Introduction
Aerosols play an important role in the atmospheric system. Aerosol particles
scatter and absorb radiation but also affect the formation, optical
properties, and lifetime of clouds and therefore have an impact on the
radiation balance of the Earth's atmosphere. However, the impact of aerosols
on the climate system is still only poorly understood .
Direct emission of soot particles, as well the formation of secondary organic
aerosols and the condensation of atmospheric gases on aerosol particles
(e.g., sulfuric acid or organic vapours), affect air quality and human
health. Various chemical processes in the atmosphere can be strongly affected
by aerosols, since these provide surfaces for heterogeneous reactions.
Examples are the heterogeneous formation of nitrous acid on soot particles
, the autocatalytic release of reactive bromine on sea
salt aerosols in polar regions , and the stratospheric
ozone depletion as a consequence of halogen activation on polar stratospheric
clouds .
A quantification of the optical properties, spatial distribution, and chemical
composition of aerosols is crucial for an understanding of these processes.
Therefore, measurement techniques for the determination of the amount,
vertical distribution, and optical properties of aerosols using relatively
simple and cost-effective instrumentation are highly desirable. Furthermore,
knowledge on the spatial distribution of aerosols and their impact on the
radiative transfer is also important for the interpretation of passive
atmospheric remote sensing observations from ground and satellite. Multi-Axis
Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements allow
for the retrieval of aerosol extinction profiles, and are sensitive to
aerosol microphysical and optical properties, in the planetary boundary
layer. The usage of MAX-DOAS measurements for the retrieval of atmospheric
aerosol properties has found
a growing number of applications during recent years
e.g.,.
As part of these studies, MAX-DOAS aerosol profiles, aerosol optical
thickness (AOT), and/or surface extinction were compared to established
instrumentation, such as lidar, sun photometer, and in situ aerosol
instruments. These intercomparison studies are of great value for the
validation of MAX-DOAS aerosol retrievals but suffer from several
difficulties. A comparison of the AOT from MAX-DOAS and sun photometer does
not allow for a validation of the retrieved profile shape. Compared to lidar
profiles, MAX-DOAS has a much coarser vertical resolution and a different
altitude sensitivity. Backscatter lidar instruments only provide information
on the backscatter signal, and a determination of the actual aerosol
extinction from these measurements is subject to large uncertainties.
Therefore comparisons of backscatter lidar with MAX-DOAS extinction profiles
can generally only be performed on a qualitative basis. Raman lidar systems
can directly measure aerosol extinction profiles but suffer from a low
signal-to-noise ratio during daylight, while MAX-DOAS measurements cannot be
performed at night. A further shortcoming of lidar measurements is the
limited overlap between lidar beam and field of view of the receiving telescope which
leads to a lack of reliable data near the surface where MAX-DOAS is most
sensitive. A comparison of MAX-DOAS measurements with in situ
instrumentation, such as nephelometer and Multi-Angle Absorption Photometer
(MAAP), is complicated by the fact that in situ instruments perform point-like
measurements, usually directly at or near the surface, whereas the aerosol
surface extinction from MAX-DOAS represents an average over a certain height
range with a typical vertical extent of 50–100 m. For this study, these
complications are partly overcome by using a common aerosol inlet at 60 m above ground. The in situ aerosol measurements are therefore expected
to be more comparable to the MAX-DOAS observations than for an inlet directly
at the surface. Most aerosol in situ instruments measure quantities which are
not directly comparable to MAX-DOAS. Aerosols can take up water and therefore
their optical properties – especially the particle light scattering
coefficient – strongly depend on the ambient relative humidity (RH)
. Continuous ground-based measurements by nephelometer
instruments are usually performed on dried air samples. Here a RH-controlled
nephelometer is used to retrieve the ambient value in addition to dry
particle light absorption measurements . A general problem of comparisons between remote sensing and
in situ observations is that MAX-DOAS usually measures different air masses,
with the retrieved aerosol profiles being representative for an average over
the light paths in the lowermost troposphere that extend horizontally over
several kilometres.
Here we present first direct intercomparisons of aerosol extinction profiles
retrieved using MAX-DOAS measurements and aerosol retrieval algorithms from
several groups. The measurements were performed in the framework of the
Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments
(CINDI) at the Cabauw Experimental Site for Atmospheric Research (CESAR) in
the Netherlands (51.97∘ N, 4.93∘ E), during June/July 2009. An
overview of the campaign as well as details of the instrumentation and DOAS
data analysis can be found in and . In
total, 22 instruments from 14 institutes participated in the campaign, of
which five participants delivered data on the aerosol vertical distribution
or on AOT. During CINDI, MAX-DOAS measurements were performed continuously by
all instruments in a west-north-westerly direction (around 287∘ azimuth
angle). The nominal set of elevation angles included 90, 30,
15, 8, 4, and 2∘, but some instruments also
observed skylight from additional directions. A primary objective of CINDI
was the intercomparison of the differential slant column densities (dSCDs) of
NO2 and the oxygen collision complex O4 measured by MAX-DOAS. A
previous study has demonstrated that the O4 dSCDs from the different
instruments participating in the CINDI campaign, which serve as input for the
aerosol retrieval algorithms, show good agreement .
Therefore, a comparison of aerosol properties derived from the measured O4
dSCDs allows us to investigate differences in the various retrieval algorithms,
which use a variety of different approaches, as well as the choice of
different retrieval parameters (e.g., the a priori).
Retrieval of atmospheric aerosol properties from MAX-DOAS
MAX-DOAS measurements of scattered sunlight yield dSCDs, i.e. the difference
dS(α)=S(α)-Sref between the slant column density of
atmospheric trace gases measured at an elevation angle α (angle
between the horizon and the line of sight, LOS) and a reference measurement
Sref. For aerosol and trace gas retrievals, usually a zenith sky
measurement of the same elevation sequence, i.e. closest in time to the
off-axis measurements, is chosen as reference. The slant column density
represents the integrated trace gas concentration along the light path, S=∫ρ(s)ds, with the integral representing the weighted average over
individual light paths through the atmosphere. The oxygen collision complex
O4 exhibits pronounced absorption structures in the UV/vis spectral region
. Since its concentration is proportional to the
square of the O2 concentration, which is well known, variations in the
O4 dSCDs are caused by variations in the atmospheric light path, which is
altered by the presence of aerosols. Therefore, measurements of the oxygen
collision complex O4 at different LOS allow for the retrieval of
atmospheric aerosol properties. Alternatively, or in addition to the O4
dSCDs, relative intensities, i.e. the ratio of the detector signal measured
in the zenith and in off-axis directions, can be used to retrieve atmospheric
aerosol properties .
Since MAX-DOAS measurements only contain indirect information on the aerosol
vertical profile, inverse methods are necessary for the retrieval procedure
. In general, aerosol properties are derived by comparing
the measured O4 dSCDs (and/or relative intensities) at different elevation
angles to simulations from radiative transfer models (RTMs). Using nonlinear
inversion algorithms, the aerosol properties that serve as input for the RTM
are altered until best agreement between measurement and simulation is
achieved. A general problem that MAX-DOAS has in common with other
atmospheric remote sensing techniques is the limited information content of
the measurements. As a consequence, the full state vector (e.g., an aerosol
extinction profile k(z) at high vertical resolution) cannot be
reconstructed without any further constraints to the results. Here, different
approaches are possible: either a Bayesian approach is applied where
additional constraints are posed in the form of an a priori state vector or
a parametrisation with only a small number of quantities describing the
aerosol vertical distribution (e.g., the AOT or
the layer height and AOT of a box profile) is used. The solution of the
former approach is retrieved using the well-known optimal estimation method
(OEM) , whereas the latter approach is based on more
simple least-squares methods (LSMs). In general, the solution of the inverse
problem x^ is determined by minimising a cost function in the
form of
χ2=(y-F(x,b))TSϵ-1(y-F(x,b))+(x-xa)TSa-1(x-xa).
Main features of the different retrieval algorithms.
Participant
Method1
Measurement
Wavelength
Retrieved quantities
Vertical grid
Sampling interval
O4 correction
factor
AIOFM
OEM
O4 and intensity
477 nm
Extinction profile
200 m
≈7 min
0.8
BIRA
OEM
O4 dSCDs
477 nm
Extinction profile
200 m
≈20 min
0.8
Heidelberg
OEM
O4 dSCDs
477 nm
Extinction profile
200 m
20 min
1.0
JAMSTEC
OEM
O4 dSCDs
477 nm
Extinction profile
1000 m
30 min
variable2
MPIC
LSM
O4 dSCDs
360 nm
Layer height & AOT
n/a
≈15 min
0.77
1 OEM: optimal estimation method; LSM: least-squares method2 see
F(x,b) is a forward model (here an RTM), which describes the measurement y (the O4 dSCDs and/or
relative intensities) as a function of the atmospheric state x
(the aerosol vertical profile). The vector b represents additional
forward model parameters (e.g. aerosol single scattering albedo (SSA) and phase
function) which are not retrieved. In the case of OEM algorithms, the a priori
state vector xa with covariance Sa serves as an
additional constraint, which has to be considered because the information
content of the measurement is usually too low to allow for a full
reconstruction of the atmospheric state on the basis of the measurements
only. In the case of LSMs, the a priori information represented by the second term
in Eq. () is omitted (i.e., Sa-1≡0), and
only a small number of parameters (i.e., layer height and AOT) are retrieved.
The covariance matrix Sϵ describes the uncertainties in
the measurement (in the case of LSMs sometimes set to unity when no error weighting
is performed). The vertical resolution of the retrieval is quantified by the
so-called averaging kernel matrix A=∂x^/∂x, which represents the sensitivity of the retrieved
profile as a function of the true atmospheric profile. The retrieved profile
x^ can be represented as the true profile x,
smoothed by the averaging kernel matrix A according to
x^=xa+A(x-xa).
The general features of the different algorithms participating in the
intercomparison are summarised in Table , and the
individual retrieval algorithms are briefly described in the following
sections. O4 dSCDs or relative intensities measured at several elevation
angles, relative to a zenith sky spectrum of the same sequence, serve as
input measurement vector. Some participants (Heidelberg and JAMSTEC) do not
use single elevation sequences but rather all observations within a fixed time
period (20 and 30 min, respectively) as input vector. All participants
except the Max Planck Institute for Chemistry (MPIC) use OEM algorithms for
the retrieval. MPIC uses an LSM algorithm for the retrieval of AOT and
aerosol layer height (see Sect. ). For the intercomparison, a
reference wavelength of 477 nm has been chosen since most of the
participants use the O4 absorption band at this wavelength for the aerosol
retrieval. Aerosol properties measured at other wavelengths (retrievals from
MPIC, as well as ceilometer, sun photometer, and humidified nephelometer) are
converted to 477 nm using the Ångström coefficient α derived
from co-located sun photometer measurements at wavelengths of 440 and 675 nm.
In contrast to all other retrieval algorithms, the Anhui Institute of Optics
and Fine Mechanics (AIOFM) uses observed relative intensities in addition to
O4 dSCDs as input vector (for details see Sect. ).
Furthermore, AIOFM did not participate in the CINDI campaign with their own
instrument but rather use data measured by the Heidelberg instrument as input for
their own retrieval algorithm.
The a priori profiles for the BIRA, Heidelberg, AIOFM and JAMSTEC retrievals
are shown in Fig. . Heidelberg and AIOFM use similar a
priori profiles with an aerosol extinction at the surface of 0.1
and 0.08 km-1, respectively, and a linear decrease with altitude. The
BIRA algorithm assumes a significantly smaller a priori aerosol extinction,
with a surface value of 0.05 km-1 and an exponential decrease with
altitude. The JAMSTEC algorithm represents the aerosol profile on a much
coarser vertical grid than the other algorithms using three layers of 1 km
thickness each and assumes a larger a priori extinction with a value of
0.126 km-1 in the lowermost layer. More specific information on the
choice of the a priori profiles and the a priori covariance matrices can be
found in the following sections. Depending on the information content of the
measurements, or more specifically the values of the averaging kernels in
each layer, there will be a potential bias of the retrieved aerosol
extinction profiles towards the a priori profiles (see Eq. ).
This influence of the a priori profile on the resulting extinction profiles
needs to be considered when comparing the results from the different
retrieval algorithms.
A priori profiles for the BIRA, Heidelberg, AIOFM, and JAMSTEC
retrievals. The symbols indicate the centre of each retrieval layer. The
BIRA, Heidelberg, and AIOFM algorithms use a 200 m vertical grid with constant
extinction in each layer, and JAMSTEC a 1 km grid with exponentially
decreasing extinction in each layer.
Based on aerosol profiles scaled by the AOT of a co-located sun photometer,
it has been found by that measured O4 dSCDs are
significantly lower than simulated dSCDs, and it was suggested to multiply
the measured dSCDs with a constant scaling factor of 0.8 prior to the
retrieval to resolve this disagreement. The observed discrepancy is
potentially caused by uncertainties in the absolute value of the O4 cross
section, possibly owing to a limited knowledge of the temperature dependence
of the absorption strength. However, a recent study by on
the basis of direct sunlight DOAS measurements indicates that measured O4
DSCDs are in very good agreement with theoretically expected values without
applying any correction factor, and suggest that elevated
aerosol layers might cause the observed discrepancies. The different
correction factors applied to the O4 dSCDs in the present study are listed
in Table . The values correspond to what the individual
groups consider as their “best setting”. BIRA and AIOFM apply a correction
factor of 0.8, MPIC a factor of 0.77, and JAMSTEC has implemented a variable
correction factor which is part of the state vector and is retrieved by
their algorithm for details see.
The BIRA retrieval algorithm
The BIRA-IASB OEM-based profiling tool called bePRO is extensively described
in and . The forward model is the
linearised discrete ordinate radiative transfer (LIDORT) model
. The LIDORT code includes an analytical calculation in a
pseudo-spherical geometry of the weighting functions needed for the profile
inversion. This allows for near-real-time automated retrievals of aerosol and
trace gas vertical distributions without the use of pre-calculated lookup
tables. The standard vertical grid implemented in bePRO consists of 10
layers of 200 m thickness between 0 and 2 km, 2 layers of 500 m between 2
and 3 km, and 1 layer between 3 and 4 km altitude. Pressure and temperature
profiles are taken from the Air Force Geophysics Laboratory (AFGL) database.
For each scan, the O4 dSCDs measured at 477 nm at nine elevation angles
(1, 2, 3, 4, 5, 8,
10, 15, and 30∘ serve as the measurement vector
y. The corresponding Sϵ matrix is constructed
as a diagonal matrix, with variances equal to the square of the O4 DOAS
fitting error. A correction factor of 0.8 has been applied to the dSCDs
. An exponentially decreasing aerosol extinction profile
with an AOT of 0.05 and a scaling height of 1 km is used as a priori. The a
priori error covariance matrix is set as in : the diagonal
element corresponding to the lowest layer, Sa(1,1), is equal to
the square of a scaling factor β (β=0.1 in the present case)
times the maximum partial AOT of the profile. The other diagonal elements
decrease linearly with altitude down to 0.2⋅Sa(1,1). The
off-diagonal terms in Sa are set using Gaussian functions with a
correlation length of 50 m. The aerosol SSA and
phase functions needed for the weighting functions calculations are derived
offline based on the co-located AErosol RObotic NETwork (AERONET) sun photometer measurements. The
surface albedo is fixed to 7 %.
The Heidelberg retrieval algorithm
The HeiPro retrieval is an updated version of the algorithm already described
in detail in and . It is based on the
optimal estimation method and retrieves the most probable state vector by
minimising the cost function given by Eq. (). The radiative
transfer model SCIATRAN serves as forward model for the
retrieval. The state vector x consists of the logarithm of the
extinction in 20 layers of 200 m thickness, extending from the surface up to
4 km altitude. Using the logarithm of the extinction instead of the actual
extinction has the advantage that negative values are avoided, which cannot
be processed by the RTM. The O4 dSCDs measured at
477 nm at elevation angles of 30, 15, 8, 4, and
2∘ serve as the measurement vector y, and the diagonal
values of the measurement covariance matrix Sϵ are set to
the square of the dSCD measurement errors. In contrast to the other
groups, no correction factor was applied to the dSCDs prior to the retrieval
with the HeiPro algorithm. All measurements within a fixed time interval of
20 min serve as measurement vector. Given a measurement time of about 7 min for a single elevation sequence, this means that the measurement
vector usually contains several measurements at the same elevation angle. The
a priori has an extinction of 0.1 km-1 at the surface, is linearly
decreasing from the surface up to an altitude of 3.5 km, and is constant
above this
with a value of 0.0033 km-1. This a priori profile has been smoothed
with a seven-point running average. The a priori error (square root of the
diagonal elements of Sa) has been set to 100 % of the a priori
at all altitudes, and the non-diagonal elements of Sa are
exponentially decreasing with distance between layer altitudes with a
correlation length of 1 km. For the radiative transfer calculations, aerosol
SSA, and asymmetry parameter g were adapted from the
co-located AERONET sun photometer measurements. For this intercomparison, all
profiles retrieved by the Heidelberg groups were used without any further
quality filtering of the data.
The JAMSTEC retrieval algorithm
The Japanese MAX-DOAS profile retrieval algorithm version 1 (JM1) applied to
MAX-DOAS observations of O4 at elevation angles of 2, 4,
8, 15, and 30∘ performed by JAMSTEC is described in
detail in . It is based on the optimal estimation method to
solve the nonlinear inversion problem. The state vector consists of AOT and
three parameters determining the shape of the vertical profile. An advantage
of this parametrisation is that the absolute value of the aerosol extinction
is unnecessary in the state vector. Instead, a priori knowledge of the
profile shape is needed. The aerosol extinction is given as the product of
the AOT and profile shape but the aerosol extinction retrieval is less
subject to a prior knowledge of the AOT and profile shape as the resulting a
priori error for the aerosol extinction is large. The adopted
parametrisation primarily yields partial AOT values or mean aerosol
extinction values for layers of 0–1, 1–2, 2–3, and 3–100 km. Since a vertical
profile shape within each layer is considered, extraction of aerosol
extinction coefficients at any altitude is possible . A
lookup table of the box-air-mass-factor vertical profile used in the forward
model was created using the JACOSPAR RTM, which was
developed based on its predecessor MCARaTS (the Monte Carlo Atmospheric
Radiative Transfer Simulator; ). Parameters for the
JAMSTEC retrieval were 0.95 for the single scattering albedo, 0.65 for the
asymmetry parameter (under the Henyey–Greenstein approximation), and 0.1 for
the surface albedo. Instead of applying a constant correction factor to the
measured O4 dSCDs, a variable correction factor is applied which is part
of the state vector and thus retrieved by the algorithm (for further details
see ).
The MPIC retrieval algorithm
The MPIC profile inversion is described in detail in . It
is based on the comparison of the measured O4 absorption (analysed using
the absorption bands at 360 and 380 nm in a joint fitting window ranging from
353 to 390 nm) at elevation angles of 2, 4, 8,
15, and 30∘ with simulated O4 differential air-mass factors
(dAMFs). The retrieved O4 dSCDs are converted into dAMFs by dividing them by
the atmospheric O4 vertical column density (VCD). From vertical profiles
of temperature and pressure at Cabauw the O4 VCD was determined to 1.32×1043 molec2 cm-5 for the units,
see. Finally, the O4 dAMFs are scaled by a constant
factor of 0.77. The MPIC retrieval follows the method of with
slight modifications described in . Atmospheric O4
dAMFs
are simulated using the radiative transfer model McARTIM
, assuming a large variety of atmospheric aerosol
extinction profiles, which are described by a simple parametrisation scheme:
for the CINDI campaign the total aerosol optical depth and the layer height
were varied. The profile shape is composed of two parts: a box profile from
the surface to the layer height with a constant aerosol extinction and an
exponentially decreasing part above (5 % of the total AOT are contained in
this exponentially decreasing part). From a least-squares fitting procedure
between the measured and simulated O4 dAMFs, the total aerosol optical
depth and layer height of the box profile are determined for each elevation
sequence. The aerosol extinction is derived by dividing the AOT of the box
profile (95 % of the total AOT) by the layer height. The errors of the
retrieved profiles are assessed based on (a) the residual sum of squares
between the measurement and the model results and (b) from the fit process
itself, taking into account the sensitivity of the measured quantities with
respect to variations of the profile parameters.
The AIOFM retrieval algorithm
The “Profile inversion algorithm of aerosol extinction and trace gas
concentration developed at AIOFM in cooperation with MPIC” (PriAM)
is applied to the O4 dSCDs and relative intensities from
Heidelberg MAX-DOAS instrument at elevation angles of 2, 4,
8, 15, and 30∘ to retrieve profiles of aerosol
extinction. The PriAM algorithm is based on the optimal estimation method
and implements a nonlinear iterative approach which is
based on the Gauss–Newton method modified by Levenberg–Marquardt to speed up
the minimisation of the cost function. The measurement vector y
consists of the O4 dSCDs and relative intensities at 477 nm in each
measurement sequence. The measured O4 dSCDs are scaled down by a factor of
0.8. Including relative intensities is a strong constraint for the AOT and
improve the sensitivity of the inversion on the upper layers
. The a priori profile xa is a linear
decreasing profile with an AOT of 0.15. A priori uncertainty covariance
matrix Sa is non-diagonal with the diagonal elements of the
square of 33 % of xa and non-diagonal elements calculated from
the Gaussian function with the correlation length of 0.5 km
. A diagonal measurement uncertainty covariance matrix
Sϵ has the diagonal elements of the square of 100 %
fitting errors of the O4 dSCDs and 1.5 % of the relative intensities. Due
to the deviation of the true aerosol phase function from the
Henyey–Greenstein parametrisation (Henyey and Greenstein, 1941) used in the
model simulations for the forward scattering, artifacts
occur in the retrieved aerosol profiles at small relative azimuth angles when
including intensity. Considering this effect, the error of the intensity has
been increased from 1.5 to 3 % in the afternoon. This effectively decreases
the weight of the information from relative intensities compared to the
information from O4 dSCDs. For the measurements on 1 and 2 July, the
relative intensity has been excluded from the retrieval for relative azimuth
angles below 20∘. The weighting function K is calculated
using the full-spherical RTM SCIATRAN 2.2 .
Complementary measurements
A large variety of aerosol measurements, both in situ and by remote sensing,
were performed during the CINDI campaign: backscatter and Raman lidar systems
as well as a ceilometer measured the vertical distribution of aerosol in
terms of backscatter and extinction profiles; two nephelometer systems, one
of which was humidity controlled, and a multi-angle absorption
photometer measured the scattering and absorption properties of aerosol
particles; finally, a sun photometer measured the AOT.
Backscatter profiles measured by a Vaisala LD40 ceilometer regularly operated
at the CESAR site by KNMI are used for the validation of the aerosol profiles
retrieved from MAX-DOAS. The ceilometer has a vertical resolution of 30 m and
measures backscatter profiles every 30 s at a wavelength of 905 nm from
about 120 m up to 11.5 km altitude using a pulsed InGaAs laser diode. Due to
the limited overlap between outgoing laser beam and the field of view of the
collecting telescope, no valid backscatter data are available for altitudes
below 120 m.
The AOT at 440, 675, 870, and 1020 nm, as well as the corresponding
Ångström parameters, SSA, and phase function, are
retrieved from continuous measurements at the CESAR site by an automated
CIMEL CE 318 sun photometer using direct sunlight measurements. This
instrument, operated by TNO, is part of the AERONET. A summary of the AERONET Level 2 data during the “golden days” of
CINDI campaign, which were characterised by predominantly clear sky
conditions (see Sect. ), is shown in Fig. . The AOT varies between 0.1 and 0.7, with a mean and
standard deviation of 0.34 and 0.18, respectively. The Ångström
exponent, which describes the wavelength dependence of the aerosol
extinction, is 1.49±0.14. The aerosol SSA during
CINDI is significantly lower than at other urban sites ,
with values as low as 0.84 at the beginning of the campaign and a mean value
of 0.92±0.03, indicating that significant amounts of absorbing
particles are present. Furthermore, a mean asymmetry parameter of 0.72±0.02 has been retrieved from sun photometer measurements.
The aerosol scattering coefficient ks near the surface was determined by a
humidified nephelometer (WetNeph) in combination with a simultaneously
operated dry-air nephelometer. The WetNeph is described in detail by
, and a comparison of extinction coefficients
from MAX-DOAS and WetNeph has already been described in .
Briefly, the aerosol scattering coefficient ks as well as the back
scattering coefficient kb are measured at three wavelengths (450, 550, and
700 nm) at defined relative humidities between 20 and 95 % using an
integrating nephelometer (TSI Inc., model 3563). The WetNeph measurements
allow the determination of the ambient particle extinction coefficient,
assuming that the particle absorption coefficient does not change with RH.
The ambient particle extinction coefficient can then be directly compared to
the retrieved value of the MAX-DOAS without any further assumption on
particle growth in humid air. The ambient RH measurements were taken at six
different locations on the 200 m high mast. The inlet of the WetNeph was
located at a height of 60 m at the Cabauw tower.
Summary of the AERONET data obtained during the golden days of the
CINDI campaign. Top: single scattering albedo ω and asymmetry
parameter g at 441 nm from almucantar measurements. Bottom: AOT at 440 nm
and Ångström coefficient α retrieved from direct sunlight
measurements at 440 and 675 nm.
Results
In this section, quantities derived from the different aerosol retrieval
algorithms are validated against independent measurements. Aerosol extinction
profiles are compared to ceilometer measurements in
Sect. . The comparison of retrieved AOT and
surface extinction with data from sun photometer and in situ aerosol
observations, respectively, is discussed in Sect. .
For the comparison, 8 days with predominantly clear sky conditions
(“golden days”) were selected. These were 23–25 June and 30 June to 4
July 2009.
Comparison of aerosol vertical profiles
In order to assess the ability of the different retrieval algorithms to
determine the general structure of the boundary layer, aerosol vertical
profiles are compared to backscatter profiles from a co-located ceilometer
instrument. For this comparison, it is important to consider that MAX-DOAS
measurements have a relatively low information content. The number of
independent pieces of information from the measurement, quantified by the
degrees of freedom for signal (DFS), typically ranges between 1 and 2. An
example for aerosol extinction averaging kernels, taken from the Heidelberg
retrieval, is shown in Fig. . The averaging kernels
indicate that information on the extinction profile can be retrieved only for
the lowermost 2 km of the atmosphere with highest sensitivity at the ground,
where the vertical resolution (quantified by the altitude where the averaging
kernel of the lowermost layer is half of its surface value) is ≈500 m. The DFS strongly varies with visibility and is also a function of solar zenith angle (and
to a smaller extent SSA); it is 1.9 for the example in
Fig. .
Example for aerosol extinction averaging kernels from the Heidelberg retrieval algorithm for 2 July 2009, 12:00 UTC.
Since the ceilometer backscatter profiles are characterised by a much higher
vertical and temporal resolution than MAX-DOAS measurements, 20 min
averages of the ceilometer profiles were degraded to the sensitivity of the
Heidelberg MAX-DOAS profiles according to the method described by
. The degraded backscatter profile x′=A⋅x, with A being the averaging kernel
matrix and x the original backscatter profile in high resolution,
represents the profile x′ that would have been retrieved with
MAX-DOAS if the true profile were x. Note that the original
equation from , x′=xa+A⋅(x-xa), cannot be applied here since the
backscatter profiles x and the a priori extinction profiles
xa are measured in different physical units. The ceilometer data
have been averaged to the vertical grid of the MAX-DOAS retrieval (200 m)
prior to the convolution with the averaging kernel. No or only limited
overlap between outgoing beam and field of view of the telescope of the
ceilometer is present in the lowermost 120 m. For this reason, ceilometer
data between surface and 150 m altitude are set to a constant value equal to
the signal at 150 m during the convolution process. Therefore the lowermost
layer of the convolved ceilometer profiles is subject to large uncertainties
when high gradients near the surface exist. It is important to note that
ceilometer and MAX-DOAS instruments retrieve different quantities. The
MAX-DOAS retrieval algorithms yield extinction profiles, whereas the
backscatter profiles from the ceilometer cannot be directly converted to an
extinction profile without further assumptions on the ratio between
backscatter and extinction. This so-called lidar ratio is not known a priori
and is a function of the size and optical properties of the particles, which
vary with time and altitude. Therefore ceilometer and MAX-DOAS profiles can
only be compared qualitatively in terms of the vertical structure of the
boundary layer. Furthermore, the MAX-DOAS instruments average over a large
horizontal distance of up to several tens of kilometres, whereas the
ceilometer probes the atmosphere directly over the measurement site.
The MAX-DOAS extinction profiles from the different groups together with the
ceilometer backscatter profiles for the golden days are shown in Figs. –. Note that BIRA,
Heidelberg, and AIOFM retrieve the aerosol extinction on a vertical grid of
200 m, whereas JAMSTEC represents the profile on four layers of 1 km
thickness each, and MPIC retrieves the height and AOT of a box profile with a
constant extinction from the surface up to a certain altitude (as well as an
exponentially decreasing profile above, which contains 5 % of the AOT). The
gaps in the datasets are caused by different quality filters applied by the
different groups and by missing data around noon, when reference measurements
were performed.
In general, the vertical structure of the aerosol profile in the boundary
layer of all groups shows good agreement with the ceilometer backscatter
profiles, in particular after these are degraded to the MAX-DOAS vertical
resolution by convolution with the averaging kernel. The temporal variation
of the MAX-DOAS profiles is in good agreement with the ceilometer data, and
the height of the boundary layer is generally captured very well in
qualitative terms.
Comparison of MAX-DOAS extinction profiles with backscatter profiles
from the ceilometer for 23 June (left) and 24 June (right). The top panel shows
the backscatter signal in original vertical resolution, the second panel the
backscatter signal with the averaging kernels of Heidelberg applied, and
below that
the extinction profiles retrieved from BIRA, Heidelberg, JAMSTEC, and MPIC.
For MPIC box-profiles with the retrieved layer height and AOT are plotted.
Same as Fig. but for 25 June (left) and 30 June (right).
Same as Fig. but for 1 July (left) and 2 July (right).
Same as Fig. but for 3 July (left) 4 July (right).
Webcam images from the mornings of 30 June and 1 July 2009, when
foggy conditions prevailed. The webcam pointed to the viewing direction of
the MAX-DOAS Instruments (north-east). The devices in the foreground are the
telescopes of the Heidelberg group.
Diurnal variation of the degrees of freedom for signal from the Heidelberg retrieval for 30 June as well as 1 and 4 July 2009.
23 and 24 June are characterised by a relatively low extinction (<0.4 km-1), with an increase both in boundary layer height and in extinction
in the early afternoon. These features are captured well by all groups. An
enhanced backscatter at ≈1.5 km altitude in the early afternoon of
23 June, probably due to clouds, is captured by the retrievals of BIRA,
Heidelberg, AIOFM, and JAMSTEC, which show uplifted layers of enhanced
extinction during this period (no data are reported for this period by MPIC).
However, as a consequence of the limited information content of MAX-DOAS
measurements, these layers are smeared out over a layer extending from 200 m to 1.2 km. A similar situation with an uplifted aerosol layer in the early
afternoon occurs on 25 June. After 06:00 UTC on 25 June, a cloud is observed
by the ceilometer at an altitude of ≈2 km, which is still visible
after convolution with the averaging kernel. The finding that none of the
MAX-DOAS retrievals captures this cloud might be due to the fact that it is
localised directly over the measurement site, whereas the MAX-DOAS extinction
profiles are representative for the atmosphere in a distance of several
kilometres along the LOS. In fact, the Heidelberg and AIOFM
profiles exhibit layers of enhanced extinction (≈0.15 km-1)
between 0.5 and 2 km throughout the morning of 25 June, which probably
correspond to the cloud layer observed by the ceilometer in zenith between
06:00
and 08:00.
As can be seen from the webcam images in Fig. that foggy
conditions prevailed during the mornings of 30 June and 1 July. The
ceilometer backscatter profiles show that these thin fog layers with a
vertical extent in the order of 100 m were initially located very close to
the surface and then uplifted during the course of the morning. Note that the
backscatter profiles smoothed with the MAX-DOAS averaging kernel do not show
an enhanced extinction in the early morning of 30 June because the fog layer
was located at altitudes below 150 m and was therefore not considered in the
smoothing procedure. These foggy conditions allow for an investigation of the
behaviour of the retrieval algorithms in the presence of a layer of high
extinction at different altitudes. As shown in Fig. ,
the diurnal variation of the DFS in the presence of fog is similar to the
clear sky case. An enhanced extinction in the morning due to fog is detected
by all retrieval algorithms. However, the limited vertical resolution in the
presence of fog leads to a strong overestimation of the vertical extent of
the extinction layer. On 30 June, the fog layer present during the early
morning hours is blurred over an altitude of 0.5 km by BIRA, MPIC, and AIOFM
and 1 km by Heidelberg and JAMSTEC, respectively. On 1 July, the retrieved
fog layer extends up to 1.3, 1.8, and 1–2 km for the BIRA, Heidelberg,
and MPIC retrievals, respectively, whereas the fog layer detected by the
ceilometer was located below 500 m until 09:00 UTC. The vertical profiles
retrieved by BIRA, Heidelberg and AIOFM are, however, qualitatively in good
agreement with the expected profile as given by the ceilometer profiles
smoothed with the MAX-DOAS averaging kernels. None of the algorithms are able
to reproduce the elevated extinction layers occurring after the uplift of the
fog layers in the course of the mornings of 30 June and 1 July. This might be
caused by the general enhancement in extinction throughout the boundary layer
on these 2 days, which could lead to a reduced sensitivity for higher
altitudes. This is in contrast to the situation on 25 June, when elevated
layers could be detected during conditions of lower aerosol load.
Although the BIRA, Heidelberg, and AIOFM algorithms are very similar in terms
of the parametrisation of the aerosol profile, the resulting profiles exhibit
some differences, which can be caused either by a different choice of the a
priori profiles and a priori covariance matrices or, in the case of BIRA, by a larger number of elevation angles with a higher sensitivity
near the surface due to the inclusion of measurements at 1∘ elevation.
A persistent feature of the BIRA profiles is a reduced extinction in the
lowermost (0–200 m) layer with significantly smaller values than in the
layers above, even when the ceilometer indicates a homogeneous distribution in
the boundary layer (e.g., on 24 and 25 June). However, the ceilometer does
not have any information on altitudes below 150 m, and it might well be that
the surface acts as a sink for aerosols or that increased RH
leads to larger particles and thus higher extinction at higher altitudes. In contrast, the BIRA and AIOFM algorithms seem to be able to capture
uplifted layers or clouds better than the Heidelberg and JAMSTEC algorithms,
e.g. in the afternoon of 30 June and the midday of 1 July. Both BIRA and
JAMSTEC detect an uplifted layer of enhanced extinction the morning of 2
July, when clouds were present, a feature that is not captured by the
Heidelberg and AIOFM algorithm. In some cases, such as the late afternoon of
2 and 3 July, the AIOFM profiles show an enhanced extinction between 1 and
2 km altitude, where the ceilometer also detects enhanced backscatter, probably
due to clouds at the top of the boundary layer. This enhanced sensitivity for
clouds at higher altitudes is probably due to the fact that the AIFOM
algorithm includes relative intensities in addition to O4 dSCDs in the
measurement vector, which render the algorithm more sensitive to enhanced
extinction at higher altitudes . The AIOFM profiles
exhibit a somewhat higher temporal variability, which is either due to the
shorter time interval for each profile (about 7 min compared to 20 and 30 min for the other algorithms) or because the inclusion of relative
intensities leads to a higher sensitivity to short-term variations due to
clouds.
Time series of particle light extinction coefficient determined at
the ground level (top) and AOT (bottom) from the MAX-DOAS retrieval (coloured
symbols), together with the surface extinction from the humidity-controlled
nephelometer (open squares) and the AOT from the sun photometer (open
circles) for the golden days of the CINDI campaign. All data are converted to
a wavelength of 477 nm using the Ångström coefficient derived from sun
photometer measurements.
On 3 July a closed cloud cover is present between 08:30 and 14:30 UTC. The
ceilometer profiles show that the cloud base is initially located at very low
altitudes (<250 m) and increases in height in the early afternoon, leading
to an uplifted layer after 12:00 UTC. These features are also present in the
ceilometer profiles degraded with the MAX-DOAS averaging kernel. Under these
conditions, the BIRA, Heidelberg, and AIOFM algorithms are able to retrieve
the vertical structure of the boundary layer realistically, although some
differences exist in the detailed structure and the height and vertical
extent of the extinction layer in the afternoon. In particular, AIFOM does
not detect the uplift until 13:30 UTC but does detect enhanced extinction
between 1 and 2 km altitude corresponding to a thin cloud layer at the top of
the boundary layer visible in the ceilometer profiles between 14:00 and
18:00 UTC. In contrast, the coarse representation of the profile by JAMSTEC and the
parametrised algorithm by MPIC both show an enhancement in extinction due to
the presence of clouds but are not capable of retrieving the uplifted layer
in the afternoon. The clouds apparent in the ceilometer profiles in the
afternoon of 4 July between 15:30 and 18:00 UTC are identified in the
extinction profiles retrieved by the AIOFM algorithm but not in the
Heidelberg data (no other groups reported profiles for this period).
Comparison of AOT and surface extinction
In this section, the AOT and surface extinction derived by the different
participants are compared to sun photometer and WetNeph measurements,
respectively. The AOT is either derived by integrating the extinction profile
(BIRA, Heidelberg, AIOFM, and JAMSTEC) or directly retrieved (MPIC). For BIRA,
Heidelberg, AIOFM, and JAMSTEC, the value of the lowermost retrieval layer is
considered as being representative for the surface extinction, whereas for
MPIC the AOT divided by the layer height serves as an estimate. The time
series of AOT and surface extinction for the golden days of the CINDI
campaign are shown in Fig. .
Comparison between the AOT from MAX-DOAS and from sun photometer.
Listed are the number of data points, intercept, and slope of the linear
regression, the correlation coefficient R, the mean difference (MAX-DOAS
minus sun photometer), and the standard deviation of the mean difference.
Participant
N
Intercept
Slope
R
ΔAOT
AIOFM
431
0.071
0.007
0.795
0.023
0.86
0.011
0.079
BIRA
140
0.021
0.014
0.702
0.045
0.80
-0.062
0.083
Heidelberg
149
0.027
0.012
0.805
0.037
0.87
-0.031
0.078
JAMSTEC*
73
0.040
0.022
0.902
0.062
0.86
0.010
0.092
MPIC
128
0.039
0.014
0.622
0.040
0.81
-0.071
0.100
* Only data points before 16:00 UTC are reported
Left panels: correlation between AOT from the different groups and
from the sun photometer. The red line shows the linear fit and the dashed
line the 1:1 line. Right panels: histograms of the difference in AOT
(MAX-DOAS – sun photometer).
An overall good agreement between the AOT from MAX-DOAS and from the sun
photometer is achieved. Under conditions of clear sky and low aerosol load
(e.g., 23 and 24 June), BIRA tends to underestimate the AOT in the afternoon,
when the relative azimuth angle (RAA) between viewing direction and Sun is
small. In contrast, MPIC tends to underestimate the AOT in the morning under
conditions of high aerosol load (1 and 3 July). Best agreement between all
MAX-DOAS measurements as well as the sun photometer is achieved under clear sky
conditions in the morning hours when the RAA is large (23 and 24 June as well
as 4 July). The larger differences between the different groups at higher and
more variable aerosol load (30 June–3 July) are caused either by differences
in the retrieval algorithm or by slightly different temporal and/or spatial
sampling (i.e., slightly different viewing directions). As already discussed
in Sect. , the very high AOT and surface
extinction values observed during the morning of 30 June and 1 July are
caused by fog. Unfortunately, no sun photometer measurements are available
for these periods since these rely on the observation of direct sunlight. The
same applies to the morning of 25 June and the noon of 3 July. However, the
overall good agreement between the vertical profiles from MAX-DOAS and
ceilometer (see Sect. ) provide confidence that
the AOT can be retrieved reliably even under these conditions of reduced
visibility.
Comparison between the surface extinction from MAX-DOAS and from
WetNeph. Listed are the number of data points, intercept, and slope of the
linear regression, the correlation coefficient R, the mean difference
(MAX-DOAS minus WetNeph), and the standard deviation of the mean difference.
All extinction values are in units
of km-1.
Participant
N
Intercept
Slope
R
ΔAOT
AIOFM
617
-0.025
0.017
3.773
0.200
0.61
0.165
0.298
BIRA
180
0.014
0.006
1.638
0.115
0.73
0.096
0.122
Heidelberg
215
0.023
0.007
2.328
0.086
0.88
0.105
0.099
JAMSTEC*
112
0.046
0.008
1.214
0.144
0.63
0.132
0.103
MPIC
158
0.025
0.011
1.492
0.099
0.77
0.070
0.076
* Only data points before 16:00 UTC are reported
Left panels: correlation between surface extinction from the
different groups and from WetNeph. The red line shows the linear fit and the
dashed line the 1:1 line. Right panels: histograms of the difference in
surface extinction (MAX-DOAS – WetNeph).
A sudden jump in the AOT values from the sun photometer occurs on 2 July at
14:30 UTC but is not apparent in the MAX-DOAS data. It is not clear whether
this is caused by local aerosols not captured by the MAX-DOAS instrument due
to the different viewing geometry or by erroneous sun photometer data. For
these reasons, the data of the afternoon of 2 July are excluded from the
following correlation analysis.
The correlation between the AOT from MAX-DOAS and from sun photometer as well
as histograms for the AOT difference (MAX-DOAS minus sun photometer) for the
different groups are shown in Fig. . The results of the
regression analyses are listed in Table . The
correlation coefficient is >0.8 for all groups, and the mean difference
between AOT from MAX-DOAS and sun photometer is -0.07 with a standard
deviation <0.1. All datasets exhibit a slope significantly smaller than
1, ranging from 0.62 (MPIC) to 0.90 (JAMSTEC). This systematic
underestimation of the AOT is likely to be caused by both the fact that the
sensitivity for high altitudes is low and that the partial AOT above the
altitude where aerosol extinction have been retrieved (4 km) has not been
considered in this analysis. Best agreement in terms of slope (0.9) and mean
difference to sun photometer measurements (0.01) is achieved by JAMSTEC.
However, compared to the other participants the difference of JAMSTEC data to
sun photometer AOT shows a large scatter (0.092), and no data have been
submitted by JAMSTEC for the late afternoon (after 16:00 UTC) when the RAA is
small and systematic problems with the retrieval might occur, leading to the
smallest number of data points (73) submitted by this group.
It is important to note that parts of the discrepancies between the AOT from
different groups originate not only from the different retrieval
strategies and parametrisations, as well as from the different time periods
for which data are available from the different groups, but also, in the case of MPIC,
from the fact that the inversions are based on O4 measurements at a
different wavelength. The MPIC retrieval is based on measurements of the 360
nm O4 absorption band, and the retrieved extinction is converted to 477 nm
using the Ångström coefficient derived from co-located sun photometer
measurements. Therefore likely reasons for the small slope in the AOT
comparison between MPIC and sun photometer are both the uncertainties in the
Ångström coefficient and the reduced visibility in the UV, which leads
to a different horizontal footprint of the MPIC observations.
As shown in the upper panel of Fig. , a
strong disagreement exists between the surface extinction of the different
MAX-DOAS retrievals and the in situ measurements from the WetNeph instrument,
especially in the afternoon and during periods of high aerosol load. The
WetNeph observes a much smaller extinction than all MAX-DOAS retrievals for
most of the time. This is also apparent in the correlation plots and the
histograms of differences between MAX-DOAS and WetNeph surface extinction
shown in Fig. . A summary of the regression analysis
for surface extinction is shown in Table . Note
that the regression analysis yields values different to those reported by
. This is first because different samples are compared (in
the present study only data from the golden days are considered) and second
because applied a weighted orthogonal fit, whereas here a
usual linear regression has been used. Best agreement in terms of mean
difference between MAX-DOAS and WetNeph is achieved by the parametrised MPIC
algorithm, which is not capable of directly determining gradients in the aerosol
extinction near the surface. Hence a possible explanation for the strong
discrepancies observed for the OEM algorithms (BIRA, Heidelberg, AIOFM, and
JAMSTEC) could be a strong increase in extinction below the height of the
WetNeph inlet (60 m above ground). Further possible reasons for these
discrepancies and a comprehensive statistical analysis of data from the CESAR
site for an extended period of time have already been discussed in detail by
. We still do not have a conclusive explanation for the
origin of these differences, in particular since both the AOT and the
vertical structure of the boundary layer are captured well by the MAX-DOAS
vertical profiles.
Surface extinction values from the different MAX-DOAS algorithms show good
agreement during conditions of low aerosol (23–26 June) but exhibit
significant discrepancies at higher aerosol load (e.g., 30 June–3 July).
Again, a likely reason for parts of the discrepancies in surface extinction
between the different MAX-DOAS retrievals is the fact that different
parametrisations of the extinction profile are used. Since RH
tends to increase with altitude in the boundary layer, hygroscopic growth of
aerosol particles usually leads to an increase in extinction with altitude.
Moreover, gas may partition to aerosol as RH increases and temperature
reduces with increasing altitude, and ammonium and nitrate were observed to
increase with altitude in the vicinity of Cabauw . An inhomogeneous vertical distribution leads to erroneous
estimates of the surface extinction for models with a coarse vertical grid
(JAMSTEC) or with parametrised retrievals (MPIC). JAMSTEC represents the
extinction profile on a 1 km vertical grid and should for these reasons tend
to overestimate the surface extinction if extinction increases with altitude.
The same should be true for MPIC, for which the surface extinction (or rather
the average boundary layer extinction) is estimated by dividing the AOT by
the retrieved layer height. Indeed, JAMSTEC retrieves the highest AOTs,
whereas MPIC retrieves a smaller extinction than the other groups under
conditions of high aerosol load and large vertical gradients (30 June to 3
July). Although Heidelberg, BIRA, and AIOFM use the same vertical grid with a
layer thickness of 200 m and comparable retrieval algorithms, surface
extinction values from these groups show significant discrepancies in cases
of high aerosol load or fog, e.g. in the morning of 30 June, on 2 July, and in
the afternoon of 4 July.
In summary, possible reasons for the observed discrepancies between surface
extinction from MAX-DOAS and WetNeph are (1) strong vertical gradients of the
aerosol extinction with increased extinction below the height of the WetNeph
inlet, (2) problems of the MAX-DOAS retrieval algorithms in the presence of
nonhomogeneous horizontal distributions (although these are not very likely
given the smooth temporal variations of the MAX-DOAS and in situ data), (3)
the overestimation of the surface extinction by MAX-DOAS in the presence of
lofted layers, as well as (4) inlet losses of the in situ instruments. Note
that the extinction profiles estimated from a co-located Raman LIDAR
instrument agreed much better to the in situ WetNeph values, although only a
limited number of profiles could be compared, and a Mie closure showed the
consistency of all major aerosol in situ measurements in the basement of the
CESAR tower .
Conclusions
We have presented a first direct intercomparison of aerosol extinction
profiles, AOT, and surface extinction from MAX-DOAS measurements. MAX-DOAS
data collected during the CINDI campaign have been compared to independent
measurements of the AOT from an AERONET sun photometer, of the vertical
structure from a commercial ceilometer instrument, and of the surface
extinction from in situ instruments.
The retrieval algorithms that were part of this study follow very different
approaches and use different parametrisations of the aerosol vertical
profiles. BIRA, Heidelberg, AIOFM, and JAMSTEC use the optimal estimation
method and retrieve the extinction profiles at different altitude grids
(BIRA, AIOFM, and Heidelberg: 200 m layers; JAMSTEC: 1 km layers). MPIC uses a
least-squares algorithm with the AOT and layer height as retrieval
parameters and no further a priori constraints.
Despite large conceptual differences between the algorithms and different
representations of the aerosol extinction profile, and although the
information content of the MAX-DOAS measurements is low (typically in the
order of 2 DFS), the comparison of the retrieved
profiles with ceilometer backscatter profiles shows that all algorithms are
able to provide an estimate for the vertical extent of the boundary layer
with the expected accuracy. BIRA, AIOFM, and Heidelberg with the finest
vertical grid of 200 m, but also to a certain extent JAMSTEC with a 1 km
vertical grid, are able to resolve the vertical structure of the boundary
layer and to detect uplifted aerosol layers, fog, and clouds in the lowermost
≈1.5 km of the atmosphere. The vertical resolution is, however,
limited by the small information content of the measurements and is ≈500 m at the surface and ≈1 km at 1 km altitude. Therefore, thin
layers of high extinction, such as fog, appear strongly blurred in the
retrieved extinction profiles. Unfortunately, the AOT retrieved under
conditions of low visibility is difficult to validate since sun photometer
measurements, which rely on direct sunlight, are not available for these
periods.
In general, the time series of AOT retrieved from MAX-DOAS shows good
agreement with co-located sun photometer measurements. A regression analysis
shows correlation coefficients better than 0.8 for all groups. All retrieval
algorithms systematically underestimate the AOT with slopes of ranging from
0.6 to 0.9 and mean AOT differences (MAX-DOAS minus sun photometer) of less
than 0.07. It is important to note that parts of the differences between
MAX-DOAS and sun photometer are probably due to the fact that both kinds of
instruments observe different air masses in a highly populated and polluted
region where horizontal gradients in aerosol load are likely to occur.
Furthermore, MAX-DOAS is insensitive to aerosols above ≈2 km. In
the case of MPIC, additional systematic differences might be caused by the
conversion of the AOT from 360 to 477 nm. A further source of the observed
discrepancies is the empirical correction factor for the O4 dSCDs, for
which different approaches were applied. AIOFM, MPIC, and BIRA use a constant
value of 0.77 to 0.8, JAMSTEC implements a variable correction factor, whereas
no correction factor was applied by the Heidelberg group. This correction
factor has not been the focus of the present paper, but recent studies
indicate that the disagreement between modelled and measured O4 dSCDs is
probably not caused by uncertainties in the temperature dependence of the
O4 cross section but rather by elevated aerosol layers .
Given that the AOT and the vertical structure of the extinction profiles are
captured reasonably well by the different retrieval algorithms, it remains
open as to why there is such a large discrepancy between the surface extinction
from MAX-DOAS and from WetNeph. In particular in the afternoon, the WetNeph
shows much smaller values than retrieved by MAX-DOAS. Significant differences
between the individual MAX-DOAS retrievals, in particular under conditions of
high aerosol load and large vertical gradients, can be partially explained by
the different parametrisations of the vertical profile. Furthermore, strong
vertical gradients in aerosol extinction near the surface are a potential
reason for the observed discrepancies.
Although the ability of MAX-DOAS measurements to determine vertical profiles
of aerosols is limited by a small information content and a relatively low
vertical resolution, this intercomparison study shows that the MAX-DOAS
technique can reliably determine the vertical structure of the lowermost 2 km
of the atmosphere, and observations are not limited to clear sky conditions
but can also be performed during situations of low visibility.
With respect to future intercomparisons of MAX-DOAS aerosol products, e.g.
during the upcoming CINDI-II campaign in September 2016, one might consider
extending measurements and retrieval algorithms in several ways. First, it
has been demonstrated recently that the 3-D distribution of trace gases can be
retrieved from azimuthal scans . Many MAX-DOAS instruments
are nowadays capable of scanning not only at different elevation angles but
also in azimuthal direction, and a similar approach as suggested by
for trace gases can be used in future campaigns in order
to quantify horizontal inhomogeneities of the aerosol distribution. Second,
azimuthal scans contain information on aerosol optical and microphysical
properties , but algorithms capable of retrieving this
information have not been compared and validated against independent
instrumentation yet. Third, an important factor affecting the quality of the
aerosol and trace gas profile retrieval from MAX-DOAS measurements is the
presence of clouds. In the present study, the intercomparison has been
restricted to mostly cloud-free conditions, but the observations during foggy
conditions have shown that a meaningful retrieval of profile information is
possible even for situations with low visibility. An inclusion of
measurements during cloudy conditions would allow for testing the
capabilities and limitations of the retrieval algorithm under more adverse
conditions. Furthermore, a reliable cloud flagging is of great importance for
the quality assessment of MAX-DOAS data products. Different approaches for
the detection of clouds from MAX-DOAS measurements have been suggested
recently , and future campaigns would offer
the opportunity to test these cloud-flagging algorithms and to investigate
their applicability to MAX-DOAS aerosol and trace gas retrieval products.
Finally, further sensitivity studies on the basis of simultaneous MAX-DOAS
and LIDAR measurements during future campaigns should focus on the observed
disagreement between modelled and measured O4 dSCDs, including the
potential impact of uplifted layers of aerosols on MAX-DOAS O4
measurements.