0.06 0.05 0.03 0.01 0.03 0.01Somma Lombardo MXP 0.06 0.05 0.02 0.01 0.02 0.01Varese via Vidoletti 0.06 0.04 0.02 0.01 0.02 0.01Lallio 0.09 0.07 0.02 0.01 0.02 0.01Costa Volpino 0.06 0.03 0.01 0.00 0.01 0.00Cantu 0.10 0.06 0.02 0.01 0.02 0.01Como 0.11 0.07 0.02 0.01 0.02 0.01Erba 0.06 0.05 0.02 0.01 0.02 0.01Mariano Comense 0.11 0.08 0.03 0.01 0.03 0.01Olgiate Comasco 0.08 0.06 0.02 0.01 0.02 0.01Colico 0.03 0.02 0.01 0.01 0.01 0.01Lecco 0.09 0.06 0.02 0.01 0.02 0.01Valmadrera 0.05 0.04 0.01 0.01 0.02 0.01Moggio 0.01 0.01 0.01 0.01 0.01 0.00 Average 0.08 0.01 0.02 0.01 0.02 0.01



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temporal correlation coef cients are < 0.2, while for Gallarate,Moggio, Darfo, Turbigo, Erba, Saronno, Busto Arsizio, Cantu, Areseand Olgiate Comasco the temporal correlation coef cients arehigher than 0.40, with the highest temporal correlation coef cientsfor Gallarate (0.59 for WRF_CLC and 0.60 for WRF_USGS).

In Fig. 13 another example is presented for Osio Sotto. Thislocation is classi ed as dry cropland and pasture in WRF_CLC and asirrigated cropland and pasture in WRF_USGS.

On 17th January higher PM10 concentrations are calculated byWRF_CLC than by WRF_USGS (up to ~9 mg/m 3 difference during themorning). This difference is related to the lower PBL heights byWRF_CLC, caused by the difference land cover classi cations andthe related heat uxes as mentioned previously. The average PBL height during the night of the 17th January by WRF_CLC is ~35 mand ~86 m by WRF_USGS with a maximum difference of ~76 m.This difference in PBL height is responsible for the differences inaerosol concentrations between WRF_CLC and WRF_USGS. Thecorresponding sensible heat uxes are 2.3 W/m 2 by WRF_CLC and6.0 W/m 2 by WRF_USGS, due to the differences in land coverclassi cation. Daily average PM10 observed concentration for OsioSotto on 17th January is 77 mg/m 3 and calculated averages are

26.6 mg/m 3 by WRF_CLC and 25.3 mg/m 3 by WRF_USGS.On the other hand, on the 17th and 18th around midday, higher

PBL heights are calculated by WRF_CLC with resulting lower PM10concentrations. Sensible heat uxes by WRF_CLC and WRF_USGS

Fig. 11. The difference in monthly average PM10 concentrations ( mg/m 3) between WRF_CLC and WRF_USGS. The white lines represent the terrain height of the topographic by the

SRTM dataset.

Fig.12. PM10 concentrations by WRF_CLC and WRF_USGS for Busto Arsizio (black andred dashed lines, respectively), together with the PBL heights for the two simulations(WRF_CLC black; WRF_USGS red) for 2 days in January (22nd e 24th January). Right y-axis represents the PBL height in meters. (For interpretation of the references to colourin this gure legend, the reader is referred to the web version of this article.)

(a)

(b)

Fig. 10. (a) Measured hourly CO concentrations (ppm) for Arese (black line) togetherwith the calculated concentrations by WRF_CLC (red line) and WRF_USGS (blue line)for January 2010. (b) Snapshot of the calculated CO concentrations together with thecalculated wind speeds for the period 22 and 23 January. The solid lines represent thecalculated CO concentrations by WRF_CLC (black) and WRF_USGS (red), the dashedlines the corresponding wind speeds by the two simulations. The right y-axis in (b)represents the wind speed in m/s. (For interpretation of the references to colour in this

gure legend, the reader is referred to the web version of this article.)



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are around 39.5 and 44.9 W/m 2, respectively and mean LH uxesare 19.2 and 22.0 W/m 2, respectively.

4. Conclusions

In this paper, we investigated the impact of using SRTMtopography data together with Corine Land Cover on the simulatedgas and PM10 by WRF-Chem. We focused our analysis on the well-known, highly urbanized region of the Po Valley in northern Italy.Our analysis was performed by comparing the results to a simula-tion using topographic and land cover USGS data, resolved to 30 arcseconds and observations.

Our analysis con rms the previous ndings of De Meij andVinuesa (2014) . Speci cally, large differences in the geographical

distribution of the land cover classes between Corine and USGSLand Cover datasets result in higher modelled temperatures at 2 mand reduced wind speeds in WRF_CLC mainly due to increasedfriction at the surface. We also found that WRF_CLC favours pre-cipitation in a large of part the Alps and that the pattern of thedifference by using the WRF_CLC or WRF_USGS follows the dif-ference in topography between the two topography datasets.

As land cover dataset's associated exchange coef cients aredifferent (i.e. latent and sensible heat uxes), large differencesbetween them impact noticeably the boundary layer developmentand depth.

In term of air quality indicators, the impacts are also large. Forinstance, monthly average of CO, NO and SO 2 concentrations over alarge part of the Po Valleyare higher when using Corine Land Cover,up to ~20, ~50 and ~55%, respectively. With respect to PM10, theimpacts are geographically dependent. Over the Po valley area,levels are in general higher using Corine Land Cover (up to 6.7 mg/m 3 [~26%] westerly of Milan) while differences are smaller over theAlps (~0.25 mg/m 3 [~20%]). Gas and PM10 concentrations areunderestimated by both model simulations. A quantitative com-parison of the monthly average PM10 concentrations with the ob-servations shows that calculated PM10 concentrations areunderestimated by a factor ~4 by the two simulations. However,during some episodes and for some regions larger differences incalculatedPM10 concentrations between WRF_CLC and WRF_USGSare found (e.g. higher than 10 mg/m 3 by WRF_CLC), with a betteragreement with the observations by WRF_CLC than by WRF_USGS.This could become important when daily limit values of PM10 maybe exceeded. For CO, SO 2 and NO x the modelled monthly meanconcentrations are similar for the two simulations. However,similar to PM10 larger differences are found between the twosimulations for some episodes, for which the simulation with SRTMand Corine LC agrees better with the observations.

Atmospheric chemistry transport models will bene t from theuse of the high resolution SRTM and Corine Land Cover data,especially with regard to reducing the differences between

observed and simulated aerosol (precursor) concentrations, whichis necessary for scienti c studies and for policy making. Besidesthis, environmental sustainable related projects (e.g. ENORASIS;http://www.enorasis.eu/ ) for which meteorological models areused for optimizing irrigation management by farmers, will bene tfrom higher precision precipitation, wind speed and temperature

elds by the meteorological models.

Acknowledgements

We would like to thank ARPA Lombardia for making the ob-servations of meteorological and air pollutant concentrationspublically available through their website. This study is fully fundedby NOVELTIS' internal R & D initiative.

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Fig. 13. PM10 concentrations by WRF_CLC and WRF_USGS for Osio Sotto (black andred dashed lines, respectively), together with the PBL heights for the two simulations(WRF_CLC black; WRF_USGS red) for 2.5 days in January (16th e 18th January). Right y-axis represents the PBL height in meters. (For interpretation of the references to colour

in this

gure legend, the reader is referred to the web version of this article.)

Table 6Monthly mean observed and calculated PM10 concentrations ( mg/m 3) for WRF_CLCand WRF_USGS.

Station name Observed monthlymean PM10 mg/m 3

WRF_CLC monthlymean PM10 mg/m 3

WRF_USGSmonthly meanPM10 mg/m 3

Busto Arsizio 63.3 27.9 17.1 6.55 17.0 6.40Gallarate Piazza

S. Lorenzo

67.9 33.3 16.1 6.17 16.1 6.13

Saronno via Santuario 76.2 35.6 18.0 7.05 17.8 6.72Calcuso d'Adda 65.1 27.1 18.2 5.68 18.2 6.14Casirad d'Adda 73.2 23.3 14.4 5.91 13.8 5.80Osio Sotto 59.3 25.8 15.3 5.77 15.0 5.78Treveglio 66.0 23.3 14.3 6.12 14.1 5.99Cantu via Meucci 50.9 22.6 15.7 5.74 15.5 5.46Erba v ia Bat tis ti 55.6 29.5 12.9 4.90 12.5 4.90Olgiate Comasco 59.9 25.5 14.1 5.32 14.0 5.20Arese 71.3 30.7 21.4 7.65 21.3 7.44Magenta 59.4 24.1 17.6 6.71 17.3 6.64Milano Verz iere 74.8 30.3 22.5 7.71 22.1 8.19Lodi viale Vignati 64.3 27.2 16.5 5.90 16.2 5.90Montanaso 58.6 21.8 16.0 4.93 14.8 5.44Bergamo via Garibaldi 78.3 32.8 13.9 4.84 13.5 5.01Lallio 71.4 27.4 14.6 5.18 14.4 5.18Moggio 11.3 7.24 8.51 3.79 8.69 3.84Valmadrera 63.9 31.1 12.3 4.68 12.9 5.01Lecco 54.2 30.6 14.5 5.31 14.4 5.40Varese Cope lli 56.8 24.7 14.0 5.34 14.2 5.10Voghera 58.2 24.3 11.2 4.22 9.80 4.30Turbigo 60.2 24.9 23.0 8.48 20.2 7.93Limito 77.2 30.8 18.7 7.50 19.1 8.13Cassano d'Adda 84.1 27.9 15.5 6.06 15.2 6.25 Average 63.3 26.8 15.9 5.90 15.5 5.93

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