We propose a new cloud screening method for sun photometry that is designed to
effectively filter thin clouds. Our method is based on a k-nearest-neighbour
algorithm instead of scanning time series of aerosol optical depth. Using 10
years of data from a precision filter radiometer in Innsbruck, we compare our
new method and the currently employed screening technique. We exemplify the
performance of the two routines in different cloud conditions. While both
algorithms agree on the classification of a data point as clear or cloudy in a
majority of the cases, the new routine is found to be more effective in
flagging thin clouds. We conclude that this simple method can serve as a valid
alternative for cloud detection, and we discuss the generalizability to other
observation sites.
Introduction
Sun photometry is one of the longest-employed and robust measurement
techniques for total column aerosol optical depth (AOD) retrieval
. AOD is the most comprehensive aerosol
parameter for radiative forcing studies and serves as the ground-truth for
validation of satellite data. Various surface-based networks such as AERONET
, SKYNET , and the WMO Global
Atmosphere Watch programme GAW-PFR conduct measurements
of AOD at a high time resolution, providing data for local short- and long-term
aerosol studies. As the calculation of AOD from photometer measurements is
based on the assumption of a cloud-free path between instrument and sun, the
identification and removal of cloud-contaminated data are some of the most
important prerequisites for high-quality AOD data.
The most widely used algorithm by sets a threshold on the
temporal variation of AOD, assuming a higher variation in the presence of
clouds, as one of its flagging criteria. It was developed and employed in
AERONET, as well as adapted for GAW-PFR . While
this method reliably flags thick clouds, detection of optically thin clouds
exhibiting small AOD changes, i.e. below the threshold, is not possible. This
limitation introduces a bias towards higher AOD ,
as well as a bias in the Ångström parameters, which indicate particle size.
To remedy this problem without additional manual quality control,
revised the algorithm (see Table 2 of
, for specifics). They include aureole scans in their cloud
screening routine which utilize the increased forward-scattering behaviour of
thin clouds for their identification. This is suitable for instruments that
measure sky radiance in addition to direct sun. However, the procedure takes
time and is only viable within AERONET where these instruments are employed, whereas
it is not applicable for precision filter radiometers operated within other
networks, such as GAW-PFR.
Therefore, we developed a new algorithm that can identify thin clouds, and
it works with direct sun measurements only. The main idea is that aerosol optical
depth and microphysical properties (represented by the Ångström parameters,
) show little and slow variation within a day, while clouds
introduce outliers and stronger fluctuations in these parameters.
Instead of scanning the time series of these variables, we examine their
density in a four-dimensional space with a k-nearest-neighbour
algorithm. This principle is well established in the fields of machine
learning and data mining as an efficient way to identify outliers in data
. In the context of AOD measurements, clear sky will lead
to regions of high density or short distance between points, whereas clouds will
result in less-dense regions or outliers.
Data and methodsInstrument and raw data
We use a precision filter radiometer (PFR) developed by the Physikalisch-Meteorologisches Observatorium Davos / World Radiation Center (PMOD WRC) for
the GAW network with four channels (368, 412, 501,
864 nm) and a field of view of 1.2∘. The instrument is set up
on top of a 10-storey university building in Innsbruck, Austria
(47∘15′ N, 11∘24′ E). Our operational guidelines are
based on the ones of GAW, and the instrument is calibrated by PMOD. But our
site runs independently of the network. In additional to the minutely (every minute) PFR reading
with an acquisition time of about 2 s, we measure the air temperature
and pressure at the site and monitor the overall cloud conditions with an
all-sky camera taking pictures every 10 min.
Processing and filtering
First steps in quality control include the removal of data points where any of
the four voltages is negative. Furthermore, flags are introduced if the sun
tracker records a value higher than 15 arcsec or an ambient temperature above
310 K.
After the initial filtering, aerosol optical depth (AOD) is calculated from
the voltage measurements. Starting from the Lambert–Beer law, the aerosol
optical depth is derived as
τa(λ)=ma-1[ln(V0(λ)V(λ)R2)-∑omoτo(λ)],
where V(λ) represents the measurements, V0(λ) the calibration
factors, R the Sun–Earth distance, and m the air mass in the path between
instrument and sun.
Several atmospheric constituents contribute to the optical depth, one of which
is aerosols (indicated by subscript a). Other factors (subscript o) which are
taken into account in our calculation are Rayleigh scattering
, ozone , and NO2. We use climatological values for O3 and
NO2, as well as temperature and pressure measured on site. Resulting
unphysical values (negative or infinite) of the aerosol optical depth at any
wavelength are discarded.
At each time step, we perform a linear and a quadratic fit (indicated with
subscripts l and q respectively) to τa(λ) at all four
wavelengths to derive the Ångström parameters .
2ln(τa(λ))=ln(βl)-αlln(λ),3ln(τa(λ))=ln(βq)-αqln(λ)+γqln(λ)2.
The spectral slope αl of the linear fit and the spectral
curvature γq of the quadratic fit are used in further
analysis and referred to without the subscript hereafter.
Cloud flagging
The next step in quality control of the data is the flagging of potentially
cloud-contaminated data points. Figure
shows the basic principle of the presently employed scheme and the proposed
new method.
Schematics of the cloud screening methods. (a) The Multiplet method evaluates the difference between the maximum and minimum AOD value of a set number of consecutive data points. (b) The Clustering method calculates the mean distance to k nearest neighbours in an n-dimensional space. Points for which a certain threshold of the respective measure d (indicated with the solid black lines) is passed will be identified as cloudy. These points are coloured in grey, whereas the clear points are coloured red/blue for Multiplet/Clustering.
Currently, our operational routine is based on the criteria laid out in
, with some minor adaptions due to the higher measurement
frequency according to . Data points for which the air mass
exceeds a value of 6 are considered cloudy. For lower air mass, the main
criterion for filtering data points is the difference between the maximum and
minimum AOD value within a multiplet of consecutive data points
(, uses a triplet, whereas we look at a quintuplet), which
cannot exceed a set value. For our site, we use a limit of 0.02 if AOD is
lower than 0.2; otherwise we use 0.03. This threshold is balanced to filter clouds
while retaining real AOD variations. Further limits are set on the standard
deviation of AOD within a day and the second time derivative of the
time series. Out of these parameters, the multiplet criterion is the most
relevant (more than 99 % of the flagged points), so we will refer to
the currently employed method as the “Multiplet” routine hereafter.
Instead of stepwise scanning time series, our new routine performs one
calculation for all currently available data points. We use a k-nearest-neighbour algorithm to establish the 20 closest points {P1,P2,…,P20} for each of our measurements P0 in a four-dimensional space. Then
the mean Euclidian distance between P0 and its neighbours is calculated
(referred to as d20), and P0 is identified as cloudy if this distance
exceeds a threshold. This method is usually used to identify clusters of data
points; hence, we will call it the “Clustering” routine.
The dimensions used are the aerosol optical depth at 501 nm, its first
derivative with respect to time, and the two Ångström parameters α and
γ. The first two cover temporal variations of one wavelength, and the
last two cover changes in the spectrum. To ensure that these parameters are
comparable in order of magnitude (and therefore of equal weight in calculating
the distance), the Ångström parameters derived from Eqs. ()
and () are divided by a factor of 10. Furthermore, the
finite-difference time derivative of the AOD, ΔτΔt, is
used in units of 1 per 5 min (i.e. the value is divided by 12 when t
is in hours), which is analogous to checking the AOD variation within a quintuplet of
minutely measurements.
Sky camera pictures at 15 min time resolution (time in UTC) for the time series shown in Fig. .
Points affected by a track error will not be considered in the set of possible
nearest neighbours. If this leads to less than 20 valid measurements, the
number of nearest neighbours k will be reduced accordingly down to a minimum
value of 5 points in real-time analysis. To account for the lower number of
nearest neighbours, the calculated distance is then multiplied by
20k to make it comparable to the original d20
measure. Similarly, if the number of data points identified as clear on one
particular day is lower than 30, the Clustering routine is rerun with 10
nearest neighbours during post-processing to ensure retention of a high number
of data points.
To establish the threshold for possible cloud contamination, we calculate the
distribution of d20 on about 150 clear days. We estimate a limit from
this continuous distribution, which is further fine-tuned on benchmark
days. These were selected as representatives of different sky conditions and
examples of unidentified thin clouds by a human observer of the AOD
time series, α–γ diagrams, and sky camera reference. An example
(12 March 2020) is given in Figs. and
, with additional examples in Fig. . We show
the four dimensions of our space, as well as the resulting d20 of our
data points and the sky camera pictures for better illustration of the
time series. Both algorithms pick up the thin clouds around 09:15 UTC, but only
Clustering determines some smaller contrails between 09:30 and 10:00 as
cloudy.
Three hours of an example day (12 March 2020) to illustrate the Clustering method: time series of the four dimensions used, as well as the time series of the distance measure d20. Colour of the rectangles codes for the flagging of the data point (see legend). For the distance measure d20, points below the threshold, i.e. categorized as clear by Clustering, are coloured dark grey, and cloudy points are in light grey.
As can be seen from the d20 time series in Fig. ,
there is no clear distinction between the two states (cloudy/clear) but rather a
continuous spectrum of values that has to be divided to best fit the two
categories. A lower threshold value will classify the ambiguous points as
cloudy but also risks a higher number of false positives and therefore lower
overall data retention, which matters for error of mean values calculated from
the data. Similarly, a higher threshold will cause more false negatives,
i.e. cloud-contaminated points to be identified as clear. We set the d20
threshold to 0.012 considering these aspects on our clear reference and
benchmark days.
Results and discussion
To assess the performance of the Clustering routine, we will compare it to the
Multiplet routine, using the last 10 years of measurements (2010–2019), with
3330 d of measurements in total. Of these days, 1906 are found to have
clear data points by at least one routine.
To exemplify the similarities and differences between the two routines,
Fig. shows days with different cloud and aerosol
conditions: clear, intermittent thick clouds, intermittent thin clouds, a
combination of passing thick and thin clouds, Saharan dust, and volcanic
ash. Depicted are the time series of AOD at 501 nm as well as a
scatterplot of the parameters α and γ for 5 d. Additional examples are shown in Fig. .
Comparison of cloud flagging routines on selected days with different cloud conditions as signified in the respective title. Colours are as in Fig. . Left: time series of AOD at 501 nm, Right: α–γ plots. The solid white lines show different particle radii, and the dotted white lines show different fine mode fractions; grid adapted from . Note the different x axis scales for the time series.
On a clear day, the routines agree very well, as expected. Clustering retains
more points at the beginning and end of the day, which get picked up by
limiting the air mass in the Multiplet routine. On the other hand, some slight
outliers in α and γ get flagged by Clustering. The difference in
daily mean is smaller than the measurement error.
When thick clouds are passing, with just short intervals of clear sky in
between, Multiplet hardly identifies these as such. As Clustering takes all
available data into account, it can assign points as clear even if the
immediately preceding and consecutive point are deemed cloudy. Despite
flagging less points, Clustering lowers the daily mean τ501 by 0.008
in this case, which is of similar magnitude as our measurement error.
On a day with lots of thin clouds (mainly contrails), the differences between
the two routines are pronounced: a few relatively high AOD points in the
morning (around 08:00 UTC) pass Clustering, as do points during midday
(between 10:00 and 12:00 UTC). These points, which are spectrally very
similar, are indeed cloud free, as confirmed by pictures from the sky
camera. Multiplet, however, filters less points as cloudy, which show cloud
contamination as a decrease in the fine mode fraction in the α–γ
plane. For this day, the Clustering minus Multiplet difference of daily mean
τ501 is -0.027, which is of the order of possible bias of Multiplet
reported by .
Another example of Clustering being more rigorous in cloud flagging can be
seen on the day labelled with “Various Clouds”. There were several optically
thick clouds passing, which get identified correctly, but neither their thin
edges nor the optically thin clouds on that day get picked up by the Multiplet
routine. This day gets correctly eliminated by Clustering despite Multiplet
marking 89 data points as clear.
Occasionally, Saharan dust can get transported to Austria
(e.g. ). Despite unusually high AOD, both routines
correctly identify most of the data as cloud free. Daily mean τ501 is
slightly lower (-0.005) when using Clustering, but this is still of the
order of the calibration error.
One very unusual event is depicted last: after the eruption of
Eyjafjallajökull in Iceland in April 2010, its ash plume was dispersed over
Europe . It exhibits high AOD and similar particle radii
and fine mode fraction as Saharan dust. Clustering flags more data due to the
high variation in AOD with time but still retains data in the afternoon after
about 13:00 UTC. Unfortunately, we do not have pictures available to estimate
whether the data in the morning were cloud free and should therefore be
retained. Clustering lowers daily mean τ501 significantly, leading to
-0.057 absolute and -12 % relative difference. However, such an
event is rare enough to be manually cloud screened if necessary.
Overall, the Clustering routine flags more data than the Multiplet routine,
albeit not necessarily the same data points. A more detailed comparison can be
seen in Fig. . The Multiplet routine identifies about
47.6 % of data points as cloudy and Clustering about 50.5 %,
which is a realistic value considering the amount of sunshine hours Innsbruck
receives on average .
Comparison of flagging by the two routines. The height of each area is proportional to the total number of data points in each category. Grey: both routines classify as cloudy, Red/blue/purple: Multiplet/Clustering/both classify as clear.
Histogram of the Clustering minus Multiplet difference in daily mean of AOD at 501 nm(a) and of α(b). Negative/positive values mean that the daily mean is lower/higher when screened by Clustering.
Comparison of the distribution of d20 for different time resolutions over 10 years. The bars extend from the 25th to the 75th percentiles; the minimum and median of the distributions are indicated in black. Note that the maxima are higher than the graph range and therefore not shown.
As the main objective of the new algorithm was to filter thin clouds which
previously passed the quality criteria, a higher number of flagged data points
overall is expected. On the other hand, Clustering can flag isolated outliers
without flagging the preceding and succeeding points of the multiplet, which
lowers the number of flagged points. In 88 % of all cases, the two
methods agree in the (non-)assignment of a cloud flag. Nonetheless, about
10 % of the data deemed cloudy by Multiplet are not flagged by
Clustering, whereas 15 % of the data passing the Multiplet criteria
are identified as cloudy by Clustering.
The mean AOD values of all clear points based on Multiplet flagging are
τ¯368=0.19, τ¯412=0.16, τ¯501=0.13, and
τ¯862=0.05. The respective values based on Clustering do not
differ significantly, which is partly due to the low number of data points on
which the routines disagree.
On daily timescales, Clustering eliminates 169 d for which Multiplet
would still find valid data points. On the other hand, there are only
10 d where the opposite is the case. Nonetheless, there are more than
1000 d without clear data in the 10-year record. The number of
data points on the days which are disregarded by Clustering ranges between 1
and 89. Most of these days would therefore not be considered in further
analysis in other measurement networks
either. Furthermore, as shown in Fig. , some of
these days should be eliminated as they are indeed cloud contaminated.
Clustering leads to lower daily mean AOD on about 63 % of the days
(Fig. ). The mean difference is -0.0029 for
τ¯501, which is of the order of the calibration error (0.005 to
0.01, depending on wavelength and air mass). However, on particular days this
difference can range from -0.08 to 0.04 in absolute numbers or
-62 % to +27% relative to the values based on
Multiplet screening. Similarly, Clustering leads to higher mean α on
67 % of the days. Averaged over 10 years of data, this leads to an
increment in α¯ by 0.02. In extreme cases, the difference can be as
high as +0.54. Both distributions are indicative of Clustering flagging
thin clouds which Multiplet cannot properly detect.
Finally, we investigate the performance of the proposed method at lower time
resolution. We subsampled the time series to 5, 10, and 15 min and
analysed the resulting data with the same settings for the algorithm. Key
parameters of the resulting d20 distribution from the whole 10-year data
record are shown in Fig. . The coarser the time
resolution, the higher the minimum and median d20 values and interquartile
range. This is mainly due to the overall density of points decreasing, thus
increasing the mean distance to the nearest neighbours. Furthermore, the
values of the first time derivative will be lower, so the relative weight of
this dimension decreases.
To account for the changes in density, there are two possible adjustments:
lower the number of nearest neighbours or set a higher cloud flagging
threshold. As an example of the latter, we show the time series on 12 March
2020 (same as in Fig. ) at four different time resolutions
in Fig. . With increasing the d20 threshold to 0.019,
0.027, and 0.042 respectively for 5, 10, and 15 min resolution, a very
similar flagging behaviour can be achieved at all time resolutions.
Conclusions
We presented a new approach for flagging cloud-contaminated data points from
sun photometer measurements by treating them as outliers or region of low density
in a four-dimensional space. Our routine only needs one semi-empirically
derived threshold and direct sun measurements for assigning a cloud flag. The
method tackles shortcomings of the currently employed routine based on
in the presence of optically thin clouds, such as cirrus
and contrails, which lead to systematic bias of higher instant AOD
values. Reducing this bias contributes to an improvement of long-term
statistics and trend analysis of aerosol conditions.
While fewer data points are retained overall, which is expected from being able
to filter thin clouds, the Clustering routine does not just flag more but
different data points (Fig. ). As there is an ambiguity in the
transition between humidified aerosols and clouds , an exact
discrimination between false positives and negatives for either routine is not
possible. Nonetheless, the new routine leads to lower AOD and higher α
in the long-term mean, which indicates a reduction of cloud contamination
bias.
Detailed comparison with the previously employed cloud screening routine
showed that both methods agree in their classification for the vast majority
of cases (Fig. ). Still, Clustering reduces mean AOD for most
of the days in our testing period (Fig. ). The daily mean
AOD at 501 nm averaged over the last 10 years is lowered by 0.0029,
which is comparable to instrument precision . However, on
single days Clustering reduces daily mean by more than 0.02 (up to 0.08),
which is the same magnitude as reported as bias of the Multiplet routine by
and exceeds the error of the instrument and trace gas
optical depth. Together with specific example days
(Figs. and ), this supports the notion
that Clustering corrects some cloudy points of the Multiplet routine to clear
while flagging some of its erroneously clear points as cloudy. The small
difference in the long-term mean is partly due to the specific cloud
conditions in Innsbruck and could therefore be much larger in regions with
higher prevalence of thin clouds.
Due to the nature of the Clustering routine, it needs at least k
measurements to serve as possible nearest neighbours. In our case, we chose
k=20, although dynamic adaptions can be made if there are less points
available. As the accuracy of the algorithm increases with a higher number of
data points, it is ideal for post-processing. Nonetheless, it can be used for
real-time analysis as well, given that erroneously cloudy points can be
corrected to clear when more data become available but not the other way
round (i.e. points identified as clear once will be labelled as clear
regardless of additional measurements).
While the four dimensions considered in the Clustering routine account for
variations of one specific wavelength and in the spectrum, the question arises as to
whether these can be reduced even further. Especially γ, which has the
highest error of the variables , might be a
candidate. Initial independence tests using mutual conditional information as
a measure show a strong association with α and
γ. However, outliers in γ can appear independently of α,
which is why we kept γ as a dimension and therefore data constraint.
So far we have tested the algorithm only for our instrument in Innsbruck. It
performs well in different cloud and aerosol conditions, as shown in
Fig. , and is able to alleviate AOD bias in the
presence of thin clouds. For the application at other measurement sites, the
time resolution of the data needs to be considered, as lower measurement
frequency leads to lower data density and therefore higher mean distances
between points (Figs. and
). Nonetheless, adaptations regarding the number
of nearest neighbours, the relative weight of the different dimensions, or the
d20 threshold can be easily done to optimize cloud detection with other
instruments as well.
The 2 h excerpts from selected days (in addition to Fig. ). The first five examples highlight cases where Clustering flags much less than Multiplet; the others show the performance in the presence of thin clouds. Both categories are ordered by date.
Time series of AOD at 501 nm and α–γ plots at the original 1 min resolution, as well as 5, 10, and 15 min subsampling. Note that the initial data point was chosen randomly, so the 10 and 15 min resolutions are not a further subset of the 5 min resolution. Grey squares indicate cloudy points and blue clear ones. The cloud detection threshold was set to 0.019, 0.027, and 0.042 for the lower time resolutions.
Code and data availability
The data and code that support the findings of this study are available from the corresponding author, Verena Schenzinger, upon request.
Author contributions
VS designed, implemented, and evaluated the Clustering algorithm. AK provided the raw data record. Both authors participated in writing, figure design, and interpretation of the results.
Competing interests
The authors declare that they have no conflict of interest.
Review statement
This paper was edited by Gerd Baumgarten and reviewed by two anonymous referees.
ReferencesÅngström, A.: On the Atmospheric Transmission of Sun Radiation and on Dust in the Air, Geogr. Ann., 11, 156–166, 10.1080/20014422.1929.11880498, 1929.Ångström, A.: The parameters of atmospheric turbidity, Tellus, 16, 64–75, 10.3402/tellusa.v16i1.8885, 1964.Ansmann, A., Bösenberg, J., Chaikovsky, A., Comerón, A., Eckhardt, S., Eixmann, R., Freudenthaler, V., Ginoux, P., Komguem, L., Linné, H., Márquez, M. Á. L., Matthias, V., Mattis, I., Mitev, V., Müller, D., Music, S., Nickovic, S., Pelon, J., Sauvage, L., Sobolewsky, P., Srivastava, M. K., Stohl, A., Torres, O., Vaughan, G., Wandinger, U., and Wiegner, M.: Long-range transport of Saharan dust to northern Europe: The 11–16 October 2001 outbreak observed with EARLINET, J. Geophys. Res.-Atmos., 108, 4783, 10.1029/2003JD003757, 2003.Bodhaine, B. A., Wood, N. B., Dutton, E. G., and Slusser, J. R.: On Rayleigh Optical Depth Calculations, J. Atmos. Ocean. Tech., 16, 1854–1861, 10.1175/1520-0426(1999)016<1854:ORODC>2.0.CO;2, 1999.Chew, B. N., Campbell, J. R., Reid, J. S., Giles, D. M., Welton, E. J., Salinas, S. V., and Liew, S. C.: Tropical cirrus cloud contamination in sun photometer data, Atmos. Environ., 45, 6724–6731, 10.1016/j.atmosenv.2011.08.017, 2011.Giles, D. M., Sinyuk, A., Sorokin,
M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N.,
Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin,
A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3
database – automated near-real-time quality control algorithm with improved
cloud screening for Sun photometer aerosol optical depth (AOD) measurements,
Atmos. Meas. Tech., 12, 169–209, 10.5194/amt-12-169-2019, 2019.Gobbi, G. P., Kaufman, Y. J., Koren, I., and Eck, T. F.: Classification of aerosol properties derived from AERONET direct sun data, Atmos. Chem. Phys., 7, 453–458, 10.5194/acp-7-453-2007, 2007.Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16, 10.1016/S0034-4257(98)00031-5, 1998.Holben, B. N., Tanré, D., Smirnov, A., Eck, T. F., Slutsker, I., Abuhassan, N., Newcomb, W. W., Schafer, J. S., Chatenet, B., Lavenu, F., Kaufman, Y. J., Castle, J. V., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karneli, A., O'Neill, N. T., Pietras, C., Pinker, R. T., Voss, K., and Zibordi, G.: An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res.-Atmos., 106, 12067–12097, 10.1029/2001JD900014, 2001.Huang, J., Hsu, N. C., Tsay, S.-C., Jeong, M.-J., Holben, B. N., Berkoff, T. A., and Welton, E. J.: Susceptibility of aerosol optical thickness retrievals to thin cirrus contamination during the BASE-ASIA campaign, J. Geophys. Res.-Atmos., 116, D08214, 10.1029/2010JD014910, 2011.Kasten, F. and Young, A. T.: Revised optical air mass tables and approximation formula, Appl. Optics, 28, 4735–4738, 10.1364/AO.28.004735, 1989.Kazadzis, S., Kouremeti, N., Nyeki, S., Gröbner, J., and Wehrli, C.: The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements, Geosci. Instrum. Method. Data Syst., 7, 39–53, 10.5194/gi-7-39-2018, 2018.King, M. D. and Byrne, D. M.: A Method for Inferring Total Ozone Content from the Spectral Variation of Total Optical Depth Obtained with a Solar Radiometer, J. Atmos. Sci., 33, 2242–2251, 10.1175/1520-0469(1976)033<2242:AMFITO>2.0.CO;2, 1976.Komhyr, W. D., Grass, R. D., and Leonard, R. K.: Dobson spectrophotometer 83: A standard for total ozone measurements, 1962–1987, J. Geophys. Res.-Atmos., 94, 9847–9861, 10.1029/JD094iD07p09847, 1989.Koren, I., Remer, L. A., Kaufman, Y. J., Rudich, Y., and Martins, J. V.: On the twilight zone between clouds and aerosols, Geophys. Res. Lett., 34, L08805, 10.1029/2007GL029253, 2007.O'Neill, N. T., Eck, T. F., Holben, B. N., Smirnov, A., Dubovik, O., and Royer, A.: Bimodal size distribution influences on the variation of Angstrom derivatives in spectral and optical depth space, J. Geophys. Res.-Atmos., 106, 9787–9806, 10.1029/2000JD900245, 2001.O'Neill, N. T., Eck, T. F., Smirnov, A., Holben, B. N., and Thulasiraman, S.: Spectral discrimination of coarse and fine mode optical depth, J. Geophys. Res.-Atmos., 108, 4559, 10.1029/2002JD002975, 2003.Ramaswamy, S., Rastogi, R., and Shim, K.: Efficient Algorithms for Mining Outliers from Large Data Sets, SIGMOD Rec., 29, 427–438, 10.1145/335191.335437, 2000.Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.: Detecting and quantifying causal associations in large nonlinear time series datasets, Sci. Adv., 5, eaau4996, 10.1126/sciadv.aau4996, 2019.Schäfer, K., Thomas, W., Peters, A., Ries, L., Obleitner, F., Schnelle-Kreis, J., Birmili, W., Diemer, J., Fricke, W., Junkermann, W., Pitz, M., Emeis, S., Forkel, R., Suppan, P., Flentje, H., Gilge, S., Wichmann, H. E., Meinhardt, F., Zimmermann, R., Weinhold, K., Soentgen, J., Münkel, C., Freuer, C., and Cyrys, J.: Influences of the 2010 Eyjafjallajökull volcanic plume on air quality in the northern Alpine region, Atmos. Chem. Phys., 11, 8555–8575, 10.5194/acp-11-8555-2011, 2011.Smirnov, A., Holben, B., Eck, T., Dubovik, O., and Slutsker, I.: Cloud-Screening and Quality Control Algorithms for the AERONET Database, Remote Sens. Environ., 73, 337–349, 10.1016/S0034-4257(00)00109-7, 2000.Stadt Innsbruck: Monats- und
Jahressummen der Sonnenscheindauer seit 1906, available at:
https://www.innsbruck.gv.at/data.cfm?vpath=redaktion/ma_i/allgemeine_servicedienste/statistik/dokumente38/meteorologischebeobachtungen/monatsjahressonnenscheindauerseit1906pdf (last access: 25 March 2021),
2019.
Takamura, T. and Nakajima, T.: Overview of SKYNET and its activities, Optica Pura y Aplicada, 37, 3303–3308, 2004.Valks, P., Pinardi, G., Richter, A., Lambert, J.-C., Hao, N., Loyola, D., Van Roozendael, M., and Emmadi, S.: Operational total and tropospheric NO2 column retrieval for GOME-2, Atmos. Meas. Tech., 4, 1491–1514, 10.5194/amt-4-1491-2011, 2011.
Wehrli, C.: GAWPFR: A Network of Aerosol Optical Depth Observatioins with
Precision Filter Radiometers, in: WMO/GAW experts workshop on a clobal
surface-based network for long term observations of column aerosol optical
properties, edited by: Baltensperger, U., Barrie, L., and Wehrli, C., no. 162
in GAW Report, World Meteorological Organization (WMO),
available at:
https://library.wmo.int/doc_num.php?explnum_id=9299 (last access: 28 March 2021),
36–39, 2005.Wuttke, S., Kreuter, A., and Blumthaler, M.: Aerosol climatology in an Alpine valley, J. Geophys. Res.-Atmos., 117, D20202, 10.1029/2012JD017854, 2012.