AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4803-2016Absolute calibration of the colour index and O4 absorption derived from
Multi AXis (MAX-)DOAS measurements and their application to a standardised
cloud classification algorithmWagnerThomasthomas.wagner@mpic.deBeirleSteffenhttps://orcid.org/0000-0002-7196-0901RemmersJuliaShaiganfarRezaWangYanghttps://orcid.org/0000-0002-9828-9871Max-Planck-Institute for Chemistry, Mainz, GermanyThomas Wagner (thomas.wagner@mpic.de)28September2016994803482327April201630May20166September201615September2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/4803/2016/amt-9-4803-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4803/2016/amt-9-4803-2016.pdf
A method is developed for the calibration of the colour index (CI) and the
O4 absorption derived from differential optical absorption spectroscopy
(DOAS) measurements of scattered sunlight. The method is based on the
comparison of measurements and radiative transfer simulations for
well-defined atmospheric conditions and viewing geometries. Calibrated
measurements of the CI and the O4 absorption are important for the
detection and classification of clouds from MAX-DOAS observations. Such
information is needed for the identification and correction of the cloud
influence on Multi AXis (MAX-)DOAS profile inversion results, but might be
also be of interest on their own, e.g. for meteorological applications. The
calibration algorithm was successfully applied to measurements at two
locations: Cabauw in the Netherlands and Wuxi in China. We used CI and
O4 observations calibrated by the new method as input for our recently
developed cloud classification scheme and also adapted the corresponding
threshold values accordingly. For the observations at Cabauw, good agreement
is found with the results of the original algorithm. Together with the
calibration procedure of the CI and O4 absorption, the cloud
classification scheme, which has been tuned to specific locations/conditions so
far, can now be applied consistently to MAX-DOAS measurements at different
locations. In addition to the new threshold values, further improvements were
introduced to the cloud classification algorithm, namely a better description
of the SZA (solar zenith angle) dependence of the threshold values and a new set of wavelengths
for the determination of the CI. We also indicate specific areas for future
research to further improve the cloud classification scheme.
Introduction
Multi AXis differential optical absorption spectroscopy (MAX-DOAS)
measurements are a widely used remote sensing technique for the measurement
of atmospheric trace gases and aerosols (e.g. Hönninger and Platt, 2002;
Wittrock et al., 2004; Hönninger et al., 2004; Heckel et al., 2005; Frieß et al., 2006; Irie et
al., 2008; Clémer et al., 2010; Li et al., 2010; Wagner et al., 2011; Ma
et al., 2013; Hendrick et al., 2014; Wang et al., 2014, 2015; Vlemmix et al.,
2015). MAX-DOAS measurements can be strongly affected by clouds (Wagner et
al., 2004, 2011, 2014; Gielen et al., 2014; Wang et al., 2015). Thus cloud-contaminated measurements have to be flagged, excluded from further
processing or corrected for the effects of clouds. Different algorithms for
the identification and classification of clouds based on MAX-DOAS
measurements have recently been developed. They are based on several
quantities derived from the measured spectra (Wagner et al., 2014; Gielen et
al., 2014; Wang et al., 2015). These quantities include the following.
A so-called colour index (CI, see e.g. Sarkissian et al., 1991, 1994),
which is defined as the intensity ratio for two selected wavelengths. In this
study we define the CI as a ratio of the intensity at the shorter wavelength
to the intensity at the longer wavelength:
CI=IshortIlong.
The measured radiance at a selected wavelength. Here it should be noted
that usually (MAX-)DOAS instruments are not radiometrically calibrated. Thus
we use the term “radiance” here in a broader sense for the measured
signal as well, e.g. expressed as counts per second.
The absorption of the oxygen dimer O4 (Greenblatt et al., 1990).
The strength of the so-called Ring effect (the filling-in of solar
Fraunhofer lines by rotational Raman scattering, see Grainger and Ring,
1962; Wagner et al., 2014).
It was shown by Gielen et al. (2014) and Wagner et al. (2014) that the CI is
very sensitive to the presence of clouds. It is thus well suited for their
detection, especially because for zenith observations, clouds always lead to a
decrease of the CI compared to clear-sky conditions (if the CI is defined
with the intensity at the shorter wavelength divided by the intensity at the
longer wavelength). In contrast, the other quantities mentioned above can be
both increased or decreased in the presence of clouds depending on the cloud
properties, wavelength and viewing geometry. Because of the unique dependence
of the CI on the occurrence of clouds, the CI is usually used as the primary
quantity for the detection of clouds. From the other quantities, especially
from the radiance and the absorption of the oxygen dimer O4, important
additional information on cloud properties can be derived (e.g. the
presence of optically thick clouds or fog, see Wagner et al., 2014; Gielen et
al., 2014; Wang et al., 2015). Since Ring effect measurements do not provide
significant extra information, and because the quantitative analysis of the
Ring effect is rather complicated, the Ring effect is not further considered
here.
The identification and classification of clouds is usually based on the
comparison of the measured quantities with their thresholds. These threshold values
can e.g. be derived from measurements on clear days. Another, more universal,
method is the determination of the threshold values from radiative transfer
simulations. However, since MAX-DOAS instruments are usually not
radiometrically calibrated, a direct quantitative comparison of measured and
simulated quantities is not possible, which hampers the direct application
of threshold values derived from radiative transfer simulations. To overcome
this limitation, in this study we develop calibration procedures for the CI
and the O4 absorption and apply them to MAX-DOAS observations.
The proposed CI calibration comprises the determination of a proportionality
constant, which converts the measured values into well-defined quantities
(i.e. radiance ratios for the selected wavelengths). Similar suggestions for
the calibration of the CI were already presented by Gielen et al. (2014) and
Wagner et al. (2014).
For the O4 measurements, the calibration comprises the determination and
correction of an additional offset (the O4 absorption of the Fraunhofer
reference spectrum, FRS) like in Wagner et al. (2014). Already in Wagner et
al. (2014) the measured CI and O4 were calibrated based on selected
clear-sky measurements. In contrast, here we develop standardised
calibration algorithms for CI and the O4 absorption, which can be
applied to other MAX-DOAS measurements in a consistent way.
Update of the cloud classification scheme
After applying the new calibration algorithms to the measurements the
calibrated CI and the O4 absorption differ slightly from the calibrated
values of the original classification scheme. Thus the threshold values of
the cloud classification scheme have to be adapted accordingly. In addition
to these changes, further improvements to the original classification scheme
(Wagner et al., 2014) are introduced.
The determination of the threshold values is based on well-defined
atmospheric scenarios.
The new thresholds better account for solar zenith angle (SZA) dependencies.
A new set of wavelengths is used for the CI: the old wavelength pair
(320 nm/440 nm, see Wagner et al., 2014) is replaced by 330 nm/390 nm.
The new wavelength pair has several advantages: the new shorter wavelength
(330nm) is less affected by the atmospheric ozone absorption than the
original choice (320 nm). The new longer wavelength (390 nm) has the
advantage that it is covered by typical UV MAX-DOAS instruments (while
440 nm is often not). The variability of the surface albedo for 390 nm
is also smaller than for 440 nm.
One major aim of this study is to provide a universal cloud classification
scheme for MAX-DOAS measurements based on the new calibration procedures for
the CI and the O4 absorption and the updated threshold values.
The calibration procedures for the CI and the O4 absorption are
described in the first part of our paper (Sects. 2 and 3). In Sect. 4, we
apply both new calibrations to the measurements used for the development of
the original cloud classification algorithm (Wagner et al., 2014), determine
new threshold values and compare the results of the new and original
algorithms.
In Sect. 5 particular problems and areas for future improvements of the
classification scheme are discussed. Section 6 presents conclusions and
outlook.
Calibration of the CI
DOAS instruments are usually not radiometrically calibrated. Thus measured
radiances and CI derived from MAX-DOAS or zenith sky DOAS measurements cannot
be directly compared to the results from radiative transfer simulations. In
this study (like in Wagner et al., 2014), we use the Monte-Carlo radiative
transfer model McArtim (Deutschmann et al., 2011) for the simulations of
radiances, CI and O4 absorption. The settings used for the radiative
transfer simulations used this study are described in Sect. 2.2 of Wagner et
al. (2014).
The CI derived from the measurements (CImeas) can be converted to
calibrated CI (CIcal) by multiplication with a proportionality
constant β:
CIcal=CImeas⋅β.β can be determined by comparison of measured and simulated CI under
well-defined conditions (see e.g. Wagner et al., 2014; Gielen et al., 2014;
Wang et al., 2015). Here it should be noted that instrumental problems (e.g.
wrong offset or dark current correction or a non-linear response of the
detector) might cause an additional offset between the measured and
simulated CI. However, except for very low signals (e.g. at high SZA) or
cases with strong oversaturation of the detector, these offsets are very
small and are ignored in our calibration procedure. Moreover, oversaturated
spectra could be easily identified by increased residuals of the spectral
analysis.
Wagner et al. (2014) used measurements during a clear morning with constant
aerosol optical depth (AOD, derived from a sun photometer) for the calibration of the CI, radiance,
O4 absorption and Ring effect. Gielen et al. (2014) applied a more
universal approach by considering CI values over extended periods of time.
They compared cluster points for minima and maxima of the CI to results of
radiative transfer simulations. In our study we basically follow their
approach, but we also apply two important modifications.
We only consider the minimum CI. As shown by Gielen et al. (2014), the
maximum CI varies strongly with changing AOD, especially for low AOD. Thus
the comparison of measured and simulated maximum CI depends critically on
the AOD during the considered period, which is usually unknown. In contrast,
the minimum CI depends only slightly on the specific atmospheric properties
and measurement conditions (for details see below).
We do not use static threshold values, but consider the SZA dependence
(of the minimum CI).
Simulated colour indices for an elevation angle of 85∘ (top:
CIoriginal= 320 nm/440 nm; middle: CInew= 330 nm/390 nm) for different aerosol and cloud optical depths. For the
aerosol cases (green lines) the OD represents the value at 390 nm
(Ångström exponent = 1); for the cloud cases (OD ≥ 2) the
same optical depth is assumed for both wavelengths (Ångström
exponent = 0). The aerosol layer is between the surface and 1 km; the
cloud layer is between 1 and 2 km. Aerosol properties are described by a
Henyey–Greenstein model with an asymmetry parameter of 0.68 and a single
scattering albedo of 0.95. Bottom: SZA for a day (26 June 2009) in the middle
of the campaign.
We also propose using the wavelength pair of 330 and 390 nm for the
calculation of the CI (see the discussion in the introduction). In the
following, the original CI (based on the wavelength pair 320 and 440 nm, see
Wagner et al., 2014) is indicated by CIorig and the new CI by
CInew. In Fig. 1 simulation results for CIorig and
CInew are shown for different aerosol and cloud conditions. For
both CI two features are obvious.
As already shown by Gielen et al. (2014), the maximum CI depends strongly
on the AOD. This finding confirms that the maximum CI is not well suited for
the calibration of the CI.
Small CI are found for cloudy cases, but interestingly the minimum values
do not always occur for the largest cloud optical depths. This finding is
probably caused by multiple scattering inside the clouds, which also
increases the probability of additional Rayleigh scattering. Depending on
the chosen wavelengths and SZA, the minimum CI is found for cloud optical
depths between 3 and 12 (but the CI for the different cloud optical depths
varies only slightly). Here it should also be noted that the CI for cloudy
conditions is almost independent from cloud height.
It should be noted that these simulations are performed not for an elevation
angle of exactly 90∘, but for 85∘, because of the specific
conditions of the measurements used here (Wagner et al., 2014). Thus the
results differ slightly from those for exact zenith view (for more details
see Sect. 4.6). For the same reason, the simulation results in Fig. 1 are
presented as function of time and not as a function of the SZA.
Comparison of measured (left axis) and simulated CI (top:
320 nm/440 nm; bottom: 330 nm/390 nm) during CINDI. The lines represent
minimum values (see Fig. 1), aerosol-free conditions and low aerosol load
(AOD = 0.1). The measurements are from the period 12 June to 15 July
2009; the simulations are performed for a day (26 June 2009) in the middle of
the campaign. Note that the outlier around 18:00 belongs to a spectrum of bad
quality as indicated by the large residual of the spectral analysis.
In Fig. 2 selected observations of CIorig and CInew
during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring
Instruments (CINDI) campaign in summer 2009 (Piters et al., 2012) are
compared to simulation results (for aerosol-free conditions, low aerosol load
and the minimum CI for cloudy conditions, see Fig. 1). The minimum values are
derived from a polynomial fit to the simulated minimum CI for different cloud
optical depths as shown in Fig. 1. The polynomial expressions as well as the
tabulated values of the minimum CI are provided in Tables 2 and A1 (in the
Appendix). Different y axes are used for the measured (left) and simulated
(right) CI. The maximum values of both axes were chosen according to the
absolute radiance calibration for the respective wavelengths presented in
Wagner et al. (2015). For CIorig and CInew most
measurements fall into the area between the simulated minimum CI and those
for an AOD of 0.1 (similar findings were presented by Gielen et al., 2014).
Interestingly, about 20 % of all measurements are slightly lower than the
simulated minimum values. This finding cannot be explained by the effect of
measurement noise on the CI, which is very small (≪ 1 % for the SZA
range considered here). Instead, the low CI values are probably caused by 3-D
effects of broken clouds, which are not considered in our simulations. If, for
example, the side of a cloud is illuminated by the direct solar beam, the
composition of the light which enters the cloud might change compared to
horizontally homogeneous clouds. The relative fraction of the diffuse sky
radiation (which is blueish) compared to those of the direct solar beam might
decrease, because the cloud side is illuminated by only part of the
downwelling diffuse sky radiation. This effect would lead to a decrease of
the CI.
However, it should be noted that even the lowest measured CI are still close
to the simulated minimum values, indicating that the overall dependence of the
CI is well represented by the model simulations (the detailed investigation
of these 3-D effects should be the topic of futures studies). The results in
Fig. 2 indicate that the minimum CI obtained from measurements (or better an
accumulation point, see below) over a period of several weeks are well suited
for the calibration of the measured CI.
Normalised CI during CINDI for both wavelength pairs. The
normalisation is performed by dividing the measured CI by the respective
simulated minimum values. For SZA < 60∘ (indicated by the red
vertical lines) the minima of the normalised CI are almost independent from
the SZA. The measurements are from the period 12 June to 15 July 2009.
Calibration procedure
We propose a calibration procedure consisting of three steps.
First the measured CI are normalised (divided) by the corresponding
simulated minimum CI (for the same SZA). The normalised CI for both
wavelength pairs are shown in Fig. 3. The normalisation procedure mostly
eliminates the SZA dependence of the measured minimum CI. However, for
SZA > 60∘ (red vertical lines in Fig. 3), a SZA dependence
is still present. Thus only measurements with SZA < 60∘ are considered
for the determination of the scaling factor.
In the next step, frequency distributions of the normalised CI values for
SZA < 60∘ are calculated, see Fig. 4. Distinct accumulation
points for the minimum CI for both wavelength pairs are obtained, indicating
the presence of clouds. Their maxima directly represent the (inverse of the)
proportionality constant β (Eq. 2).
The normalised CI of the accumulation point is determined by fitting a
Gaussian curve to the frequency distribution after the clear-sky data were
removed (data with normalised CI larger than 0.59 and 0.93 for the
CIold and CInew respectively. Note that the derived
values are almost independent from the chosen clipping value. We also
determined the uncertainty of the scaling factor from the Gaussian fit to
< 1 %. In order to account for possible temporal variation of the
instrument sensitivity, we applied the method separately to the measurements
during the first and second half of the campaign and found deviations
< 2 %. This value probably represents a more realistic uncertainty for
the measurements used in our study.
For CIorigβ is found to be 2.04±0.04, and for
CInew it is 1.16±0.02. The derived proportionality constants agree
well with those calculated from the absolute radiance calibration presented
in Wagner et al. (2015): 2.00 for CIorig and 1.19 for
CInew.
Frequency distribution of the normalised CI for
SZA < 60∘ (for bins of 0.02). The blue and magenta curves
represent the results for Cabauw (1527 measurements); the black curve
represents the results for Wuxi (2440 measurements).
In Fig. 4 results for MAX-DOAS observations at Wuxi (China) for the new
CI are also shown (Wang et al., 2015). Measurements over a period from 1 January
to 31 December 2012 were used. As for the Cabauw measurements a clear peak
is found indicating that the method works in a similar way for completely
different locations and measurement conditions. The derived proportionality
constant is different from that for the Cabauw measurements caused by the
different (wavelength-dependent) sensitivities of the instruments. Here it
should be noted that differences of the aerosol properties at both locations
could also contribute to the differences, but the effect of aerosols on the
CI in the presence of clouds is typically below 2 %.
It should be noted that for measurements at locations with very low or very
high cloud probability a larger time period than for our method might be
needed to obtain a sufficient number of both cloudy and cloud-free
measurements. In extreme cases, an accumulation point might even exist for
CI representing clear-sky conditions (if the AOD also stays constant over an
extended time period). In such cases, clear-sky measurements might be
identified by visual inspection and be removed before the frequency
distribution is calculated.
Calibration of the O4 absorption of the
Fraunhofer reference spectrum
The O4 calibration is also performed by comparing the measurements to
model simulations for specific atmospheric properties and measurement
conditions. From the spectral analysis, the O4 slant column density
(SCD) is derived, which represents the integrated O4 concentration
along the atmospheric light path. Since the FRS also contains atmospheric
O4 absorptions, the result of the spectral analysis eventually
represents the difference of the O4 SCDs of the measurement and the
FRS, which is usually referred to as differential SCD or DSCD.
Both the O4 SCD and DSCD can be converted to the corresponding O4
air mass factor (AMF) or O4 differential air mass factor (DAMF):
AMF=SCD/VCDDAMF=DSCD/VCD.
The VCD represents the vertical column density, the vertically integrated
concentration which can be calculated from vertical profiles of temperature
and pressure. Note that, in contrast to other trace gases, the SCD and VCD of
O4 are expressed relative to the square of the O2 concentration
(see Greenblatt et al., 1990). For the measurements during the CINDI
campaign, the O4 VCD is determined as 1.41×1043 molecules2 cm-5 (Wagner et al., 2014).
To obtain the total O4 AMF of the measurement, the O4 AMF of the FRS
(AMFFRS) has to be added:
AMF=DAMF+AMFFRS.
The determination of AMFFRS constitutes the calibration of the
O4 measurements:
AMFcal,i=DAMFi+const=DAMFi+AMFFRS.
Here DAMFi indicates the differential O4 AMF derived from the
spectral analysis of an individual measurement. AMFcal,i
indicates the corresponding calibrated O4 AMF.
Simulated O4 AMFs (360 nm) for different aerosol and cloud
scenarios. The violet and red lines indicate results for minimum and maximum
AOD of 0 and 3 respectively. For clear-sky conditions almost the same
O4 AMFs are obtained around SZA of 36∘ (indicated by the blue
arrows), independent from the assumed AOD. Depending on the cloud OD and the
cloud height, clouds can either increase or decrease the O4 AMF compared
to clear-sky conditions (Wagner et al., 2011). The simulations are performed
for a day (26 June 2009) in the middle of the campaign.
In the following we describe how AMFFRS can be determined. Figure 5
presents simulated O4 AMFs for (near) zenith view for different
cloud-free (coloured lines) and cloudy conditions (black lines). From these
simulation results, two important findings can be deduced.
Around SZA of 36∘ (indicated by the blue arrows) the O4
AMFs for the different aerosol scenarios are almost the same.
For cloudy scenarios the O4 AMF can be either decreased or increased
compared to cloud-free scenarios: for low and optically thick clouds the
O4 AMFs are enhanced, while for high and optically thin clouds the
O4 AMFs are decreased (Wagner et al., 2011).
Comparison of measured O4 DAMFs (right axis) with simulated
O4 AMFs (left axis) for different AOD. In the top panel all
observations are shown and in the bottom panel only clear-sky observations are shown.
The rectangles indicate the SZA ranges (30–50∘) used for the
calibration of the O4 measurements. The right y axis is shifted compared
to the left y axis by the O4 AMF of the FRS (-1.78). The
measurements are from the period 12 June to 15 July 2009; the simulations are
performed for a day (26 June 2009) in the middle of the campaign (aerosol
layer height: 1000 m, surface albedo: 5 %).
The first finding indicates that clear-sky observations (around SZA of
36∘) can in principle be used for the calibration of the O4
measurements, even if the exact AOD is not known. The second finding
indicates that cloudy measurements (as identified by CI) should be removed
before the calibration is performed. Figure 6 presents a comparison of the
measured O4 DAMFs and simulated O4 AMFs. Note that the y axes were
shifted by 1.78, the final value derived for the O4 AMF of
the FRS (see below). In the top panel all measurements are shown and in the bottom
panel only measurements for clear-sky conditions are shown (the cloud
filtering was performed using the CI as described in Wagner et al., 2014).
Interestingly, not only for the cloud-filtered measurements, but for all
measurements the minimum O4 DAMFs are well represented by the
simulations (for clear sky), indicating that during the CINDI campaign
situations with only high thin cloud layers did not occur. This finding is
confirmed by results from lidar measurements (see Wagner et al., 2014). Of
course, the probability of high clouds (without low clouds present at the
same time) can be different for other seasons and locations. Thus we
recommend that a cloud filter should always be applied to the measured
O4 DAMF, before they are compared to the simulated O4 AMFs.
Normalised O4 DAMF for all measurements (blue) or measurements
under clear-sky conditions (red). The normalisation is performed by
subtracting the simulated O4 AMFs for AOD of 0.2. The rectangles
indicate the SZA ranges used for the calibration of the O4 DAMF. The
measurements are from the period 12 June to 15 July 2009.
Figure 7 presents the measured O4 DAMFs after the simulated O4 AMFs
for AOD of 0.2 were subtracted. This normalisation procedure is applied to
remove the SZA dependence. The choice of the simulations for AOD of 0.2 is
somehow arbitrary, but the exact choice has only a very small effect on the
normalisation results. Polynomial expressions and tabulated values of the
O4 AMF for AOD = 0.2 (clear-sky reference value) are provided in
Tables 2 and A1.
For the calibration of the O4 DAMF, measurements for SZA between 30 and
50∘ were chosen for two reasons.
For different aerosol layer heights, the SZA for which the O4 AMF
for different AOD become similar varies slightly between about 30∘
(for layer heights of 500 m) and 50∘ (for layer heights of 2000 m),
see Fig. A1 in the Appendix. Since the aerosol layer height is usually
unknown and can vary with time, we chose a SZA range which covers typical
aerosol layer heights.
After applying the cloud filter, the number of measurements decreased by
about a factor of 3. The rather large SZA range ensures that a useful
number of measurements is still available for the comparison with the simulation
results.
Frequency distribution (for bins of 0.05) of the normalised O4
DAMF for SZA between 30 and 50∘. The blue and red curves represent
observations at Cabauw for all sky conditions (896 measurements) and clear-sky conditions (302 measurements) respectively. For both selections the
frequency maximum is found for -1.78. The black curve represents the
frequency distribution for clear-sky observations at Wuxi (790
measurements).
In Fig. 8 the frequency distribution of the normalised O4 DAMF is shown
for all observations (blue) or only clear-sky observations (red). The maximum
values and uncertainties are determined in the same way as for the CI
(Sect. 2). For both cases (all measurements or clear-sky measurements) a
value for the O4 AMF of the FRS (AMFFRS) of 1.78 is derived. The
uncertainties are ±0.09 and ±0.08 for all and clear-sky observations
respectively. Here it should be noted that the rather large uncertainties are
caused by the low number of observations and can probably be reduced if
measurements for longer periods with more clear days are analysed. In the
original version of our algorithm (Wagner et al., 2014) AMFFRS was
determined based on selected clear-sky days with similar AOD. In spite of the
different procedures, a very similar value for AMFFRS (1.75) was
found.
In Fig. 8 results for MAX-DOAS observations at Wuxi (China) are also shown
(Wang et al., 2015). Measurements over a period from 1 January to 31 December
2012 were used. As for the Cabauw measurements, a clear peak is found
indicating that the method works in a similar way for completely different
locations and measurement conditions. The derived value for the O4 AMF
of the FRS is almost the same as for the Cabauw measurements as both FRS
were recorded at similar AOD and SZA.
Finally, two important aspects should be mentioned.
For long-term measurements, it might be necessary to use different FRS
for different parts of the whole time series. In such cases the calibration
procedure has to be applied for each selected FRS.
The O4 VCD depends on atmospheric temperature and pressure. Thus it
varies with time. Depending on the weather conditions and season, such
changes can exceed 10 %. The variation of the O4 VCD leads to a
similar variation of the measured O4 DAMFs. In addition, the temperature
dependence of the O4 cross section probably further increases this
variability. So far, these effects are not explicitly considered in most
studies, and here we also assumed the O4 VCD to be constant. Thus part
of the scatter of the measured O4 AMFs in Figs. 7 and 8 might be caused
by the variation of the O4 VCD and temperature dependence of the O4
cross section. However, the current version of the algorithm is only slightly
affected by the corresponding uncertainties of the derived O4 DAMFs,
because they are used only for the identification of optically thick clouds
and fog. Future studies might take the effects discussed above into account
when retrieving the O4 DAMFs from the measured spectra.
Update of the cloud classification scheme using the newly calibrated
MAX-DOAS CI and O4 data
Wagner et al. (2014) developed a scheme for the classification of cloud and
aerosol conditions based on MAX-DOAS measurements. In this section we
introduce an updated version of this classification scheme. Compared to the
original version, four major changes are applied.
The threshold values of the original scheme have been adapted to the
newly calibrated CI and O4 data.
The new threshold values are based on well-defined atmospheric
conditions. Thus the procedures for the determination of the threshold
values can be applied to any measurements at different locations and
seasons. The new threshold values also better represent the SZA
dependencies.
The threshold values are also provided for the new CI.
The determination of optically thick clouds is only based on O4
measurements (without making use of the radiance measurements).
Flow chart of the updated cloud classification scheme. The basic
structure is the same as in the original scheme (Fig. 14 in Wagner et al.,
2014). However, except for the decision criterium for fog (decision no. 7), all
other criteria were changed compared to the original scheme. The individual
changes are summarised in Table 1. For decision no. 6, no universal
recommendation can be given, because the spread of the non-zenith angles
depends in particular on the relative azimuth angle. Thus it is omitted in the
updated classification scheme.
Comparison of the normalisation procedures and threshold values for
the individual decisions of the cloud classification scheme of the original
algorithm and the new algorithms (for CIorig and CInew).
In the column “algorithm steps” it is indicated in which decisions the
selected quantity is involved (see Fig. 9).
QuantityAlgorithmOriginal algorithm New algorithm based New algorithm based steps(320/440) on CIorig (320/440) on CInew (330/390) NormalisationThresholdNormalisationThresholdNormalisationThresholdCI(1)yes, division by simulation for AOD = 0.30.65noSZA-dependent, simulation for AOD = 0.751noSZA-dependent, simulation for AOD = 0.852TSI3(2,3,5)yes, division by simulation forAOD = 0.31.2×10-7 s-2noSZA-dependent, simulation forAOD of 0.2 and minimum CI; α=0.06noSZA-dependent, simulation for AOD of 0.2 and minimum CI; α=0.06Spread CI(3,4)yes, division by simulation for AOD = 0.30.14no0.14no0.14Spread O4(7)no0.37no0.37no0.37O4 zenith(8)yes, subtraction of simulation for AOD = 0.30.74noSum of 0.85 and simulated O4 AMF for AOD = 0.2noSum of 0.85 and simulated O4 AMF for AOD = 0.2Radiance4yes, division bysimulation for AOD = 0.30.9not used anymorenot used anymorenot usedanymorenot used anymore
1 AOD for 440 nm; 2 AOD for
330 nm; 3 The definition of TSI has changed: for the new TSI no time
information is used (see text). 4 In the new scheme optically thick
clouds are identified by the O4 absorption alone.
The basic scheme of the original classification algorithm, however, is not
changed. For most of the individual decision steps the normalisation
procedures and the definition of the threshold values for the involved
quantities were adapted. An overview on the new classification scheme and the
applied changes is provided in Fig. 9 and Table 1. Only for the
identification of fog, are exactly the same criteria as in the original algorithm
still used. For the other quantities the new thresholds were chosen to
best match those of the original scheme. The individual changes are
summarised in Table 1 and are discussed in detail in the next subsections.
At the end of this chapter (Sect. 4.6) the results of the new scheme are
compared to the results of the original scheme.
New threshold values for the CI
In the original cloud classification scheme the measured CI was first
normalised (divided) by a SZA-dependent clear-sky reference value (simulated
for an AOD of 0.3). The normalisation was applied to correct the strong SZA
dependence of the CI (see Fig. 1). Then a constant threshold (independent
of the SZA) was used to discriminate clear from cloudy observations.
However, it turned out that the simple normalisation was not sufficient for
large SZA (> 60∘). Thus we decided to use a SZA-dependent
threshold in the new version of our cloud classification algorithm (but we
not apply the normalisation of the measured CI values anymore). As threshold
values, we use simulation results for AODs of 0.75 for 440 nm
(CIorig) and 0.85 for 390 nm (CInew) respectively. The
AOD value of 0.75 at 440 nm was chosen to achieve consistency between the
new and the original classification scheme (for small SZA). The corresponding
AOD values at the other wavelengths (including those for the calculation of
CInew) were derived from the AOD at 440 nm using a typical
Ångstrøm exponent of unity.
Comparison of measured normalised CI for 24 June 2009 with simulated
CI for different scenarios. Top: original CI for 320 and 440 nm; Bottom: new
CI for 330 nm/390 nm. The AOD values in the legends correspond to the
wavelengths 440 and 390 nm respectively. The blue curves represent CI for
the AOD measured by the AERONET instrument on the morning of 24 June 2009.
The red curves represent CI for the threshold values used in the new
classification scheme. The minimum values represent cloudy conditions (see
Fig. 1). The simulations are made for a day (26 June 2009) in the middle of
the campaign.
In Fig. 10 the calibrated CIorig and CInew for 24 June
2009 are compared to simulated CI for different aerosol and cloud properties.
Note that the CI for AOD of 0.85 at 390 nm represents the SZA-dependent
threshold value, see Tables 1 and A1. During the morning the measured CI are
similar to the simulation results (blue lines) for the AOD obtained from the
simultaneous AERONET measurements. Around noon, the AOD increases and
some clouds also appear. As a consequence the measured CI decreases and it even falls slightly below the simulated minimum values several
times. After about
15:00 the clouds disappear, but the CI stays at low levels because of the
increased AOD in the afternoon.
Polynomial expressions describing the SZA-dependent threshold values for
zenith viewing direction are provided in Table 1; tabulated values are
provided in Table A1 in the Appendix.
New threshold values for the temporal smoothness indicator (TSI)
In our original algorithm a so-called temporal smoothness indicator (TSI)
is used, which is derived from the temporal variability of the CI (Eq. 7 in
Wagner et al., 2014). It is used to identify rapid variations of the sky
conditions, e.g. related to broken clouds. In our original study, the time
differences between the individual measurements were explicitly considered
for the calculation of the TSI. In fact, the TSI was defined as the discrete
approximation of the second derivative in time. The TSI was also normalised
(divided) by the clear-sky reference value and a constant threshold was
applied to discriminate measurements with high TSI from measurements with low
TSI (indicating a smooth temporal variation of the CI). In the new version
two important changes are applied: first, the time difference between the
individual measurements is not considered anymore for the calculation of the
TSI. The motivation for this change is the fact that the TSI (as defined in
Eq. 7 in Wagner et al., 2014) depends strongly on the time differences
between individual measurements and thus on the individual instrument
properties and measurement protocols. For practical use in MAX-DOAS
inversions it is, however, sufficient to know whether the sky condition has
changed during the time interval of a typical elevation sequence (independent
from the actual duration of this elevation sequence). Thus, in the updated
version of the cloud classification scheme, we use the following (simpler)
definition of the TSI:
TSIn=CIn+1+CIn-12-CIn.
Here CIn indicates the CI in zenith direction of the nth elevation
sequence. Another change compared to the original classification scheme is
that the TSI is not normalised by the clear-sky CI reference values, but
instead a SZA-dependent threshold is used. The advantage of this approach is
that the threshold values can be calculated based on well-defined atmospheric
scenarios. Here we suggest using the difference of the CI for a clear-sky
scenario with moderate aerosol load (AOD of 0.2) and the minimum CI for
cloudy conditions (see Fig. 10). The AOD of 0.2 is assumed for the upper
wavelength and the AOD for the lower wavelength is calculated assuming an
Ångström exponent of 1. The threshold value is calculated from the CI
for both scenarios:
TSIthreshold(SZA)=α×CIdiff(SZA)=α×[CIAOD=0.2(SZA)-CImin(SZA)].
(a) Measured normalised CI (a) for 24
June 2009. (b) Normalised TSI according to the original algorithm.
Here a constant threshold value was used. (c, d) New TSI as used in
this study without normalisation and explicit consideration of the time
(Eq. 7). The threshold values for the new TSI depend on the SZA.
Here TSIthreshold(SZA) represents the threshold value and
CIdiff(SZA) is the difference of the simulated CI for clear and
cloudy conditions. We chose the proportionality constant α such that
for SZA around 50∘ the threshold value for the new version of the
algorithm matches that of the original version. The best agreement is found for
α=0.06 (see Fig. 11). The polynomial expression for CIdiff
(SZA) for the exact zenith view is provided in Table 1, and tabulated values are
provided in Table A1 in the appendix. Note that the provided values have to
be multiplied by α=0.06 before they can be used as threshold value
for the new TSI.
In this study, we do not provide a set of thresholds for the TSI in
non-zenith viewing directions, because they also depend on the azimuth
angle, which is different for different instruments (and seasons).
Spread of the CI (top: CIorig for 320 nm/440 nm; bottom:
CInew for 330 nm/390 nm) between the different elevation angles for
different aerosol (left) and cloud (right) scenarios. The spread is
calculated as the difference between the maximum and minimum CI for a given
combination of SZA and relative azimuth angle. The different lines of the
same colour represent simulations for different relative azimuth angles (0,
30, 60, 90, 120, 150, 180∘). The simulations are made assuming an
elevation angle of 85∘ for zenith view. Results for exact zenith
view are almost identical (see Fig. A2 in the Appendix).
Threshold values for the spread of the CI for different
elevation angles
The spread of the CI for the different elevation angles approaches zero in
the presence of clouds (Wagner et al., 2014), which makes it a useful
quantity for the distinction between situations with enhanced aerosol loads
or clouds. In addition, the spread of the CI can be used to identify clouds
at low SZA, when identification by the absolute value of the CI fails (see
Wang et al., 2015). The spread of the CI is calculated as the difference
between the maximum and minimum CI for all elevation angles of an individual
elevation sequence. Wagner et al. (2014) performed simulations of the spread
of the CI for selected relative azimuth angles only. Here we extended these
calculations to cover all relevant combinations of relative azimuth angles
and SZA (see Fig. 12). Based on these results the following can be concluded.
No clear SZA dependence is found. Thus a simple normalisation as
a function of the SZA (as in Wagner et al., 2014) is in general not
appropriate, and in the new version of the classification scheme no
normalisation as function of the SZA is performed. We keep the original
threshold value of 0.14 (see Table 1), because according to Fig. 12 it still
seems to be a good compromise to discriminate clouds and aerosols. Here it
should be noted that for individual measurements (with a specific relation
between the SZA and the relative azimuth angle) a monotonous relationship
between the spread of the CI and the SZA might occur. In such cases a
SZA-dependent threshold might still be useful.
Clouds and aerosols (with the same optical depth) have a very similar
effect on the spread of the CI (the differences are mainly a result of the
different wavelength dependence of cloud and aerosol scattering). Thus in
many cases, it is difficult to clearly discriminate between both types of
atmospheric scatterers based on the spread of the CI.
Cases with optical depths about > 6 can be clearly identified
based on the used threshold of 0.14. This finding makes the spread of the CI
an important indicator for the presence of clouds in situations (e.g. at
small SZA) in which they can not be identified based on the zenith CI itself
(Wang et al., 2015).
New threshold for the O4 AMF in zenith
direction
In contrast to the original version of the algorithm, the SZA dependence of
the measured O4 AMFs is not corrected by subtracting the corresponding
clear-sky reference values in the new algorithm. Instead, the measured
O4 AMFs are compared to the wavelength-dependent clear-sky reference
value, to which a constant offset is added (representing the effect of
multiple scattering inside optically thick clouds). In contrast to the
original classification scheme, the clear-sky reference value is calculated
for an AOD of 0.2 (instead of 0.3) to be consistent with the AOD measured by
the AERONET sun photometer on 24 June 2009, which was chosen as a clear-sky
reference. Because of the new O4 calibration and the new clear-sky
reference values a slightly different offset (0.85) compared to the original
version (0.74) was chosen in order to bring the results of the new algorithm
into close agreement with those of the original algorithm. A comparison of
the measured (calibrated) O4 AMF with the threshold value for a day
with occasional optically thick clouds is shown in Fig. 13.
Measured O4 AMF (blue, after normalisation) for 15 June 2009.
The magenta curve represents the SZA-dependent threshold.
Threshold for the spread of the O4 AMF
The calculation of the spread of the O4 AMF for the different elevation
angles is not affected by the new calibration, since the same offset value
is added to the O4 DAMF of all elevation angles. Thus, the same
threshold value as in the original version (0.37) is still used.
Comparison of the results of the original and updated classification
scheme. The black numbers indicate the comparison results between the
original algorithm and the updated algorithm using CIorig (1) and
between the original algorithm and the updated algorithm using
CInew (2). Blue and red colours represent cases
which were assigned or not assigned to the respective class by the original
algorithm. Bars with full colours indicate agreement between
the old and new algorithm, hatched bars indicate disagreement between the old
and new algorithm.
Comparison of the results of the original and the new
classification schemes
We applied the updated classification scheme (using either CIorig
or CInew) to the same data set as in Wagner et al. (2014). A
summary of the comparison results is shown in Fig. 14. In general, good
agreement between the original and new results is found, but especially at
high SZA (> 70∘) substantial deviations for particular
classification results also occur. These differences are mainly caused by the
different treatment of the normalisation and the SZA dependence of the
thresholds, in particular those for the absolute value of the CI (step no. 1
in Fig. 9). Due to this change, much fewer measurements for
SZA > 70∘ are assigned to the class “clear sky with low
aerosol” than in the original classification scheme. This change, however,
seems to be reasonable, since for the new algorithms the relative fraction of
the class “clear sky with low aerosol” has become more similar for low and
high SZA. The updated threshold for the CI also leads to a considerable shift
of cases from the class “cloud holes” to the class “broken clouds”.
This change also seems to be reasonable, because the
results of the new algorithm for both classes have become more similar for low
and high SZA, especially for the new CI.
Comparison of simulated CI (top: CIorig: 320 nm/440 nm;
bottom: CInew: 330 nm/390 nm) for elevation angles of 85∘
(blue lines) and 90∘ (red lines). The different symbols represent
different atmospheric scenarios. The black line represents results for a
cloud optical depth of 10.
Comparison of the clear-sky reference values of the O4 AMF for
elevation angles of 85 and 90∘.
Threshold values for observations at exactly zenith direction
(elevation angle of 90∘)
Since the zenith
observations were not performed in exact zenith direction (but instead at
an elevation angle of 85∘) for our MAX-DOAS measurements during the
CINDI campaign, the question arises as to whether the threshold
values can also be used for observations in exact zenith direction. In
Fig. 15 the threshold values for the CI (based on the defined atmospheric
scenarios) are compared for elevation angles of 85 and 90∘. The
differences of the CI are typically < 10 % (the minimum values are
almost identical). Thus we conclude that our findings can be directly
transferred to observations at 90∘. The same conclusions hold for the
quantities derived from the CI, the TSI and the spread of the CI (see also
Figs. 12 and A2 in the Appendix). In Fig. 16 the clear-sky reference values
of the O4 AMF for elevation angles of 85 and 90∘ are compared.
Almost identical values are found, indicating that the reference value for
85∘ can also be used for measurements at exactly zenith view.
Polynomial expressions for all threshold values for exact zenith direction
are provided in Table 2; tabulated values are provided in Table A1 in the
Appendix.
Polynomial expressions for the different SZA-dependent clear-sky
reference and threshold values.
Function in classification schemeQuantityScenarioPolynomial as function of the normalised SZA (S= SZA / 90∘)Clear-sky reference valueCInew (330 nm/390 nm)AOD = 0.2=8.399×S6-24.253×S5+29.143×S4-21.056×S3+7.673×S2-0.197×S+0.964Threshold valueCInew (330 nm/390 nm)AOD = 0.85=-0.654×S6+0.367×S5+2.647×S4-6.006×S3+3.576×S2-0.094×S+0.779Minimum valueCInew (330 nm/390 nm)cloudy sky=-5.261×S6+8.045×S5+0.621×S4-6.588×S3+3.029×S2+0.09×S+0.66Difference betweenclear and cloudy sky1TSI for CInew (330 nm/390 nm)CIdiff as defined in Eq. (8)=13.66×S6-32.298×S5+28.522×S4-14.468×S3+4.644×S2-0.288×S+0.304Clear-sky reference valueCIorig (320 nm/440 nm)AOD = 0.2=22.785×S6-54.778×S5+53.783×S4-33.961×S3+12.088×S2-0.563×S+0.762Threshold value1CIorig (320 nm/440 nm)AOD = 0.75=11.216×S6-25.441×S5+22.575×S4-13.89×S3+5.313×S2-0.221×S+0.542Minimum valueCIorig (320 nm/440 nm)cloudy sky=18.635×S6-57.262×S5+67.785×S4-39.153×S3+10.144×S2-0.472×S+0.41Difference between clear and cloudy sky1TSI for CIorig (320 nm/440 nm)CIdiff as defined inEq. (8)=4.15×S6×+2.484×S5-14.002×S4+5.191×S3+1.944×S2-0.09×S+0.352Clear-sky reference value2O4 AMF zenithAOD = 0.2=-81.975×S6+197.773×S5-172.649×S4+64.482×S3-7.832×S2+0.964×S+1.265
1 For use as threshold for the TSI, the polynomial has
to be multiplied by a factor α=0.06 (see Eq. 8). 2 For use
as threshold for the O4 AMF, a value of 0.85 has to be added to the
polynomial.
Further improvements of the classification scheme
In this section possible extensions and improvements of the calibration
procedure and classification scheme are discussed.
Effect of instrumental degradation for long-term measurements
Especially for long-term measurements, instrumental degradation can become an
important issue, because the results of the CI, O4 absorption (and
radiance) might systematically change over time. Wang et al. (2015) presented
a method to quantify the effect of instrument degradation using time series
of the derived quantities. They also suggested a degradation correction for
the observed CI and radiance. Unfortunately, the
effect of instrumental changes for the O4 absorption (in particular the change of the instrument's
resolution) can be very strong, and these influences cannot usually be
corrected well. In such cases, the O4 absorption can not be used for the
detection of optically thick clouds. Thus, for long-term observations the
occurrence of optically thick clouds should probably be based on observations
of the radiance. An approach for an indirect calibration of the radiance
will be proposed in Sect. 5.2.
Estimation of a SZA-dependent threshold for the radiance
Optically thick clouds can be identified using the O4 absorption or the
measured radiance (Wagner et al., 2014). Especially for long-term
measurements, the effect of instrumental degradation on the radiance is
usually much weaker than for the retrieved O4 absorption (see e.g. Wang
et al., 2015). However, as mentioned before, the calibration of the radiance
requires more effort than the calibration of the CI and O4 absorption.
In particular, measurements for days with constant and well-known AOD are
required (Wagner et al., 2015). Thus, the updated version of the cloud
classification scheme does not use the measured radiance for the detection of
optically thick clouds, because such clouds can also be clearly identified by
the O4 absorption observed in zenith direction. Nevertheless, especially
for long-term observations, the use of O4 observations for the detection
of optically thick clouds might be strongly affected by instrumental
degradation (Wang et al., 2015). For such cases it might still be useful to
identify optically thick clouds based on the measured radiance. Thus, in this
subsection we propose a simple method for the determination of a threshold
value which can be applied to the uncalibrated radiance. It is based on the
comparison of measured radiances for optically thick and thin clouds as
determined from the O4 absorption. In Fig. 17 all radiance observations
for optically thin clouds (identified by the O4 absorption) are
indicated by red dots and measurements for optically thick clouds by blue
dots. In spite of some outliers, the transition between thin and thick
clouds (as a function of the SZA) can be clearly identified. Moreover, the
threshold value used in the original version of the algorithm (indicated by
the black line) fits well to the transition between the red and blue points.
This finding indicates the possibility of determining the threshold value for
the radiance without performing an explicit radiance calibration of the
instrument via the relationship with the observed O4 absorption in
zenith direction. This method could be applied for periods in which the
effect of instrumental changes on the O4 absorption are negligible. The
derived radiance calibration could then be used for the entire period of the
MAX-DOAS measurements.
Measured radiance at 360 nm (in units of counts s-1) for
near-zenith observations during the Cabauw campaign. The blue/red dots
indicate measurements which were classified as under optically thick/thin
clouds, based on the calibrated O4 absorption using the
updated thresholds. The black curve represents the threshold value for the
radiance used in the original version of the algorithm. The measurements are
from the period 12 June to 15 July 2009.
Observations at low latitudes
The results in Fig. 15 indicate a potential problem for measurements at low
SZA (< 30∘). For such viewing geometries, the difference of the CI
for clear and cloudy observations decreases, and the CI for cloudy situations
can even become larger than the threshold values for the detection of high
aerosol loads or clouds. Thus the identification of cloudy measurements for
low SZA becomes increasingly uncertain or eventually even impossible based on
the absolute value of the CI. For such situations, it is recommended to
identify clouds by the spread of the CI as proposed by Wang et al. (2015).
Possible ways of distinguishing between the effects of clouds and
aerosols with similar optical depths
As discussed in Sect. 4.3, from measurements of the absolute value of the CI
alone it is difficult to distinguish between aerosols and clouds if they have
the same optical thickness (especially for optical thicknesses between about
1 and 6). This is especially important for the presence of continuous clouds,
because they cannot be detected by enhanced values of the temporal smoothness
indicator.
We recommend making use of the O4 observations to distinguish between
aerosols and continuous clouds. We suggest the following two approaches.
If clouds are not included in the forward model for the aerosol profile
inversion of MAX-DOAS retrievals, continuous clouds might be simply
identified by the bad convergence (large differences between the measured
O4 absorptions and the corresponding results) of the forward model.
More sophisticated versions of the forward model might even include a
simple parameterisation of clouds (e.g. based on homogenous clouds with
different optical depths and altitudes). Homogenous clouds might then simply
be identified by the retrieved layer height (if they is larger than the
typical aerosol layer height).
These possibilities should be investigated in further studies.
Observations at high latitudes and over bright surfaces
The application of the cloud classification scheme at high latitudes is
subject to two specific problems. First, measurements at small SZA are rare.
This problem mainly affects the calibration of the O4 absorption.
Second, the surface will be covered by snow and ice during large parts of
the year. Here it should be noted that, also at midlatitudes, the surface
might be covered by ice and snow during part of the year. Increased values
of the surface albedo strongly affect the atmospheric radiative transport
and thus probably also the proposed calibration approaches and derived
threshold values.
Top: simulated CI for zenith direction for clear-sky measurements above bright surfaces (albedo = 80 %).
Middle and bottom: simulated O4 AMF for low (5 %) and high
(80 %) surface albedo.
The different colours indicate results for different AOD.
Figure 18 shows simulated CI and O4 AMFs in zenith direction for high
surface albedo (0.8). Interestingly, the CI is hardly affected by the
increase of the surface albedo: compared to the results for low surface
albedo (Fig. 15, bottom), the CI values are shifted towards slightly higher
values by an almost constant value, indicating the effect of increased
multiple scattering, which also leads to more Rayleigh scattering events of
the observed photons. These results indicate that for measurements over snow-
and ice-covered surfaces a similar calibration approach for the CI as for
measurements over low albedo can be applied.
In contrast, the O4 AMFs (Fig. 18, bottom) are strongly affected by the
increased surface albedo. Compared to the results for low surface albedo,
systematically higher values are found. Moreover, for SZA < 80∘,
the O4 AMF depends strongly on the AOD. We made additional simulations
for different values of the surface albedo between zero and unity (see
Fig. A3 in the Appendix). Interestingly, for all values a specific SZA
exists, for which the O4 AMFs become independent from the AOD. The
dependence of this SZA on the surface albedo is almost linear (see Fig. 19).
SZA, for which the O4 absorption becomes independent from the
aerosol optical depth as function of the surface albedo (see also individual
simulation results in Fig. A3 in the Appendix).
From these findings we conclude that the calibration method developed for
measurements above small surface albedo has to be modified before it can be
applied to measurements over high surface albedo. Clear-sky
measurements at large SZA can probably be used for the comparison of measured and
simulated O4 AMF. Fortunately, this possibility fits well to the fact
that at high latitudes most measurements are performed at moderate to high
SZA.
These simulation results indicate that for measurements over bright surfaces
modified calibration approaches and threshold values would have to be used.
These modifications need to be tested in more detail. Here it should also be
considered that the surface albedo often changes rapidly, especially in spring
and autumn. Thus methods for the detection (and quantification) of
changes of the surface albedo based on the MAX-DOAS observations should also be
developed.
Conclusions and outlook
We developed methods for the calibration of the colour index (CI) and the
O4 absorption derived from MAX-DOAS measurements of scattered sunlight,
which are an important step towards a universal cloud classification scheme
for MAX-DOAS observations. Both calibration methods are based on the
comparison of measurements and radiative transport simulations for
well-defined atmospheric conditions (e.g. clear or cloudy conditions) and
limited SZA ranges. It should be noted that, except for the determination of the
spread of the CI and the O4 absorption (see Fig. 9), the algorithm can
also be applied to “traditional” zenith sky DOAS instruments.
For the calibration of the CI, observations under cloudy conditions are used,
for which minimum values of the CI are found (if the CI is defined as the
ratio of the intensity at the short wavelength divided by the intensity at
the long wavelength). The result of the calibration procedure is a
proportionality constant, which is applied to the measured CI.
For the calibration of the O4 absorption observations under clear-sky
conditions and for a limited SZA range are used. As a result of the
calibration procedure a constant offset is determined (the O4
absorption of the Fraunhofer reference spectrum), which is added to the
measured O4 absorption. We successfully applied both calibration
methods to measurements at two locations: Cabauw in the Netherlands and
Wuxi in China.
In the second part of our study we applied the cloud classification algorithm
described in Wagner et al. (2014) to the calibrated CI and O4
absorptions and adapted the original threshold values accordingly. Together
with the calibration method, the new set of threshold values can be used in a
consistent way for any MAX-DOAS measurement thus constituting a universal
method for cloud classification. In addition to the new threshold values, the
updated version of the cloud classification includes further important
improvements.
We used a new wavelength pair (330 nm/390 nm) for the CI. Compared to
the CI (320 nm/440 nm) used in our original study (Wagner et al., 2014)
this choice has two advantages: the change of the low wavelength to 330 nm
largely minimises the impact of the ozone absorption on the CI. The change
of the upper wavelength to 390 nm ensures that the new CI can be calculated
for almost all UV (MAX-)DOAS instruments (which often do not cover 440 nm).
The new threshold values better describe the SZA dependence. They are
obtained from radiative transfer simulations for well-defined atmospheric
scenarios. This aspect is important, since it ensures that threshold values
for possible modified CI or additional cloud-sensitive quantities could be
determined in a consistent way (based on the same atmospheric scenarios).
No radiance measurements are used in the new version, because the
absolute calibration of the measured radiance spectra is more complicated
than those of the CI and the O4 absorption. Fortunately, the omission
of the radiance measurements has no large impact on the classification
results, because the radiance was only used for the detection of optically
thick clouds, which can also be identified from the O4 absorption.
We compared the results of the updated cloud classification scheme with those
from the original version and found general good agreement. The comparison
results indicate that the updated classification scheme yields more
consistent results for high SZA (> 70∘) than the original
classification scheme.
It should be noted that our cloud classification algorithm is optimised for
MAX-DOAS measurements at midlatitudes. For measurements at high and low
latitudes specific problems occur: at low latitudes, many measurements are
performed for small SZA, for which the CI becomes indistinguishable for clear
and cloudy conditions. As suggested by Wang et al. (2015) this problem can be
partly overcome by the use of the spread of the CI for the identification of
clouds. At high latitudes, measurements at small SZA are rare, which can lead
to problems for the application of the calibration methods, especially for
the O4 absorption. In addition, frequently increased surface albedo due
to snow and ice strongly affect the atmospheric radiative transfer and thus
the prerequisites of our calibration methods. However, sensitivity studies
suggest that for such conditions modified versions of the calibration methods
and cloud classification scheme can still be applied.
Another problem with the new version of the algorithm is that, especially for
long-term observations, the derived O4 absorptions might be strongly
affected by temporal changes of the instrument properties. Thus, the
identification of optically thick clouds might be impossible for such
measurements. As a possible solution for this limitation we propose a new
indirect determination of the threshold value for the uncalibrated radiance,
which is based on the O4 measurements for periods which are not
affected by instrumental changes. Optically thick clouds could then be
identified based on the uncalibrated radiances, which are usually less
affected by instrument degradation (and can be better corrected for
instrumental changes than the O4 absorption).
Finally we identify important research areas, which should be addressed in
future studies in order to further improve the cloud classification scheme.
These areas include a more sophisticated use of CI from individual elevation
angles (instead of simply using the spread of the CI), modifications for the
cloud classification algorithm for situations with high surface albedo, an
improved discrimination of clouds and aerosols based on O4 absorptions
as well as the investigation of 3-D cloud effects on the CI.
Data availability
The raw data can be obtained on request from the authors.
Comparison of measured O4 DAMFs (only clear-sky observations,
right axis) with simulated O4 AMFs (left axis) for clear-sky conditions
and different AOD (top: aerosol layer height of 500 m; bottom: aerosol layer
height of 2000 m). The rectangles indicate the SZA ranges (30–50∘),
which are used for the calibration of the O4 measurements. The
measurements are from the period 12 June to 15 July 2009; the simulations are
performed for a day (26 June 2009) in the middle of the campaign.
Same results as in Fig. 12, but for zenith observations at exactly
90∘ elevation.
Simulated O4 AMF for zenith direction for clear-sky conditions
and different surface albedos. The different colours indicate results for
different AOD.
Tabulated values for the SZA-dependent thresholds of the different
quantities.
1 For the use as threshold for the TSI, the tabulated values
have to be multiplied by a factor α=0.06 (see Eq. 8). 2 For the
use as threshold for the O4 AMF, a value of 0.85 has to be added to the
tabulated values.
Acknowledgements
We want to thank the organisers of the Cabauw Intercomparison Campaign of
Nitrogen Dioxide measuring Instruments (CINDI) campaign in Summer 2009
(http://www.knmi.nl/samenw/cindi/), especially Ankie Piters and Marc Kroon.
We thank J. S. (Bas) Henzing and his staff for his effort in establishing and
maintaining the Cabauw AERONET site used in this investigation. This study
was co-funded by the European project QA4ECV (http://www.qa4ecv.eu/).
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: P. Stammes
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