We compare tropospheric column densities (vertically
integrated concentrations) of NO

Nitrogen oxides (NO

NO

In this study, we present car-MAX-DOAS observations of tropospheric NO

Here it should be noted that since 2005, ground-based observations are
performed in Paris using a zenith sky instrument (Dieudonné et al.,
2013). From these measurements important information on the seasonal/diurnal
cycle of NO

Compared to previous car-MAX-DOAS measurements around emission sources (e.g. Rivera et al., 2009; Ibrahim et al., 2010; Shaiganfar et al., 2011), our study is special in many respects.

Our car-MAX-DOAS measurements cover many days in two seasons (summer 2009 and winter 2009/2010). Thus with respect to spatial and temporal coverage our comparison between car-MAX-DOAS and satellite observations goes beyond most existing comparisons (e.g. Brinksma et al., 2008; Chen et al., 2009; Hains et al., 2010; Shaiganfar et al., 2011; Irie et al., 2012; Ma et al., 2013; Lin et al., 2014).

We systematically compare our measurements with simultaneous satellite and model data sets.

We make synergistic use of the different data sets by combination their specific advantages. Thus the specific uncertainties of all used data sets are minimised. The regional model is used as a transfer tool to correct for the differences in spatial resolution.

The paper is organised in the following way: in Sects. 2 and 3 we describe the data sets, their specific advantages and limitations and how to use them in a synergistic way. In Sects. 4 and 5 CHIMERE model data are compared to car-MAX-DOAS and OMI observations, respectively. Section 6 presents the comparison of coincident observations of all three data sets. A summary and outlook are provided in Sect. 7.

The car-MAX-DOAS observations in and around Paris were performed during two
extensive measurement campaigns organised in the frame of the MEGAPOLI
project (Mahura and Baklanov, 2012; see also

Car-MAX-DOAS observations were performed by a temperature-stabilised
mini-MAX-DOAS instrument which is described in detail in Wagner et al. (2010),
Ibrahim et al. (2010) and Shaiganfar et al. (2011). Here a brief
overview is given. The MAX-DOAS instrument is mounted on top of a car in
backward direction. The telescope (field of view

The first step of the data analysis comprises the spectral analysis using
the DOAS technique (Platt and Stutz, 2008). The spectral range from 420
to 460 nm was used, and in addition to the NO

Different driving routes in and around Paris were used on different days (Fig. 1). On most days (34 days), measurements around large circles (Fig. 2a) were carried out, usually also including additional measurements closer to the city centre (Fig. 2b). On some days, measurements around smaller circles (Fig. 2c) or following different patterns (Fig. 2d) were also performed.

Typical driving routes around Paris with different radii. The numbers indicate the following – 1: small circle (Périphérique), 2: intermediate circle, 3: large circle, 4: Eiffel tower, 5: Airport, 6: Stade de France, 7: Creteil, 8: Palace of Versailles.

OMI was launched in 2004 onboard the Aura satellite (Levelt et al., 2006).
It measures spectra of light scattered in the Earth's atmosphere and
reflected by the Earth's surface. OMI covers the UV and visible spectral
range up to 500 nm, enabling the DOAS retrieval of ozone, NO

Aura is operated on a sun-synchronous orbit, crossing the equator at 13:45 LT.
Spatial resolution is 13

In this study, we use operational tropospheric NO

Typical car-MAX-DOAS results.

In this paper, simulations are performed with the CHIMERE CTM (Schmidt et
al., 2001; Menut et al., 2013) (

Simulations are performed with a horizontal resolution of 3

Anthropogenic emissions input data are taken from the so-called TNO-MP
(MEGAPOLI) European emission inventory built by the TNO in the framework of
the MEGAPOLI project (Valari and Menut, 2008). It is based on the TNO
inventory (Kuenen et al., 2014) but incorporates, over four megacities in
Europe (Paris, London, Po valley, Rhine–Rhur region), bottom-up emission
data compiled by local authorities (e.g. Airparif in Paris) (Timmermans et
al., 2013). It is characterized by a high spatial resolution, 1/8

Meteorological data are produced with the PSU/NCAR Mesoscale Model (MM5;
Dudhia et al., 1993). MM5 provides meteorological data for CHIMERE in an
hourly time step. Observed wind speeds used for the comparisons are
available at an hourly time step. In general, observed (and simulated) wind
directions smoothly change form 1 hour to the next, at a rate of typically
10–20

Boundary and initial conditions are taken from the LMDz-INCA2 and LMDz-AERO
global models for gaseous and particulate species, respectively
(Hauglustaine et al., 2004; Folberth et al., 2006). Land-use data are taken
from the 1

The three data sets differ in many aspects, the most important properties are discussed below.

Comparison of the tropospheric NO2 VCDs across the Paris
metropolitan area derived from model simulations, satellite observations and
car-MAX-DOAS observations on 16 July 2009.

From the satellite and car-MAX-DOAS observations the tropospheric vertical
column density (VCD) is derived, which is the vertically integrated NO

With typical measurement durations of about 1 min for an individual
observation, the spatial resolution of the car-MAX-DOAS derived NO

For most days the measurement strategy was to drive around Paris along large
circles (with diameters of about 35 km, see Fig. 3), in order to estimate
the total emissions from Paris (Shaiganfar et al., 2011, 2015).
Such measurements were carried out once or twice per day.
In addition, measurements along smaller circles, and other road segments
were performed on individual days to gain more information on the horizontal
NO

OMI satellite observations over the Paris region are made once per day at around 13:45 local time (LT). Car-MAX-DOAS observations were typically made several times per day between about 8:00 and 17:00 LT. CHIMERE data are available in hourly time steps.

The measurement sensitivity of the car-MAX-DOAS and satellite measurements
is systematically different: car-MAX-DOAS observations are most sensitive
for layers close to the surface and become increasingly insensitive for
altitudes above about 3 km (Frieß et al., 2006). In contrast, the
sensitivity of satellite observations decreases towards the surface. This is
accounted for in the satellite NO

The uncertainties of the model data depend on several input parameters, in particular the distribution and strength of the individual emission sources, as well as chemical transformations and atmospheric transport play important roles. The orientation and the extent of the simulated emission plume depend critically on wind direction and speed, respectively.

The sensitivity of satellite observations for tropospheric trace gases is
strongly influenced by clouds. In particular if a trace gas is located below
a cloud layer, the satellite observations can become almost insensitive. The
details of the cloud influence depend on the cloud properties, especially on
the cloud fraction (CF) and cloud altitude. To minimise the cloud influence,
often only measurements for small CF are considered. Here only OMI
measurements with effective CF below 30 % are retained. In contrast to
satellite observations, the sensitivity of car-MAX-DOAS measurements is
hardly affected by clouds as long as the trace gas is located below the
cloud layer (which is a valid assumption close to strong NO

In summary, the characteristics of the three data sets are quite different;
all three data sets have their specific strengths and weaknesses. The main
limitations of the satellite observations are their coarse resolution, their
large uncertainties, and their strong dependence on cloud cover and the
a-priori assumptions on the NO

Same as Fig. 3 for the 25 July 2009. OMI overpass was on 12:47; the car-MAX-DOAS measurements were performed between 8:26 and 14:53; the measurements around the large circle were performed between 11:37 and 14:07.

Overview on the different quantities used in this study.

The main aim of this study is to test the consistency of the three data sets during the two MEGAPOLI measurement campaigns in summer 2009 and winter 2009/2010. The most direct and usually applied way is the comparison of the original data sets. In addition to these basic comparisons, we also compare modified versions of the three data sets making synergistic use of the strengths and weaknesses of the three different data sets. Here modifications of the model data are of particular importance, because (i) the model results depend critically on the used input data, especially the wind fields and the distribution and strength of the emission sources, and (ii) the model data play a crucial role as transfer tool to connect both remote sensing data.

The following corrections to the original data sets are applied in this
study.

The satellite and ground-based observations are used to test if the horizontal patterns of the pollution plumes in the CHIMERE simulation are correct. Possible mismatches in the direction of the emission plume (due to inaccurate wind direction in meteorological input data) are corrected by rotating the modelled concentration fields around the centre of Paris (see Sects. 4.1 and 5.2).

The car-MAX-DOAS data are used to test to which detail the model data can resolve the measured horizontal gradients. Different degrees of spatial smoothing are applied to the car-MAX-DOAS data until best match with the model data is achieved (see Sect. 4.2).

The vertical profiles extracted from the CHIMERE model data are used to improve the satellite retrievals. Compared to the results from the global model used in the original satellite product, the regional CHIMERE model resolves finer spatial gradients (see Sect. 5.1).

Here it should be noted that in cases when rotated CHIMERE data are used
(see point a. above), not the original but the rotated CHIMERE profiles are
applied to the OMI retrieval. This is an important detail, as the NO

In addition, the car-MAX-DOAS data and model results are used for satellite validation with a specific focus on the effects of clouds on the satellite retrievals (see Sects. 5 and 6). And the satellite and model data are used to investigate the representativeness of the car-MAX-DOAS data. The model data are used as a transfer tool to bridge the different scales of the MAX-DOAS and satellite observations (see Sect. 6).

Typical examples for the comparison of the three data sets during the Paris
campaigns are illustrated in Figs. 3 and 4. Besides the original model data
(at 3

Comparison of car-MAX-DOAS observations with model results for 12 February 2010 (left) and 16 July 2009 (right). Top: CHIMERE data at the centre time of the car-MAX-DOAS observations (13:00); middle: CHIMERE data interpolated in space and time to match the individual car-MAX-DOAS observations; bottom: rotated CHIMERE data interpolated to the car-MAX-DOAS observations.

The comparison of the car-MAX-DOAS and model data presented in Fig. 4 again
indicates a spatial mismatch between both data sets: the model data would
have to be rotated counterclockwise by about 25

In this paper we compare the three data sets in a quantitative way for all days with available car-MAX-DOAS measurements. In addition to the original data sets we compare versions which are modified in different ways as indicated above and described in detail in the respective sections. An overview of the different modifications is presented in Table 1.

Comparison of the spatial distribution of NO

Results of the spatial correlation analyses between CHIMERE and
car-MAX-DOAS for individual days (dots: results for original CHIMERE data;
stars: results for rotated CHIMERE data).

In the lower panel of Fig. 5 the CHIMERE data are rotated around the centre
of Paris. The rotation angle was determined by optimising the spatial
correlation between the car-MAX-DOAS observations and the CHIMERE data
(rotation angles are varied in steps of 5

We applied rotations to CHIMERE data for all days when car-MAX-DOAS
measurements around large circles are available. We chose car-MAX-DOAS
measurements around large circles, because these observations allow the most
accurate determination of the emission plume of Paris. Here it should be
noted that instead of rotating the model results, it would have been more
correct to rotate the wind fields before using them in the model simulations
(and leaving the emissions sources unchanged). However, this procedure would
be very time-consuming, since complete model simulations would have to be
performed for each rotation angle of the wind fields. Fortunately, for the
model results the errors caused by rotating the whole wind fields are small,
because the NO

The results of the spatial correlation analyses for the original and rotated
CHIMERE data are shown in Fig. 6. The applied rotations cause a substantial
improvement of the correlation coefficients. While such an improvement has
to be expected (and was the criterion to determine the rotation angles), it
is interesting to note that also the slopes of the regression lines increase
while the

Correlation analyses of original (left) and rotated (right) CHIMERE data versus coincident car-MAX-DOAS observations for 2 selected days. On 16 July 2009 (top) the rotation substantially improves the correlation. On 27 January 2010, the rotation only leads to a slightly improved correlation.

In Fig. 7 we show correlation plots between CHIMERE and car-MAX-DOAS data for 2 selected days: on 16 July 2009 (top) the correlation is largely increased for the rotated CHIMERE data; on 27 July 2009 similar correlation is found for original and rotated CHIMERE data. On both days the slope is close to 0.5 and is hardly affected by the rotation of the CHIMERE data.

The frequency distribution of the optimum rotation angles is presented in
Fig. 8. There, in addition to the results of the comparison to the
car-MAX-DOAS data, the corresponding results for the comparison to the
OMI observations are also shown (note that in contrast to Fig. 6 not only
measurements around large circles are shown). Somewhat different rotation
angles are found for the comparison between car-MAX-DOAS and OMI
observations with a correlation coefficient

Frequency distribution of the optimum rotation angles for the comparison of the CHIMERE data with car-MAX-DOAS observations (blue) and satellite observations (green). Here not only results for large circles (like in Fig. 6), but for all measurements are shown.

Top: comparison of the car-MAX-DOAS results (small circles) with
coincident original or rotated CHIMERE results (squares) for 21 July 2009.
The different plots present car-MAX-DOAS data smoothed by Gaussian functions
with different smoothing kernels (

From the comparison of CHIMERE data to both observational data sets
counterclockwise rotations are found more frequently than clockwise
rotations, with corrections reaching up to 25

In addition to the rotation of the CHIMERE data we also investigated if the
nominal resolution of the CHIMERE data matches the spatial gradients
observed by car-MAX-DOAS. For that purpose we applied a spatial smoothing
(convolution with Gaussian kernels of different widths) to the car-MAX-DOAS
results before they are compared to the CHIMERE data. Two of these
comparisons are shown in Figs. 9 and 10. Both days were chosen because they
represent cases of different improvement after the application of smoothing
and rotation. On 21 July 2009 (Fig. 9) best agreement between both data sets
is found after the car-MAX-DOAS data are smoothed with a kernel of 4 km (in
addition to a rotation of

An overview on the effect of the spatial smoothing for all days is presented
in Fig. 11. The results of the correlation analyses (top: correlation
coefficients

Same as Fig. 9, but for 24 January 2010.

Results of the spatial correlation analyses between CHIMERE and car-MAX-DOAS data for individual days (thin lines) as function of the smoothing kernel (measurements along large circles). The thick lines indicate the averages of the individual days. Left: original CHIMERE data; right: rotated CHIMERE data. The vertical lines indicate the smoothing kernels, for which the highest correlation coefficients are found.

Ratios of daily average and maximum values (CHIMERE/car-MAX-DOAS) as well as slopes and correlation coefficients of the regression analyses for measurements at large circles.

Interestingly, the optimum horizontal smoothing kernels are significantly
larger than the spatial resolution of the CHIMERE data (3 km). This result
was unexpected, and the potential reasons for the need of an additional
smoothing are not completely clear. Probably some atmospheric process(es)
relevant for the dispersion of the NO

Figure 12 presents a comparison of the daily average and maximum values of the
tropospheric NO

Comparison of the daily average

Correlation analyses between CHIMERE and car-MAX-DOAS observations
(along large circles) for

The tropospheric NO

We quantify the agreement of the tropospheric NO

The agreement of the tropospheric NO

Here it should be noted that similar results are found if in addition to the measurements along the large circles also the measurements in the city centre on the same days are considered (Fig. S1a in the Supplement). Also for all coincident measurements similar results are obtained (Fig. S2), but the correlations become worse, because in many cases the rotation angle of the CHIMERE data is less well constrained than for the large circles. Note that for larger smoothing lengths (higher sigma), slopes close to 1 and intercepts close to 0 can be reached (Fig. 11), while correlation coefficients again decrease. However, for lengths above 10 km, the Paris emission plume becomes less and less resolved.

The tropospheric NO

Thus in all further comparisons of this study, in addition to the original DOMINO v2.0 product, we also consider the OMI results retrieved using the CHIMERE profiles (this version is referred to as modified OMI data).

Here it is important to note that, the modified OMI data are partly
dependent on the CHIMERE profiles. In an extreme case, for example, the
application of CHIMERE profiles to a hypothetic homogenous (non-zero) OMI
NO

Comparison of different versions of OMI and CHIMERE data for 28 July 2009. Also shown are a MODIS RGB image and the car-MAX-DOAS results of that day.

Comparison of different versions of OMI and CHIMERE data for 8 February 2010. Also shown are a MODIS RGB image and the car-MAX-DOAS results of that day.

As for the car-MAX-DOAS data, we investigated the effects of rotations of
the CHIMERE results around the centre of Paris on the comparison with the
OMI data. Two examples are shown in Figs. 15 and 16. On 28 July 2009 (Fig. 15)
the emission plume extended in north-east direction. Good spatial
agreement between the model simulations (re-sampled to OMI ground pixel
extent) and satellite observations is found after the model data is rotated
by

Results of the spatial correlation analyses between CHIMERE and OMI
(v2.0 for CF < 30 %) for individual days (dots: results for
original CHIMERE data; stars: results for rotated CHIMERE data).

In Fig. 16 results for 8 February 2010 are shown. Although the CF is
> 30 % for all OMI measurements on that day, we chose this
example to illustrate that even under such unfavourable conditions the
satellite observations can yield useful information on the location of the
emission plume. Like for the previous example, good agreement between both
data sets is found after the CHIMERE data is rotated by

Note that similar comparisons between the three data sets for all days of
both car-MAX-DOAS campaigns are presented in the Supplement (Fig. S4). Here it
should again be noted that for the modified OMI data (see Sect. 5.1) not
the original but the rotated CHIMERE profiles were applied to the OMI
retrieval. This is an important detail, as the NO

Ratios of daily average and maximum values (CHIMERE/OMI) as well as slopes and correlation coefficients of the regression analyses for OMI observations with effective CF below 0.3.

In Fig. 17 the results of spatial correlation analyses between CHIMERE and
OMI for all days with coincident data are shown. Like for the comparison
with the car-MAX-DOAS measurements, for most days the rotation of the
CHIMERE data leads to an improvement of the correlation coefficients (as has
to be expected). However, the improvement of the correlation coefficient is
smaller than for the comparison with the car-MAX-DOAS measurements. Also,
for the slopes and

Figure 18 presents the daily averages and maxima for both data sets for effective CF < 30%. Like for the comparison between car-MAX-DOAS and CHIMERE, the day to day variability is well represented in both data sets. The average ratios of daily maximum and average values are summarised in Table 3. For the ratios of the averages the modification of the OMI data using the CHIMERE profiles has a small effect. The ratio between CHIMERE and OMI data is about 0.76 in summer and 1.00 in winter. Concerning the maxima, the effect of the modification of the OMI data using the CHIMERE profiles is stronger: the ratios between CHIMERE and OMI data decrease from 0.83 to 0.68 in summer and from 0.85 to 0.80 in winter.

Figure 19 presents the correlation results between CHIMERE and OMI for all
individual data pairs. In addition to the original OMI (v2.0) and CHIMERE
data, results for the rotated CHIMERE data and the modified OMI data are also
shown. Again an orthogonal linear regression is performed, where the
uncertainties of the CHIMERE data are described by a constant (2

Comparison of the daily average

After each modification step the correlations between both data sets improve
(

We quantify the agreement of the tropospheric NO

The agreement of the tropospheric NO

If the modified OMI data are considered, the ratios and slopes become even smaller: they are between 0.52 and 0.68 and 0.80 and 0.90 in summer and winter, respectively. This finding reflects the fact that the use of the CHIMERE profiles causes an increase of the OMI results.

Correlation analyses between CHIMERE and OMI observations (for CF
< 30 %) for

Comparison of the daily average

Ratios of daily average and maximum values (modified OMI/car-MAX-DOAS) as well as slopes and correlation coefficients of the regression analyses for measurements at large circles and OMI effective CF below 0.3. For the correction of the car-MAX-DOAS observations see text.

Correlation analyses between OMI observations (modified version for
CF < 30 %) and different versions of car-MAX-DOAS observations
(along large circles).

In this section we first directly compare car-MAX-DOAS to OMI observations. Then we use the CHIMERE model as a transfer tool to correct for the differences in the spatial coverage. For the comparison between car-MAX-DOAS and OMI data we averaged all MAX-DOAS measurements within each OMI ground pixel with effective CF below 0.3, for days where large circles around Paris are sampled. Like for the comparison between CHIMERE and car-MAX-DOAS data (Sect. 4), the choice for large circles was made because for these observations the rotation of the CHIMERE data can be more accurately determined. The rotation angle was determined from the comparison to the car-MAX-DOAS data (see Sect. 4). The (rotated) CHIMERE profiles were used for the modification of the OMI data.

In Fig. 20 the averages of the daily maximum and average values of the
different data sets are compared. Overall, the day to day variability of the
tropospheric NO

In Table 4 the average ratios of the daily maximum and average values are
shown. In summer, similar ratios for the averages and maxima (about 0.90)
are derived for the original and smoothed car-MAX-DOAS data. If, however,
the car-MAX-DOAS data are scaled to full OMI pixels (based on CHIMERE
spatial patterns), the ratios between OMI and car-MAX-DOAS data becomes
close to unity. The correction for the effect of spatial gradients is
performed by multiplying the car-MAX-DOAS data by the average ratio CHIMERE
(OMI)/CHIMERE (DOAS), which is

Summary of the quantitative comparisons between the three data sets for different data selections. The background results are derived from the ratios of the daily averages. The results for the emission plumes are derived from the ratios of the daily maximum values and the correlation analyses of the individual observations. Note that for both data selections the results for the comparison between OMI and car-MAX-DOAS are the same, because CHIMERE data are available for all days.

For winter, again similar ratios for the averages and maxima are found for the original and smoothed car-MAX-DOAS data, but now they are much lower (around 0.60). After correction for the effect of spatial gradients within the satellite ground pixels, the ratios increase from 0.72 to 0.82, but still are systematically below unity, indicating that the OMI results underestimate the car-MAX-DOAS data.

The results of the correlation analyses for individual measurements are shown in Fig. 21. Again an orthogonal linear regression is performed, where the uncertainties of the car-MAX-DOAS data are described by the standard deviation of the individual observations divided by the number of observations inside the OMI pixels. For OMI the individual errors are taken from the DOMINO data product (Boersma et al., 2011).

The different sub-plots show results for different versions of the
car-MAX-DOAS data: original car-MAX-DOAS data (Fig. 21a), smoothed
car-MAX-DOAS data (Fig. 21b) and scaled car-MAX-DOAS (Fig. 21c). Here the
car-MAX-DOAS data are again scaled to the full OMI pixels, but now the
correction is applied for individual car-MAX-DOAS measurements using the
respective ratios CHIMERE (OMI)/CHIMERE (DOAS). The correction of the
car-MAX-DOAS data leads to a much better agreement between both data sets:
the correlation coefficients

We quantify the agreement of the tropospheric NO

The agreement of the tropospheric NO

We also compared the original OMI data (v2.0) to the car-MAX-DOAS
observations (see Fig. S3). Like for the modified OMI data,
better agreement is found after applying the correction for the spatial
gradients inside the OMI ground pixels to the car-MAX-DOAS data. However,
the correlation coefficients and slopes are much smaller than for the
comparison with the modified OMI data, indicating the importance of using
appropriate NO

The data selection in this section is quite different compared to the selections for the bilateral comparisons presented in Sect. 4 and 5. Especially for the comparison between OMI and CHIMERE much larger areas are covered in Sect. 5. Thus it is interesting to see how the different data selections affect the comparison results. In Table 5 the respective ratios of daily averages and maxima as well as the results of the regression analyses are compared for the different data selections.

For the comparison between CHIMERE and car-MAX-DOAS slightly higher ratios
are found if only coincident CHIMERE and car-MAX-DOAS data are compared (the
additional constraint of coincident OMI observations mainly excludes cloudy
situations from the comparison, because only OMI observations with effective
CF below 0.3 are considered). This finding probably indicates that
car-MAX-DOAS observations under mostly overcast conditions slightly
underestimate the true tropospheric NO

For the comparison between CHIMERE and OMI similar ratios are found for
winter, but in summer CHIMERE underestimates the OMI observations more than
for the comparison presented in Sect. 5. This discrepancy is probably
related to the fact that the observations selected in Sect. 5 cover a much
larger area around Paris and indicates that the underestimation of the OMI
data by CHIMERE increases with increasing distance from the emission source.
One possible explanation for this finding is that the underestimation of the
NO

In this study we compared extensive data sets of tropospheric NO

All three data sets all have their specific strengths and weaknesses,
especially with respect to their spatiotemporal resolution and coverage as
well as their uncertainties. Car-MAX-DOAS have rather small uncertainties
and high spatial resolution, but provide only small spatiotemporal
coverage. Satellite observations cover the area of interest on a daily
basis, but with a rather coarse spatial resolution and relatively large
uncertainties. The influence of clouds on the satellite results is strong,
and usually only measurements with low effective CF provide meaningful
information on the tropospheric NO

First we directly compare the original versions of the three data sets.
Here, rather large systematic differences and low correlations are found. In
particular the enhanced NO

CHIMERE model results were rotated around the centre of Paris
until best spatial agreement with car-MAX-DOAS or satellite measurements was
obtained. The observation of a “tilt” between the spatial patterns of
CHIMERE and MAXDOAS are probably related to a mismatch in wind direction in
the MM5 meteorological model. A spatial misplacement of emissions in the
Paris region, in particular as a function of the distance from Paris centre,
is also a possible error source, as has been shown by comparing NO

Car-MAX-DOAS measurements were spatially smoothed until best match with the model data was achieved. The resulting smoothing Kernels of about 6–8 km suggest an effective resolution of CHIMERE of this order.

OMI data were corrected by using the vertical NO

The effect of spatial gradients within the satellite ground pixels on the comparison between car-MAX-DOAS and OMI observations was accounted for and partially corrected using the CHIMERE model data.

The last two points underline the need for a regional model in order to compare MAX-DOAS with satellite measurements in a meaningful way.

Using these modified data sets, the correlation of individual data pairs
largely improved. Also, much better quantitative agreement between the data
sets was found. However, still the satellite observations and the CHIMERE
model results systematically underestimate the car-MAX-DOAS observations
inside the emission plume from Paris, although the underestimation is much
less compared to the original data sets. For the tropospheric NO

From these results we conclude that close to strong emission sources, the
applied improvements of the observational and simulation data sets are
essential for a meaningful quantitative comparison of the tropospheric
NO

One additional interesting finding of our study is that in summer the
underestimation of the OMI observations by the CHIMERE model increases with
increasing distance from the emission source. This finding could indicate a
too low atmospheric NO

We suggest that future studies should use more sophisticated methods for the
extraction of the tropospheric NO

The research leading to these results has received funding from the European
Union's Seventh Framework Programme FP/2007-2011 within the project
“MEGAPOLI”, grant agreement no. 212520. We acknowledge the free use
of tropospheric NO