1

Modeling Aerosol Optical Properties with AODEM: 1

Software description and preliminary results 2

Tony C. LANDI1, Gabriele CURCI1 3

1CETEMPS – Dept. Physics, University of L’Aquila, L’Aquila, Italy 4

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corresponding author: Tony C. Landi (tony.landi@aquila.infn.it) 6

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Keywords: aerosol optical properties, aerosol optical depth, chemistry transport models, aerosol 8

mixing state, vertical distribution 9

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Research highlights: 11

• Development of a post processing tool for aerosol optical depth calculation (AOD) from 12

chemistry transport model output 13

• External, internal and core-shell aerosol mixing state is simulated 14

• Results are compared to LIDAR vertical profiles, and AOD observed by sun-photometer 15

(AERONET) and satellite (MODIS) over Northern Italy 16

• Spatial and spectral broad features captured, AOD underestimated by 25% 17

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Abstract 1

We present a new Aerosol Optical DEpth Module (AODEM) conceived as a post-processing tool 2

for a general chemistry-transport model which simulates aerosol with a sectional approach. 3

AODEM computes particle number concentrations and extinction coefficients for each grid-cell, 4

species, size bin and time, under the assumption of spherical particles. The user may select three 5

types of aerosol mixing: external, internal homogeneous and internal coated spheres (black carbon 6

core and a well mixed shell). Mie calculations are carried out for each scene, avoiding use of lookup 7

tables. A first application of AODEM to CHIMERE model output is reported for a three day 8

summer period in 2007 over Northern Italy. The model qualitatively reproduces the vertical 9

distribution of aerosol particle throughout the day as observed by a LIDAR in Milan, roughly 10

reproducing the nighttime layer at 1500 m altitude and the daytime extinction enhancement just 11

below the boundary layer top. The spectral dependence of aerosol optical depth (AOD), which is 12

seen to decrease by a factor of 3 from 440 nm to 870 nm over the AERONET station in Modena, is 13

also captured by the model. The distribution of AOD over the Po Valley as observed by MODIS is 14

reproduced with a spatial correlation of 0.37 and a negative bias of -25%, pointing out most likely 15

model deficiency in the simulation of vertical mixing and secondary aerosol formation. 16

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1 Introduction 1

The role of aerosols in the climate system and their impact on global warming is a challenging 2

issue. Current estimates give a cooling at the Earth’s surface, a warming of the atmosphere, and a 3

negative balance at top of the atmosphere which is estimated to compensate part of the warming 4

due to greenhouse gases (IPCC, 2007). Aerosols affect climate directly by scattering and absorbing 5

shortwave and thermal radiation (direct aerosol effect). Aerosols also modify the radiation budget 6

indirectly by acting as cloud condensation nuclei (CCN) and ice nuclei (IN) (e.g. Lohmann et al., 7

2010). World-wide observations show that aerosol are a mixture of primary and secondary material 8

(Poschl, 2005) mostly made up by soil dust, sea spray, inorganic compounds (sulphates, ammonium 9

nitrates, and ammonia), and carbonaceous components (organic compounds and black or elemental 10

carbon). The latter account for 20 to 90% of submicron particle fraction (Jimenez et al., 2009). 11

Aerosol concentration, size, structure, and chemical composition and mixing are all factors that 12

determine the aerosol related environmental and health effects (Poschl, 2005). 13

Modeling aerosol properties is a challenging task because of the complexity of particle sizes, 14

chemical composition and degree of mixing (e. g. Bessagnet et al., 2008). The aerosol mixing state 15

plays a crucial rule in the atmospheric direct radiative forcing (Jacobson 2001), and also controls 16

the influence of other aerosol parameters on predicted CCN concentration (Wang et al., 2010). For 17

example, black carbon (BC) may exist in one of several possible mixing states: distinct from other 18

aerosol particles, or externally mixed (Haywood et al., 1997, Jacobson, 2000), or incorporated 19

within them, or internally mixed (Haywood et al., 1997, Myhre et al., 1998, Jacobson, 2000). The 20

internal mixing case may also be found in a core-shell arrangement, with BC being the core 21

surrounded by a well mixed shell made of other species (Jacobson, 2000). 22

Aerosol optical properties are generally modeled following the Mie theory (Bohren and Huffman, 23

1983). Global models typically use fixed size distributions depending on the aerosol type (sea-salt, 24

sulfates, etc.) in order to compute or tabulate aerosol extinction coefficients (e.g. Chung et al., 2005; 25

Chin et al., 2002; Penner et al., 2002; Kinne et al., 2006; Yu et al., 2006; Ginoux et al., 2006), while 26

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regional scale models exploit simulated aerosol size-dsitrubutions to calculate optical properties off-1

line (Jacobson, 2000; Hodzic et al., 2004, 2006; de Meij at al., 2007; Tombette et al., 2008; Péré et 2

al., 2009; Aouizerats et al. 2010). Through the simulation of lidar backscattering profiles, Hodzic et 3

al. (2004) demonstrated the usefulness of optical aerosol modeling to investigate the skills of 4

chemistry-transport models (CTM) in reproducing the aerosol vertical structure in and above the 5

planetary boundary layer. de Meij et al. (2007) tested CTM skills against remote-sensed (ground 6

and satellite based) aerosol optical properties over several horizontal resolution. Sensitivity studies on 7

the effect of aerosol mixing hypothesis on the ability to simulate aerosol optical depths has been reported by 8

several authors (Jacobson, 2001; Tombette et al., 2008; Péré et al., 2009; Péré et al., 2010). In their 9

study focusing on the European heatwave in summer of 2003, Péré et al. (2009) concluded the most 10

probable mixing state of aerosol is the core-shell mixing, with secondary aerosols coating over 11

primary soot and mineral dust. Most of the methods proposed to efficiently compute the optical 12

properties from CTM calculations exploit look-up tables of aerosol extinction and scattering 13

efficiencies as a function of the imaginary and the real part of the complex refractive index and size 14

parameters (de Meij et al., 2007; Tombette et al., 2008; Péré et al., 2009). 15

In this work we present the new stand-alone Aerosol Optical DEpth Module (AODEM) that 16

calculates aerosol optical properties from the output of a generic chemistry-transport model (CTM) 17

which simulates aerosol composition with a sectional approach (e.g. Bessagnet et al., 2004). 18

External, internal homogeneous, and internal core-shell have been considered as alternative 19

hypotheses of aerosol mixing. Mie computations are performed every time it is required, avoiding 20

use of lookup tables. 21

The structure of the paper is as follows. In section 2, we describe the CTM used to simulate aerosol 22

composition and distribution (CHIMERE) and the software package AODEM. We presents results 23

from a first qualitative and quantitative comparison with observations during three summer days 24

2007 over Northern Italy in section 3. We draw conclusions and plan future developments of 25

AODEM in final section 4. 26

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2 Models description 1

2.1 CHIMERE Chemistry Transport Model 2

In this study we apply the post-processing tool for calculation of aerosol optical properties to the 3

output of CHIMERE model. CHIMERE is a 3-D Chemistry Transport Model that given a set of 4

NOx, SOx, NH3, VOCs, CO, and PM emissions computes the concentrations of 44 gas-phase 5

species, on a regular grid (Bessagnet et al., 2008). The source code is available for download on the 6

web site: http://www.lmd.polytechnique.fr/chimere/download.php, where a more complete 7

documentation for the model is available. 8

Here, we use the version 2008c of the model over two nested domains. The first one covers Western 9

Europe ([15°W-35°N; 28°E-58°N]) with an horizontal resolution of 0.5° x 0.5°, while the one-way 10

nested domain covers Northern Italy ([6°W- 42°N; 14.5°E -47°N]) with an horizontal resolution of 11

0.1° x 0.1°. The vertical grid has 16 layers from surface to 500 hPa. The particle size distribution 12

ranges from about 40 nm to 10 µm and is distributed into 12 bins. Physical processes such as 13

transport and turbulent diffusion, and aerosol dynamical processes such as coagulation, 14

condensation/evaporation, adsorption/desorption, wet and dry deposition are taken into account and 15

fully described elsewhere (Bessagnet et al., 2004, 2008). 16

CHIMERE model is driven by the meteorological model MM5 for the dynamical parameters (wind, 17

temperature, humidity, pressure, etc.). MM5 vertical grid has 32 levels ranging from surface to 18

10hPa. The horizontal resolution is 36km and 12km respectively over coarse and nested domains. 19

Global meteorological forcing is taken from six-hourly ECMWF analyses in combination with grid 20

nudging option (FDDA). 21

Over coarse domain, anthropogenic emission data from the EMEP database (http://www.ceip.at/) 22

have been used, while over nested domain the emissions are taken from an higher resolution 23

inventory (5 km) developed by CTN-ACE (Italian National Focal Point on Atmospheric 24

Emissions). 25

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Gas and aerosol boundary conditions are issued from a 5-year climatology (2001-2005) of the 1

global model LMDzT-INCA. For aerosol boundary conditions, only elemental and organic carbon, 2

desert dust and sulfate are taken into account. 3

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2.2 Aerosol Optical DEpth Module (AODEM) 5

In Figure 1 we show the structure of AODEM package. Given the output of a chemistry-transport 6

model (e.g. CHIMERE) and an optical database (e.g. Shettle and Fenn, 1979), AODEM computes 7

the aerosol optical properties for each size bin, time slot, and grid point for a given wavelength. The 8

wavelength range obviously depends on the range of the input optical database: in the case of 9

Shettle and Feng (1979) the allowed wavelengths are from 340 nm to 1640 nm. AODEM first 10

calculates particle number concentration Nik (#/m3) of each aerosol model species i for each size bin 11

k provided by the CTM: 12

610−×=avoii

iairikik Nv

MwnNρ

χ (1) 13

where χik (ppbv), Mwi (g/mole), ρi (g/cm3), and vi (m3) are the mixing ratio, molar weight, density, 14

and volume of the i-th species in k-th bin respectively, nair is the air concentration (molecules/cm3) 15

and Navo is the Avogadro’s number (6.022×1023 molecules/mole). Assumed density for model 16

species are taken from Table 1. 17

Number concentrations of original model species are then aggregated into five species for optical 18

calculations: black carbon (BC), organic carbon (OC), sea salt (SS), dust (DUST), and the 19

secondary inorganic phase made of nitrate, sulphate, and ammonium (NSA). The two main user 20

options concern the refinement of original model size bins and the aerosol mixing hypothesis. 21

The first option consists in specifying a number of regular log-spaced subinterval in which divide 22

each model size bin to perform Mie calculations. This step is needed to correctly represent the non 23

linear variation of Mie extinction efficiency as a function of particle radius (Landi, 2010). Optical 24

properties are calculated in each “fine” bin and then summed into the original model bins. 25

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As shown in Figure 1, a bifurcation of the algorithm takes place according to the user choice of 1

aerosol mixing option. In case of external mixing, the radius and the spectral complex refractive 2

index ( )()()( λλλ iknm −= , where λ is the user selected wavelength) of the five “optical” species 3

are grown in each bin according to relative humidity (Hanel, 1976): 4

jejkjkwet RHrr −−= )1(, (2) 5

3,, )/()( jkwetjkwatjwatjwet rrmmmm ⋅−+= (3) 6

where rjk, rwet,kj, mj, and mwet,j are dry and wet particle radii and refractive indices of j-th species and 7

k-th bin, respectively, ej is the Hanel exponent of j-th species (reported in Table 1), mwat is the water 8

spectral refractive index (Shettle and Feng, 1979), RH is the relative humidity. Examples of dry 9

refractive indices mj at three wavelengths are given in Table 1. A call to the Mie routine for 10

homogeneous sphere (Bohren and Huffman, 1983) is done for each species and bin to calculate 11

extinction efficiencies (Qext, unitless). The output of AODEM in this case is two 6-D arrays (3 space 12

dimensions, “optical” species, size bin, and time) with number concentrations and extinction (km-1): 13

jkjkwetjkext Nr 2 ,, πσ = (4) 14

plus a 3-D array (2 horizontal dimensions and time) with Aerosol Optical Depth (AOD) obtained as 15

the vertical integral of the extinction of all “optical” species and size bins: 16

dzAODtopz

j kjkext∫∑∑=

0,σ (5) 17

where ztop is the model top height. 18

In case of internal mixing, we average the wet refractive indices of species with a volume weighted 19

approach (e.g. Levoni et al., 1997): 20

∑

∑=

jjk

jjjk

v

mvm (6) 21

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where m is the average complex refractive index of the aerosol, and mj and vjk are the complex 1

refractive index and the volume mixing ratio of j-th species in k-th bin, respectively. The volume is 2

calculated as: 3

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jkjkjk rNv π= (7) 4

where Njk and rjk are the number concentration and the wet radius of j-th species in k-th bin, 5

respectively. In case of “well mixed” hypothesis (denoted hereinafter as internal), a single call to 6

Mie routine for homogeneous sphere is done for each bin. In case of “core-shell” mixing hypothesis 7

(denoted hereinafter as core-shell), we assume an hydrophobic (non hygroscopic) black carbon 8

core, and an hydrophilic well mixed shell. Eq. (6) is used to calculate the refractive index of the 9

shell, then a call to Mie routine for coated spheres (Bohren and Huffman, 1983) is done for each 10

size bin.

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3 Results 13

Here we present the results of AODEM application to a test case in July 2007 over Po valley 14

domain, Northern Italy. Po Valley, and in particular the Milan urban area, has been the location of 15

an intensive campaign in the frame of the QUITSAT project (www.quitsat.it; Di Nicolantonio et al., 16

2009) funded by the Italian Space Agency (ASI). The set up of the chemistry-transport model 17

CHIMERE is reported in section 2.1. 18

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3.1 Vertical profiles 20

In Figure 2 we show the vertical profile of aerosol number concentration on July 13th 2007 over 21

Milan (45.4N, 9.1E) calculated with AODEM from aerosol mass simulated with CHIMERE. The 22

simulated time-height profile (bottom panel) shows a typical daily cycle of aerosol number 23

concentration ( ∑ ∑=j k jkNN , where indices denote j-th species and k-th bin) in Po Valley’s 24

planetary boundary layer (PBL) during summer (e.g. Angelini et al., 2009). During night, maximum 25

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values of N are seen below the low nocturnal PBL. A sharp decrease of N is observed above the top 1

of PBL, while an enhanced feature around 1200 m above ground level denotes the presence of a 2

residual layer of aerosol formed the previous day (Landi et al., 2009). After sunrise, convection 3

starts and aerosols are mixed in the growing PBL, which reach its maximum development in the 4

afternoon. In upper panels, we pick two profiles and show the simulated aerosol size distributions at 5

two heights, one below and one above the top of PBL. At night the two size distributions are 6

different, and then they homogenize as air mixes in the PBL during the day. 7

In Figure 3 we show a qualitative comparison with the range corrected signal (RCS) collected by a 8

commercial LiDAR-Ceilometer (Vaisala LD-40) installed in Milan (Angelini et al., 2009). This 9

instrument sends 855 nm laser pulses in the atmosphere and records the light backscattered from air 10

molecules and particulate matter. At this wavelength the molecular contribution is small, so the 11

returned RCS is roughly proportional to the aerosol backscatter cross section. In the bottom panel, 12

we show the time-height profile of RCS detected on July 13th 2007. The daily cycle of PBL mixing 13

is well visible and follows the same pattern described above for simulated particle concentration. 14

The nighttime residual layer around 1500 m altitude is also visible. In top panels, we show two 15

vertical profiles at 6 UTC and 13 UTC. To ease comparison with model output, the vertical 16

resolution of RCS is degraded to that of CHIMERE. The observation is represented by the median 17

value in a given model layer, with error bars denoting 25th and 75th percentiles. At 6 UTC, both the 18

observed RCS and the simulated extinction coefficient exponentially decrease up to an altitude of 5-19

600 m. Above this height, the LIDAR detects a strong residual layer which is not well captured by 20

the model. A similar discrepancy was also reported by Hodzic et al. (2004) and were attributed to 21

model deficiencies in simulating nighttime advection and mixing. At 13 UTC, vertical profiles are 22

much more homogeneous with height. Both LIDAR and model show an enhanced feature at top of 23

PBL, but the model’s peak is lower than observations by ~500 m. 24

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3.2 Aerosol Optical Depth 1

In Figure 4 we compare the calculated AOD time series for days 13-15 July 2007 with data 2

collected by the sun-photometer located in Modena (44.6N, 10.9E) in the frame of AERONET 3

network (Dubovik et al., 2000; http://aeronet.gsfc.nasa.gov/). Comparison is shown for AOD 4

calculated at two wavelengths (440 and 870 nm) and with the three assumptions on aerosol mixing. 5

Data collected by the sun-photometer at the two selected wavelengths are regarded as the most 6

reliable (Remer et al., 2005). In Figure 5 we compare simulated PM10 at lowest model layer with 7

hourly PM10 observations in Modena (BRACE database, http://www.brace.sinanet.apat.it/). In 8

Figure 6 we compare AODEM output with AOD observed by MODIS/Terra and MODIS/Aqua 9

(ftp://ladsweb.nascom.nasa.gov/allData/5). Model output is sampled at same time and location of 10

satellites overpass, and results are averaged over the regular grid of the model. Statistical indices of 11

the comparisons are reported in Table 1. 12

We generally observe larger discrepancies of model AOD with respect to AERONET observations 13

at 440 nm than 870 nm. In case of internal and core-shell mixing, the model bias is about -25% and 14

-5% at 440 and 870 nm respectively. The temporal correlation is generally very low, <0.1 and <0.2 15

at 440 and 870 nm respectively; one possible explanation could be the limited number of available 16

data. The model displays a larger bias of about 25% at 870 nm with respect to -3% at 440 nm only 17

in the case of external mixing assumption. A general low bias of CHIMERE derived AOD in 18

summer was also reported in a European-scale comparison reported by Péré et al. (2010) and 19

attributed to underestimation of the secondary organic fraction of aerosol. The model does not fully 20

capture the diurnal variability, in particular it underestimates the sharp AOD enhancement of about 21

+80% from the morning values to the afternoon. However, as shown in Figure 5, the model 22

captures the diurnal variability of PM10 observed at ground, thus the bias is most probably related 23

to the aerosol mixing in the PBL, which strongly modify the aerosol vertical profile over Po Valley 24

during the day, especially in summer (Barnaba et al., 2010). 25

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The spatial distribution of daily AOD observed by MODIS is partially reproduced by the model, 1

which displays a spatial correlation of 0.37 and 0.24 with respect to Terra (overpass at 10:30 local 2

time) and Aqua (overpass 13:30 LT) data respectively. The model generally underestimates satellite 3

observations, having a lower relative bias of -25% with external mixing assumption and an higher 4

bias of -41% with core-shell mixing assumption. Most of the negative bias is attributable to the 5

features not captured by the model over the southern part of Adriatic coast and over the eastern part 6

of the domain (Piedmont region). Moreover, the model display a smoother AOD gradient at land-7

sea interface over the northern Adriatic coast. Similar model underestimates with respect to MODIS 8

were also reported by de Meij et al. (2007) in their study over Po Valley, and they pointed out 9

potential important modeling uncertainties in the parameterization of RH dependence and in the 10

specification of aerosol boundary conditions. 11

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4 Discussion and future outlook 13

We presented the first version of a new post-processing tool for aerosol optical calculation from the 14

output of a generic chemistry transport model, called AODEM (Aerosol Optical DEpth Module). 15

Given the output of a CTM and an optical database, AODEM computes the aerosol optical 16

properties for each size bin, time slot, and grid point for a given wavelength. The module allows 17

inspection of the modelled vertical profile of particle number concentration and of aerosol 18

extinction, with three choices on assumed aerosol mixing: external mixing, internal homogeneous 19

mixing, and internal mixing in a core-shell arrangement. In the latter case, we assume a non 20

hygroscopic black carbon core, surrounded by a hygroscopic shell made up by all other species 21

homogeneously mixed. We use the routines developed by Bohren and Huffman (1983) for Mie 22

calculations. 23

Results from a first application of AODEM to CHIMERE model output on a three day summer 24

period (13-15 July 2007) over Po Valley (Northern Italy) are reported. The model qualitatively 25

reproduces the features of aerosol vertical distribution observed over Milan by a LIDAR, in 26

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particular a nighttime residual layer above the PBL (around 1500 m altitude) and a daytime aerosol 1

accumulation just below the PBL top. Simulations are directly compared with Aerosol Optical 2

Depths (AOD) observed at a ground-based AERONET site in Modena and by MODIS instrument 3

onboard Terra and Aqua satellites over Po Valley. The comparison reveal that the model generally 4

underestimates satellite AOD values by at least -25%, but reproduces the broad spatial features of 5

observed AOD. The model also generally underestimates AOD observed from the ground at 6

Modena by -25% and -5% at 440 and 870 nm, respectively, however it captures the increase of 7

AOD by a factor of 3 when moving from 870 nm to 440 nm. Model bias are consistent with 8

previously reported comparison over Po Valley. 9

Future work will better explore the model bias on a longer time scale, and quantitative comparison 10

with other relevant variables will be performed (e.g. aerosol single scattering albedo from 11

AERONET network). Moreover, the LIDAR equation will be solved with simulated optical 12

properties in order to better exploit the unique information provided by this instrument in the PBL 13

(Angelini et al., 2009; Barnaba et al., 2010). Future development of the module will be focused on 14

dust and sea salt optical properites in order to account for non spherical particles, because of the 15

importance of Saharan dust events on the Mediterranean basin. Furthermore, the use of different 16

optical databases (e.g. Hess et al., 1998) and different parameterization of the hygroscopic growth 17

(e.g. Gerber, 1985) will be implemented and tested. 18

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Acknowledgements 20

We would like to acknowledge Gian Paolo GOBBI and Federico ANGELINI at ISAC CNR, for 21

providing lidar data and for their scientific support. The work is part of the Pilot Project QUIT- 22

SAT, funded by the Italian Space Agency (ASI), contract I/035/06/0 – http://www.quitsat.it. 23

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Tables 1 2 Table 1. Physical and optical parameters used for aerosol optical calculations. 3

Species Long name ρ (g/cm3)a eb m(λ=440 nm)c m(λ=550 nm)c m(λ=870 nm)c

BC Black Carbon 1.5 0.285 1.75-i4.5e-01 1.75-i4.4-01 1.75-i4.3e-01

OC Organic Carbon 1.5 0.25 1.53-i5.2e-03 1.53-i6.0-03 1.52-i1.2e-02

SS Sea Salt 2.17 0.285 1.34-i1.0e-09 1.33-i1.9e-09 1.33-i6.3e-07

NSA Nitrate-Sulfate-

Ammonium

1.6 0.285 1.54-i1.0e-07 1.53-i9.9e-08 1.52-i3.6e-07

DUST Soil dust 2.3 0 1.53-i7.8e-03 1.53-i8.0e-03 1.52-i8.0e-03 a particle density, from d’Almeida (1991) 4 b Hanel exponent for hygroscopic growth (see Eq. (2)), from Hodzic et al. (2004) 5 c from Shettle and Fenn (1979) 6

7 8

18

Table 2. Statistical indices of model to observations comparison for days 13-15 July 2007, over the 1

Northern Italy domain. Observations are: ground-based PM10 measurements from the AirBase 2

network (http://air-climate.eionet.europa.eu/databases/airbase/), Aerosol Optical Depths (AOD) 3

observed by a ground-based CIMEL at Modena (AERONET), and AOD observed by satellite-based 4

MODIS instrument (onboard the two spacecrafts Terra and Aqua). 5

Dataset MBa MNBb MEc MNGEd RMSEe rf

AirBase PM10 -9.5 -36.9 10.5 43.8 10.9 0.37

AERONET

λ = 440 nm

Ext -0.017 -3.4 0.050 22.1 0.063 0.08

Int -0.061 -25.8 0.063 26.8 0.084 0.01

C-S -0.067 -28.5 0.067 28.7 0.087 0.01

AERONET

λ = 870 nm

Ext 0.017 24.6 0.002 27.5 0.024 0.17

Int -0.004 -3.7 0.012 16.0 0.014 0.14

C-S -0.006 -7.1 0.012 16.2 0.014 0.15

MODIS/Terra

λ = 550 nm

10:30 LT

Ext -0.034 -25.6 0.054 46.1 0.068 0.38

Int -0.050 -39.3 0.058 47.1 0.073 0.37

C-S -0.052 -41.3 0.059 48.6 0.074 0.37

MODIS/Aqua

λ = 550 nm

13:30 LT

Ext -0.039 -25.1 0.057 44.9 0.077 0.25

Int -0.056 -39.6 0.062 47.1 0.083 0.24

C-S -0.058 -41.6 0.063 47.8 0.084 0.24

6

a mean bias, ∑=

−=N

iii OM

NMB

1

1 , where Mi and Oi denote i-th modelled and observed values, 7

respectively. 8

b mean normalized bias, 10011

×−

= ∑=

N

i i

ii

OOM

NMNB 9

c mean error, ∑=

−=N

iii OM

NME

1

1 10

d mean normalized gross error, 10011

×−

= ∑=

N

i i

ii

OOM

NMNGE 11

e root mean square error, ( )∑=

−=N

iii OM

NRMSE

1

21 12

f spatial (AirBase, MODIS) or temporal (AERONET) correlation 13

14

19

Figure Captions 1

Figure 1. Flow diagram of Aerosol Optical DEpth Module (AODEM). Input and output data are 2

represented in the blue boxes on top and bottom of the diagram, respectively. Black boxes denote 3

the main steps of the algorithm. 4

Figure 2. Simulated vertical profiles of aerosol number concentration (#/m3) over Milan (45.4N, 5

9.1E) on July 13th 2007. In the upper panel, vertical profiles at 6 UTC (left) and 13 UTC (right). In 6

figures inset, the modeled aerosol size distributions extracted at two heights, one above (blue) and 7

one below (red) the planetary boundary layer. In the bottom panel, the simulated time-height 8

profile. 9

Figure 3. In the bottom panel, the Range Corrected Signal (RCS) from LIDAR in Milan. In the 10

upper panels, qualitative comparison of RCS with extinction coefficients calculated with three 11

aerosol mixing hypotheses (external, homogeneous internal, and core-shell internal) at 6 UTC (left) 12

and 13 UTC (right). 13

Figure 4. AERONET level 2.0 Aerosol Optical Depths (AOD) observed in Modena (44.6N, 10.9E) 14

at 440 nm (top) and 870 nm (bottom) compared with model AOD calculated with three aerosol 15

mixing hypotheses. Black squares and vertical bars denote the observed median value, and 25th and 16

75th percentile, respectively. 17

Figure 5. Hourly PM10 concentrations in Modena observed with a station of BRACE network 18

(http://www.brace.sinanet.apat.it/) and simulated with CHIMERE for 14th and 15th July 2007. 19

Figure 6. Average AOD observed by MODIS instrument onboard Terra (top) and Acqua (bottom) 20

satellites during 13-15 July 2007, compared with AOD calculated with three aerosol mixing 21

hypotheses. Model values are sampled at satellites overpass location and time. 22

23

20

1

Figure 1. Flow diagram of Aerosol Optical DEpth Module (AODEM). Input and output data are 2

represented in the blue boxes on top and bottom of the diagram, respectively. Black boxes denote 3

the main steps of the algorithm. 4

5

21

1

Figure 2. Simulated vertical profiles of aerosol number concentration (#/m3) over Milan (45.4N, 2

9.1E) on July 13th 2007. In the upper panel, vertical profiles at 6 UTC (left) and 13 UTC (right). In 3

figures inset, the modeled aerosol size distributions extracted at two heights, one above (blue) and 4

one below (red) the planetary boundary layer. In the bottom panel, the simulated time-height 5

profile. 6

22

1

Figure 3. In the bottom panel, the Range Corrected Signal (RCS) from LIDAR in Milan. In the 2

upper panels, qualitative comparison of RCS with extinction coefficients calculated with three 3

aerosol mixing hypotheses (external, homogeneous internal, and core-shell internal) at 6 UTC (left) 4

and 13 UTC (right). 5

6

23

1

Figure 4. AERONET level 2.0 Aerosol Optical Depths (AOD) observed in Modena (44.6N, 10.9E) 2

at 440 nm (top) and 870 nm (bottom) compared with model AOD calculated with three aerosol 3

mixing hypotheses. Black squares and vertical bars denote the observed median value, and 25th and 4

75th percentile, respectively. 5

6

Figure 5. Hourly PM10 concentrations in Modena observed with a station of BRACE network 7

(http://www.brace.sinanet.apat.it/) and simulated with CHIMERE for 14th and 15th July 2007. 8

24

1

Figure 6. Average AOD observed by MODIS instrument onboard Terra (top) and Acqua (bottom) 2

satellites during 13-15 July 2007, compared with AOD calculated with three aerosol mixing 3

hypotheses. Model values are sampled at satellites overpass location and time. 4

5