In this study, we discuss the differences in the total precipitable water
(TPW), retrieved from a Cimel sun photometer operating at a continental site
in southeast Europe, between version 3 (V3) and version 2 (V2) of the
AErosol RObotic NETwork (AERONET) algorithms. In addition, we evaluate the
performance of the two algorithms comparing their product with the TPW
obtained from a collocated microwave radiometer and nearby radiosondes during
the period 2007–2017. The TPW from all three instruments was highly
correlated, showing the same annual cycle, with lower values during winter
and higher values during summer. The sun photometer and the microwave radiometer
depict the same daily cycle, with some discrepancies during early morning and
late afternoon due to the effect of solar zenith angle on the measurements of
the photometer. The TPW from V3 of the AERONET algorithm has small
differences compared with V2, mostly related to the use of the new
laboratory-based temperature coefficients used in V3. The microwave
radiometer measurements are in good agreement with those obtained by the
radiosonde, especially during night-time when the differences between the two
instruments are almost negligible. The comparison of the sun photometer data
with high-quality independent measurements from radiosondes and the radiometer
shows that the absolute differences between V3 and the other two datasets are
slightly higher compared with V2. However, V3 has a lower dependence from the
TPW and the internal sensor temperature, indicating a better performance of
the retrieving algorithm. The calculated one-sigma uncertainty for V3 as
estimated, from the comparison with the radiosondes, is about 10 %, which is
in accordance with previous studies for the estimation of uncertainty for V2.
This uncertainty is further reduced to about 6 % when AERONET V3 is
compared with the collocated microwave radiometer. To our knowledge, this is
the first in-depth analysis of the V3 TPW, and although the findings presented
here are for a specific site, we believe that they are representative of
other mid-latitude continental stations.
Introduction
Water vapour is a crucial atmospheric component of Earth's climate
since it is the most abundant greenhouse
gas . Water vapour plays a prominent role in
the hydrological cycle through water evaporation
and condensation while providing the energy to drive moist convection
and resulting precipitation. The large-scale
flow and local circulations contribute to the large variability
of the spatial and temporal distribution of
water vapour. For weather forecasting, precipitation efficiency is
strongly related to the water vapour content,
which in turn determines the potential stability of the atmospheric column.
Thus, accurate estimations of
water vapour content are essential for meteorological and climate applications such
as radiative transfer
modelling e.g. or weather
forecasting e.g..
A common measure of the water vapour content in the atmosphere is the total
precipitable water (TPW),
defined as the total water contained in a column of unit cross section extending all the way from
the earth's surface to the top of the atmosphere ().
Initially, radiosonde measurements were used to measure
TPW e.g.. Although, the radiosonde measurements
are reliable they are limited, for example, by freezing of moisture sensors, which leads to errors in
the estimation of moisture, or by the phase lag between the dry and wet bulb sensors .
In addition, the global radiosonde network coverage is limited e.g..
Thus, considering the large variability of water vapour both in time and space,
it becomes obvious that
soundings provide a very limited spatio-temporal representation of TPW .
To overcome these issues, a number of methods for TPW estimation based
on active or passive remote-sensing techniques, either from the ground or the space, have
been developed. From the ground the most common
ones include the GPS system , microwave radiometers , Cimel
sun photometers , Fourier transform infrared spectroscopy and Raman lidars
. Recently, techniques have
been developed for the retrieval of TPW from
measurements of the precision solar spectroradiometer at the
World Radiation Center (WRC) Davos ,
the PESR/PREDE-POM sun–sky radiometers and from MAX-DOAS
observations .
The quality of the retrieved TPW from each instrument is assessed through comparison with other
independent measurements. In general, radiosondes and the global GPS
systems have been used for the evaluation
of TPW measurements from satellite data . Of particular
interest is the evaluation of the TPW from the Cimel sun photometer
that is part of the AErosol RObotic NETwork (AERONET), a network with global
coverage. Several studies have validated
the TPW retrieval from Cimel sun photometer with radiosondes, GPS
and microwave radiometer measurements
e.g..
Although their network is dense, the Cimel sun photometer has a series of limitations
because they require sunlight, which indicates that at least
the solar disc must be free from clouds
for TPW retrieval. These conditions restrict the availability of data
just during daytime and thus reduce
the temporal availability of the datasets. Nevertheless,
demonstrated that the Cimel sun photometer can provide extended time series with good
temporal resolution. A lunar photometer could provide TPW
during night-time e.g., but this product
is not yet available in the AERONET database.
In this article, we focus on measurements conducted at the
Romanian Atmospheric 3D research Observatory (RADO). The reason for this is that RADO is
the only site, to our knowledge, in southeastern Europe that has long-term
measurements of TPW from three independent instruments: Cimel sun photometer,
microwave radiometer and radiosondes. Therefore, it can be used as a test bed
to assess the quality of the measurements, especially because the radiometer
provides continuous high-quality observations of TPW. Furthermore, this site
is one of the few potential sites from southeastern Europe that can be used for
satellite calibration and validation activities. Thus, the evaluation of the RADO measurements is
an essential process towards this goal.
Recently the newly released version 3 of the AERONET products
has become publicly available. This new version
incorporates significant improvements for direct sun measurements,
such as a new, improved cloud screening
algorithm, automated quality check procedures, inclusion of higher
air mass data, and new temperature characterisation
and corrections to all channels .
To our knowledge, no study has evaluated the newly released
version of the TPW
from the AERONET. In this study,
the quality of the TPW measured by three different instruments
(i.e. HATPRO-G2 microwave radiometer, Cimel sun photometer
and Vaisala RS92 radiosondes) at a site in southeastern Europe is assessed.
The paper is organised as follows. The instruments
used in this study are described in Sect. . In Sect. ,
the climatology of
the annual cycle of TPW observed over the study area
and the comparison of the different datasets employed for
the measurements of TPW are presented. More specifically, the differences
between the microwave radiometer and
radiosondes, and Cimel V2 and V3 and the radiosondes, Cimel V2 and V3
and the radiometer are analysed, and the factors
affecting their agreement are assessed. Section summarises this article.
Data and methodologyMeteorological parameters
The HATPRO-G2 microwave radiometer and the Cimel sun photometer used in this
study were located at the Romanian Atmospheric 3D Observatory (RADO,
44.82∘ N, 26.82∘ E, 93 m a.s.l.), part of the National
Institute of Research and Development for Optoelectronics (INOE2000). The
observatory is located in the city of Măgurele, Ilfov, at the central
part of the Romanian plain, approximately 10 km southwest of Bucharest, the
capital city of Romania, and is surrounded by research facilities, residence
buildings and a small forest. The central Romanian plain has a temperate climate
influenced by the western circulation, the east European anticyclone, the
Mediterranean cyclones and the tropical advection
. The relative humidity at Măgurele, as calculated
from observations from the RADO weather station between 2007 and 2016, has high
values (>80 %) during November–February and low values (<60 %) between
May and September (Fig. ). Since the Cimel sun photometer performs
measurements only when the solar disk is free of clouds, two critical
parameters for the availability of the Cimel sun photometer data are the sunshine duration
and the cloud fraction. For the calculation of the climatology of the
sunshine duration the Surface Radiation Data Set – Heliosat (SARAH) –
Edition 2 of the EUMETSAT's Satellite Application Facility on
Climate Monitoring (CM SAF) was used. The sunshine duration (SDU) product is
the daily sunshine duration per day at which direct normal irradiance (DNI)
exceeds the WMO threshold of 120 W m-2. SDU is
derived by the ratio of sunny slots to all slots during daylight multiplied
by the length of day. The length of day is calculated depending on the date,
longitude and latitude. The length of day is restricted by a threshold of the
solar elevation angle (SEA) of 2.5∘. The SDU product is provided on a
regular latitude–longitude grid with a spatial resolution of
0.05∘× 0.05∘. In this study, for the calculation of the climatological
sunshine duration, the daily SDU at the closest pixel over Măgurele during
the period 2005–2015 was used. A full description of the SDU product can be
found at . For the calculation of the cloud fraction
climatology, the CM SAF cloud property dataset using SEVIRI – edition 2
(CLAAS-2; ) was used. The cloud fractional cover (CFC) is
defined as the fraction of cloudy pixels per grid cell compared to the total
number of analysed pixels in the grid cell and is expressed as a percentage. In
this study the daytime CFC during the period 2005–2015 was used. The daily
CFC product is provided on a regular latitude–longitude grid with a spatial
resolution of 0.05∘× 0.05∘. A full description and
evaluation of the CFC product is given in . High cloud
coverage (>70 %) affects the RADO site from November to February
(Fig. ), while the lowest cloud fraction (<40 %) is during July–August. The rest of the months the cloud fraction ranges between 50 % and
60 %. The high percentage of clouds, in combination with the small
sunshine duration (Fig. ), during late autumn and winter affects the
availability of the Cimel sun photometer data during these months. Thus calculations of
multi-year annual mean TPW values from Cimel sun photometer observations are biased from the
highest number of data points during summer. The sunshine duration exhibits a
clearly annual cycle with a minimum during winter and maximum during summer and
ranges from ∼2.3 h during January (minimum) to up to more than 10 h during July (maximum) (Fig. ).
Annual cycle of the cloud fraction, sunshine duration and
relative humidity at Măgurele (adapted from Fig. 2).
Cimel sun photometer
A Cimel Electronique 318A sun photometer (serial number
359) was installed at the RADO facilities in July 2007
and was operated until May 2016,
when it was reallocated to Poland. As a replacement a Cimel lunar photometer has been operating since 2016,
but data from this instrument have not been used in this study due to the
limited availability of level 2 data.
The Cimel sun photometer is the standard instrument of AERONET used for the study of
the aerosol total column load. It performs spectral measurements of the direct sun
irradiance and sky radiance at six discrete
wavelengths using interference filters. The filters are centred at
the wavelengths of 340, 380, 440, 500, 675, 870 and
1020 nm. An additional channel at 935 nm is used for the retrieval of the TPW.
The instrument is calibrated almost annually
following the procedures and the guidelines of AERONET. TPW is calculated based on a modified expression of
the Beer–Bouguer–Lambert law. Since provide a full description of the
TPW retrieval algorithm (see Sect. 2 of that paper), in this section just the major
differences between V2 and V3 and some other factors that may influence the TPW retrieval are
discussed. For the computation of TPW a necessary preliminary step is the subtraction
of the AOD and Rayleigh optical depths
from the total optical depth at 935 nm. Since AOD is not calculated directly for the 935 nm
channel due to the strong effect of water vapour, the AOD at 870 nm is extrapolated at the 935 nm
using the Ångström exponent (AE) at 440–870 nm.
The main differences in the computation of TPW in V3 are that the new algorithm accounts
for an
updated continuum look-up table , using total internal partition sums
and using the extraterrestrial spectral solar irradiance from .
In this study all available data from July 2007 to May 2016 for level 2 from
versions 2 and 3 of AERONET algorithms were
used. Level 2 data are screened for clouds, quality controlled, and pre-field and post-field calibrations are applied. The newest released version 3 incorporates
improvements for the direct sun measurements
(1) related to the screening of clouds, (2) the automated data quality assurance,
(3) inclusion of data with higher air masses
(i.e. from 1 to 7, in contrast with V2 that ranges from 1 to 5) and (4) implementation of
spectral temperature corrections based
on laboratory measurements (i.e. unlike version 2 that was based on the
manufacture specifications). Details about all the
improvements implemented in V3 of AERONET can be found at .
The AERONET TPW measurement uncertainty is estimated to be <10 % ,
which is consistent with the one-sigma uncertainty for AERONET V2 provided by
of 7 %–9 % after evaluating the TPW from AERONET at the U.S. Department of Energy Atmospheric Radiation Measurement
Program (ARM) sites against microwave radiometers, GPS and radiosondes.
Microwave radiometer
The HATPRO-G2 microwave radiometer used in this study was produced by Radiometer Physics GmbH.
It is a passive instrument working
in the microwave regime. It consists of two working bands at 22–31 and 51–58 GHz,
each with seven channels. The relevant receiving
optics, the ambient load, the internal scanning mechanism, the electronics and the data
acquisition system of the radiometer are
described in . For humidity profiling only the first band is used.
The vertical resolution for profiling is variable,
ranging from 200 below 2000 to 800 m for altitudes higher than 5000 m.
Water vapour emission dominates the signal in
the 23.8 GHz channel, which is on the wing of the 22.2 GHz
water vapour absorption line, whereas liquid water emission
constitutes the primary portion of the signal at 31.4 GHz . From
these two observations, both integrated water vapour (IWV) and liquid
water path (LWP) can be retrieved.
The retrievals are performed in the zenith direction.
In this study, the IWV was used, which presents, according to
the manufacturer (RPG-HATPRO-G4 series
microwave radiometers for continuous atmospheric profiling, available at
https://www.radiometer-physics.de/download/PDF/Radiometers/HATPRO/RPG_MWR_PRO_TN.pdf,
last access: 13 July 2018),
an accuracy of ±0.2 kg m-2 RMS and noise of 0.05 kg m-2.
Considering the density of liquid water, the IWV expressed in kilograms per square metre is equivalent
with the TPW expressed in millimetres of liquid water
. In this study measurements are performed each 2 s.
To ensure the high quality of measurements, the instrument
is absolutely
calibrated with liquid nitrogen every
6 months following the instructions of the manufacturer. All
the available data between 16 December 2009 and 31 December 2017 were used. The internal data quality
has three options for filtering level 2 data (retrieved atmospheric data).
The “Flag Data Quality” (level 2) option does not
filter the level 2 data according to the quality level but flags each data sample
in the rain flag byte. With the option “Remove Medium/Low Q.”, medium- and
low-quality samples are not transmitted by the radiometer.
In this case, the sample sent to the personal computer that controls
the instrument is the repeated latest high-quality sample. The filter
“Remove Low Quality” only removes the worst-quality data and
transmits high- and medium-quality data. In the present study, the
first option was used for the creation of the level 2 data; thus only
data that have been flagged as rain
from the internal sensor of the instrument have been removed.
In addition, all days
with data have been visually inspected for identification of instrumental malfunctions, which can
include periods when there are no changes in the TPW values due to
bad transmission of data or periods with low-quality data
(i.e. when the TPW remained high after rain, until returning to its previous levels after some time).
Radiosondes
The radiosonde measurements were obtained from the sounding database
maintained by the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html, last access: 13 July 2018).
Between July 2007 and December 2017, 3760 radiosonde measurements for 00:00 UTC and 3759 for 12:00 UTC were available from the Bucharest site, situated at
approximately 30 km northeast from the RADO facility and operated by the
Romanian National Meteorological Administration. The radiosondes used during
the study period were of the Vaisala RS92 type. For this type of radiosondes,
showed that the accuracy of the humidity sensor during
daytime depends on the calibration error and the dry bias due to the solar
heating effect and during the night-time just from the
calibration error. The overall uncertainty of TPW from radiosonde
measurements has been estimated to be ±5 % .
TPW over the entire sounding was calculated as
TPW=1ρg∫p1p2xdp,
where xp is the water vapour mixing
ratio at the pressure level p, ρ is the density of water
and g is the acceleration of gravity.
Methodology
For the computation of the daily mean values of TPW
all available measurements that qualify the quality criteria were used. A preliminary step was the
averaging of TPW from the microwave (MWV) radiometer into 1 min intervals.
Table gives an overview
of the total number of observations, along with their total number
of corresponding days that have been analysed for each instrument and
for the different versions of the
AERONET algorithms in order to compute the daily averages. Due to the different
schedule of each instrument
and the gaps in each database, the computed averages cannot be directly compared between them.
For a direct comparison we extracted the common measurements between V2 and V3 and they were
averaged for ±20 min around the launch time
of the noon radiosonde. The same averaging was applied to the MWV radiometer data, so as to
extract a dataset of simultaneous or nearly
simultaneous measurements from all instruments. Since the exact hour of the radiosonde launch is not
explicitly known, this 40 min interval has been selected in order to ensure that the instruments
detect the same air masses and to limit the atmospheric variability that takes place on
timescales larger than 1 h . If the GPS information
of the radiosondes is available,
a further improvement in the coincidence criteria would be to average
the Cimel sun photometer data for ±20 min since
the time the balloon reaches the altitude of the 4 km, following .
However, in our case access to the raw data is not available; thus the averaging was performed ±20 min
around the launch time. For the comparison of the MWV radiometer and Cimel sun photometer data with the
radiosondes,
the same coincidence criteria as described above were used. The comparison of the two different
algorithms of AERONET is based just on their common measurements. This way the
comparison provides insight into the TPW calculation differences between the two algorithm versions
rather than impacts due to cloud screening and instrument quality controls.
For the comparison of the Cimel sun photometer data with the MWV radiometer, the exact time
matched measurements were selected. For the evaluation of
the MWV radiometer and Cimel sun photometer data, the radiosonde TPW was used as the
reference measurements because
they are considered more representative of the actual atmospheric conditions. However,
since the radiosonde site is at a distance of ∼30 km from the
RADO facilities, there is the possibility that
the different instruments detect air masses with different
characteristics, especially when the radiosondes
are affected by southwest winds. Thus, the calculated uncertainty
expressed as the 1σ of the mean
difference among the different datasets is expected to be little
overestimated when compared to the
radiosondes. The absolute and relative differences between two
sets of measurements were defined as
X-Xref
and
100⋅(X-Xref)Xref,
respectively, where (Xref) is the reference measurement (i.e. the radiosonde
measurement, except for the comparison between the Cimel sun photometer and the microwave radiometer).
Overview of the measurement characteristics and datasets used in this study for the period 2007–2017.
InstrumentRetrieval methodTotal numberTotal number ofData frequencyof observationsdaily mean valuesRadiosondesThin-film capacitance relative humidity7503378412 hsensor use of balloons for vertical profilesRadiometerSky brightness temperature at 23.8 GHz1 859 31516122 swater vapour absorption bandCimel V2Solar direct irradiance33 3241293∼20 min forat 940 nm absorption bandclear sky conditionsCimel V3Solar direct irradiance35 3731325∼20 min forat 940 nm absorption bandclear sky conditionsResultsClimatology of total precipitable water in Măgurele
The times series of the daily mean values for the TPW from the different instruments
employed in this study are shown
in Fig. . In general, the radiosonde measurements are available twice per day
(i.e. 00:00 and 12:00 UTC).
The Cimel sun photometer measurements are restricted only during daytime and under conditions
that require the solar disc to be clear of clouds, while the microwave
radiometer performs measurements during daytime and night-time under all weather conditions.
Although there are differences in the measurement schedule, all three instruments depict the
same annual cycle, demonstrating their capability of
performing long-term measurements for climatological applications (Fig. ). The gaps
in Cimel sun photometer time series are due to the calibration of the instrument, which requires the
reallocation of the instrument.
Data gaps of the microwave radiometer are due
to malfunction of the instrument or controlling personal computer
(usually solved with a restart after a maximum of couple of days)
or due to the relocation of the instrument during different measurement
campaigns (data not included in this study).
Furthermore, in the beginning of 2016 the instrument was sent to the
manufacturer for testing and replacement of several components.
Time series of the daily mean values of the total
precipitable water during the period 2007–2017 based on
measurements from radiosondes (blue dots), a microwave radiometer (orange dots),
and Cimel sun photometer version 2 (yellow
dots) and version 3 (magenta dots) of the algorithm.
Summary of the daily mean statistics of all
instruments and algorithms for the period from July 2007 to December 2017.
The observed differences in the mean values calculated from all
instruments (Table ) can be mostly attributed to
the different operating period of each instrument and their different sampling rates.
However, even though the overall mean from Cimel sun photometer measurements
is not significantly different from the radiosondes and the microwave radiometer estimates,
Cimel sun photometer measurements are actually biased towards the higher TPW values observed during the summer.
Since the cloud fraction during the winter months at Măgurele is pretty high,
more than 70 % from November to January (Fig. ) when TPW also attains its
minimum values (Fig. ), the number of Cimel sun photometer observations is substantially
reduced, leading to the inclusion of a reduced number of low-TPW days in the Cimel sun photometer dataset.
This observed summer (wet) bias is partly compensated for by the inherent Cimel
sun photometer
dry bias e.g. due to restrictions of measurements
when the solar disc is cloud free and thus the overall TPW mean
from the Cimel sun photometer is similar to the other methods (Table ).
This dry bias for the mid-latitudes is more pronounced
during winter and can range from 25 % to 50 %, while in summer it ranges from 5 % to 25 % .
The clear-sky monthly bias can be clearly seen in the mean monthly values of TPW (Table ),
for which the Cimel sun photometer measurements during January can be lower by ∼25 % compared to the radiosondes
while the summer mean monthly values are lower by only a few percent (e.g. ∼4 % for August) (Fig. a). Such behaviour is not
observed for the MWV radiometer, with the differences in their mean
monthly values ranging within ±10 % for all months
(Fig. b).
The minimum values daily values can be as low as 2 mm, while the maximum values exceed 44 mm (Table ).
The peak-to-peak range during the year (i.e. from minimum to maximum) can be up to 20 mm.
Mean monthly and median values of TPW and their IQR from the different instruments used in this study. All units are in millimetres.
Monthly variation in total precipitable water from (a) radiosondes during the period 2007–2017,
(b) microwave radiometer during the period 2009–2017, (c) Cimel sun photometer version 2 data
and (d) Cimel sun photometer version 3 data for the period 2007–2016. The median values are shown
as the red lines, the interquartile range (IQR) is spanned by the vertical bars and the whiskers
show the 1.5 IQR. The red + symbols show the
outliers in the datasets.
Monthly ratio of TPW among microwave radiometer, Cimel V2
and V3, and radiosondes (a) for all the available
measurements and (b) for their datasets.
The annual cycle of the TPW as depicted by all three instruments has a minimum
during winter months (DJF) and a maximum during summer months (JJA)
(Fig. ). Higher air temperature during the summer implies a larger
capacity to store water vapour without saturation . The
small differences in the monthly median values for all instruments are due
to their different sampling rates. For example, the increased number of
outliers in the radiosonde box plots, compared to the other instruments, can
be attributed to the limited number of measurements (i.e. a maximum of two
per day). Thus, some high or low values are not smoothed by averaging all
measurements during the day (Fig. a). In any case, the main aim of
the analysis presented here is to show that the annual cycle of TPW can be
depicted fairly well by all instruments and demonstrate their capabilities
for long-term monitoring for climatological applications. A direct comparison
of the daily values from each instrument is not valid due to the very
different sampling rates and the diurnal variation in TPW as shown in
Fig. . An overview of the statistical values based on all available
measurements for all three instruments is shown in Table . A dataset
was constructed, as described in Sect. 2.4, containing the common
measurements and thus allowing for a direct comparison among the three
instruments. This dataset consists of a total of 234 days during the
measurement period, which is limited by the Cimel sun photometer observations during
conditions in which the solar disc was free of clouds. For this reason, the
comparison of the different instruments is not affected by the clear-sky dry
bias. An overview of the long-term averages of the common measurements from
all instruments can be seen in Table . The MWV radiometer has the
higher mean TPW (18.57 mm), followed by the radiosondes (17.96 mm), Cimel V2
(17.80 mm) and finally Cimel V3 (17.65 mm). Although this dataset consists of
nearly time-matched measurements, the small differences in the long-term
averages may occur from differences in the geometry of the measurements and
subsequently the sounding of air masses with different characteristics. For
example, the Cimel sun photometer measures the direct sunlight and can track
the sun between clouds, while the MWV radiometer measures the zenith sky radiance and it may not
be completely cloud free for the same sky. The radiosondes are also
launched from a different area, which could possibly track different
air masses. The mean monthly TPW values (Table ) appear to have very
good agreement (within ±5 %) among the different instruments when
using common data periods (Fig. b). The Cimel sun photometer clear-sky dry bias
that had been observed, especially during the winter months in the long-term
averages when computed from all measurements (Table ), has been
cancelled out, as can be clearly seen in Fig. b.
Same as Table 2 but just for the common measurements from all instruments.
Diurnal variation in total precipitable water from the radiosonde (magenta triangle), microwave
radiometer (blue dots) and Cimel sun photometer (V2 and V3 of the algorithm, red circle and
orange cross, respectively) for 6 selected days (i.e. to cover all seasons
and have a relative high number of Cimel sun photometer measurements). The time is in UTC (i.e. local time – 2 h).
The red line indicates the range of the SZAs under which Cimel sun photometer measurements were performed.
Sensitivity of the instruments to diurnal variation
As mentioned previously, the temporal resolution of the microwave radiometer on the order of a few seconds in
combination with its capacity to operate under all weather conditions allow the detection
of the TPW diurnal variations. In addition,
under clear-sky conditions, the Cimel sun photometer performs measurements at
about every 15 min. To verify if
both instruments depict the same daily cycle, the diurnal variability for 6 selected days was examined. The days
were selected to meet the following conditions: the Cimel sun photometer measurements cover most of the
day and in particular for high solar zenith angles (SZAs,
>70∘),
no discontinuation due
to clouds in Cimel sun photometer measurements was observed from
sunrise to sunset and the measurements cover all seasons.
The Cimel instruments and MWV radiometer depict the same
diurnal variation during daytime (Fig. ),
with some small differences
in their absolute values that are further
investigated in the following sections. For some of the
selected days (i.e. 8 June 2012, 26 October 2013) there are
differences in the diurnal variation during the early morning or late
afternoon hours, which are most likely artefacts
associated with direct sun measurements at high
air masses (e.g. SZA > 70∘).
These artefacts are due to Cimel clock deviations that
result in some minor deviation in the optical air mass calculation and
thus slightly impact AOD but within uncertainty expectations
(see Sect. 3.3.1 of ).
Comparison between radiosondes and microwave radiometer
To account for spatial and temporal differences between the radiosonde and
the microwave radiometer, all the microwave radiometer data were averaged
over an interval of 40 min centred on the radiosonde launching time. A
total number of 2820 common measurements, out of which 1416 during daytime
(i.e. at 12:00 UTC) and 1404 during night-time (i.e. at 00:00 UTC), were
extracted for the comparison. The relative difference between the two
datasets is in general within ±25 % (Fig. ). The MWV radiometer
slightly overestimates TPW with the overall difference from the radiosondes
to be 1.82±9.61 % (0.17±1.66 mm). This overestimation is more
evident during daytime (i.e. 3.12±9.93 % or 0.35±1.71 mm) due
to the radiation dry bias effect that affects the radiosondes
e.g., which is more pronounced for TPW values less than
10 mm (Fig. b). During night-time the differences are almost
negligible (i.e. -0.50±9.10 % or -0.01±1.57 mm).
Time series of the relative difference (%) between the TPW from the microwave radiometer and
the radiosonde during the period 2009–2017.
(a) Scatter plot of TPW values derived from microwave radiometer and radiosondes. The blue dashed
line represents the identity line and the red
solid line is the least-square linear fit. The regression coefficients
are displayed along with their 95 %
confidence interval (in parentheses).
(b) Frequency distribution of the relative mean
difference in TPW between microwave radiometer and radiosondes in bins of 2.5 %.
The two datasets are highly correlated (Fig. a; R2=0.97), with the majority of
the points over the y=x
line. However, for the higher values of TPW (i.e. TPW > 30 mm) an increased scatter of the data is observed,
without being significantly high. The histogram of the relative differences between
the two instruments, which peaks at about 1 % (Fig. b), does not follow a
normal distribution, as indicated by the Shapiro–Wilk test for
normality (p value < 2.2e–16).
About 96 % of the data are within ±20 %,
while ∼78 % lie in the range of ±10 %. The difference between the two
datasets has a small dependence from
the TPW amount of -0.169 % mm-1 (Fig. a). This dependence is
more evident for the daytime
measurements (i.e. for radiosondes launched at 12:00 UTC; Fig. b),
while for the night-time measurements
the dependence is almost negligible (i.e. -0.092±0.052 % mm-1; Fig. c).
The best agreement between the two datasets
is achieved for TPW values ranging between 15 and 35 mm. The increased difference for TPW
values higher than 40 mm
cannot be fully evaluated due to the very small number of
observations (i.e. just 19 measurements).
Dependence plot of the relative difference of the TPW from the microwave radiometer and the radiosondes from the total amount of TPW for (a) all points,
(b) the daytime measurements and (c) the night-time
measurements. The black dots show the average difference in bins of 5 mm and the error bars represent
their standard deviation. The linear fit is based on all measurements.
Comparison of Cimel V2 and V3
To assess the differences of the TPW derived from the newly released version 3 from
AERONET and the previous version 2, only
their common measurements were used. The difference in the number of observations between the two versions
(see Table ) arises from the fact that they have different quality control and
cloud screening procedures .
A total of 27 707 common observations between the two versions were extracted for comparison.
In general, the differences between the two versions are small, ranging
within ±2 % and rarely exceeding 5 %, with V2
having higher values than V3 (Fig. a and b). The overall difference between
the two datasets for the period 2007–2016 is 0.60±0.91 % (0.08±0.14 mm).
The differences at the AOD at 870 nm between the two different algorithm versions
(Fig. c) are generally pretty low and rarely exceed the ±0.01 AOD units. The cyclic nature of the AOD differences
(Fig. c) suggests the variation in the AOD with temperature for version 2. The V2 data are not temperature-corrected for the 870 nm filter and this produces a difference in AOD between temperature-corrected (V3) and not-corrected (V2)
data due to this specific filter before 2009. The 870 nm filter
was changed in 2009 in this
specific instrument and its dependence on temperature was
a magnitude lower than the initial filter. As a result,
the filter used in the instrument from 2009 and onward
shows less deviation from V2 since the temperature
correction needed for the filter is minimal. This is a clear example of how implementation
of temperature correction in version 3 significantly improved the AOD and TPW, before 2009.
Time series of the (a) absolute and (b) relative differences between
level 2.0 of V2 and V3 TPW and (c) differences
of AOD at 870 nm between V2 and V3 from Cimel sun photometer measurements, for their
common measurements during the period 2007–2016.
Dependence plot of the relative difference of the TPW from V2 and V3 AERONET algorithms from (a) the total
amount of TPW and from (b) the temperature of the censor. The black dots show the average difference
in bins of 5 mm and 5 ∘C, and the error bar represents the standard deviation of the
mean. The linear fit is based on all measurements.
To further evaluate the differences between TPW from the two different versions of
the AERONET algorithm, a series
of factors that could affect the measurements (i.e. the total amount of TPW, the SZA,
the sensor temperature and the differences at the AOD at 870 nm)
were examined. No significant dependence was found with SZA when comparing the two versions. The relative difference
between V2 and V3 show a dependence on
TPW (Fig. a).
The biggest differences (i.e. ∼2.5 %) are observed for TPW values lower than 10 mm,
while the agreement between the two
datasets improves with increased TPW
values. However, the decrease in the relative difference of TPW between
V2 and V3 is due to the different treatment of the
temperature correction in the versions.
As shown in Fig. the lowest TPW values appear during
wintertime, when the temperature is low as well.
Corresponding to these low temperature values
the differences between V2 and V3 shows a mean
maximum value of ∼2.5 % (Fig. b).
A very pronounced dependence is also seen
by the temperature of the internal
sensor of the instrument. This dependence is due
to the different temperature coefficients in the two versions of the retrieval algorithm.
For V2 the temperature coefficients are based on the manufacturer specifications,
while in V3 the temperature characterisation
is based on laboratory measurements during the calibration of the instrument.
The highest positive differences, on the order of
∼5 %, appear for low temperatures
(<10∘C). For the whole range of temperatures that are
recorded in the instrument (i.e. ∼50∘C) a total
difference of up to 5 % is observed (Fig. b).
Time series of relative differences between (a) level 2.0 V2 TPW from the Cimel sun photometer and the
radiosondes and (b) from level 2.0 V3 TPW from the Cimel sun photometer, during the period 2007–2017.
Comparison between the Cimel sun photometer and radiosondes
To have a better overview about how the differences between the two versions affect the agreement with
the other instruments, the evaluation of Cimel sun photometer measurements with radiosondes and the microwave radiometer was
based on the common dataset between the two different
algorithm versions (Sect. ). Since
this Cimel model is not capable of night-time measurements,
the comparison is limited to daytime measurements only
(i.e. radiosondes launched at 12:00 UTC).
To account for spatial and temporal differences, the same
procedure with the one described for the comparison
between microwave radiometer and radiosondes was used
(i.e. averaging all Cimel sun photometer points over an interval of
40 min centred on the radiosonde launching time).
Thus, a total of 682
common measurements were identified.
The differences between the Cimel sun photometer and radiosondes range within ±20 % (Fig. ),
while the overall mean difference is -1.95±10.97 % (or -0.39±2.1 mm)
and -2.74±10.56 % (or -0.50±2.05 mm),
for V2 and V3. These results are in
agreement with previous studies that showed that AERONET
sun photometers generally underestimate TPW in
comparison with other instruments
. Version 3 shows an increased underestimation
of TPW in comparison with the radiosondes;
however the standard deviation is slightly
better than in
the previous version (Fig. b).
The TPW from both versions is highly correlated with the TPW from the radiosondes (i.e. R2
is 0.95 for both Cimel V2 and Cimel V3; Fig. a and c), with
the slope of the least-square regression line being very close to unity. The histogram
of the relative
differences between the two datasets has a very small kurtosis towards negative
values, for both Cimel V2 and Cimel V3 (Fig. b and d).
According to the Shapiro–Wilk test for normality , it does not
follow a Gaussian distribution (p value = 0.01427 and 0.004603, for V2 and V3, respectively).
For Cimel V2 about 65 % of the differences
are within ±10 %, while ∼93 % are within ±20 %.
For Cimel V3 the respective numbers are 67 % and 93 %.
The low number of the coincidence measurements, and their
big scatter among
different SZAs, TPWs and temperatures
of the sensor, does not allow a further evaluation of the influences from these factors.
Scatter plot between the TPW from (a) the radiosondes and Cimel V2 and (c) Cimel V3. The red thick line
shows the least-square regression line and the blue dashed line is the identity line. The regression coefficients are displayed along with their 95 % confidence interval (in parentheses).
Frequency histogram of
the relative difference between (b) the TPW from the radiosondes and Cimel V2 and (d) Cimel V3.
Time series of the relative differences between (a) level 2.0 V2 TPW from Cimel sun photometer and the microwave
radiometer, and from (b) level 2.0 V3 TPW from the Cimel sun photometer, during the period 2009–2017.
Comparison between the Cimel sun photometer and radiometer
The comparison between the Cimel sun photometer and the microwave radiometer is
based on their coincident measurements, with
the microwave radiometer observations averaged over a 1 min interval. This common dataset
consists of
8505 observations for the period December 2009–May 2016.
The differences between the TPW from both
versions of AERONET algorithms are in general
within ±10 % (Fig. ).
The Cimel sun photometer underestimates the TPW by 2.75±5.85 % (or 0.70±1.22 mm)
and 3.57±5.54 % (or 0.81±1.17 mm),
for V2 and V3, respectively. The comparison of the Cimel sun photometer with the MWV reveals a
lower overall uncertainty of ∼6 % estimated as the one sigma of the mean difference, compared
to the one (∼10 %) that was calculated from the comparison of the Cimel sun photometer with the radiosondes. This lower
uncertainty can be attributed to the collocation of the Cimel sun
photometer
and MWV and subsequently the sounding of the same
air masses from both instruments. The distance between the RADO site
and the radiosonde launching site increases the
estimated uncertainty of the retrieved TPW from Cimel sun photometer;
however it still remains within the limits
that have been estimated by other studies in the past e.g..
Scatter plot between the TPW from (a) the microwave radiometer and Cimel V2 and (c) Cimel V3.
The red thick line
shows the least-square regression line and
the blue dashed line is the identity line.
The regression coefficients are displayed along
with their 95 % confidence interval (in parentheses).
Frequency histogram of the relative difference
between (b) the TPW from
the microwave radiometer and Cimel V2 and (d) Cimel V3, respectively.
Dependence plot of the relative difference of the TPW from Cimel sun photometer and the radiometer from the SZA (a) for Cimel V2 and (b) Cimel V3. The
relative difference
between (c) Cimel V2 and (d) Cimel V3 as a function of TPW and the internal sensor temperature for (e) V2 and (f) V3. The black dots show the
average difference in bins of 5∘, 5 mm and 5 ∘C and the error bar represents the standard
deviation of the mean.
The TPW values from the Cimel sun photometer (both Cimel V2 and Cimel V3) and the microwave radiometer are highly correlated
(Fig. a and c; R2=0.99). Taking into consideration that the microwave radiometer and the
Cimel sun photometer have the same diurnal variations (Sect. ), a very high correlation of
the two datasets was expected. For higher values of TPW there is a deviation from the identity line.
The histogram of the relative differences between the two datasets has a very small
flattening towards negative values, for both Cimel V2 and Cimel V3
(Fig. b and d). According to the Shapiro–Wilk test for normality
(p value < 2.2e–16 for both Cimel V2 and Cimel V3) it does not
follow a Gaussian distribution. For Cimel V2
about 88 % of the differences lie within ±10 %,
while differences for almost the entire dataset are within ±20 % (>99 %). For Cimel V3 the respective
values are similar. These results show a very good agreement between the two different
methods for the retrieval of TPW.
The difference of the TPW between the Cimel sun photometer and the microwave radiometer does not
show a pronounced dependence
on the SZA (Fig. a and b), for both versions of AERONET algorithms. However, there
is an increased scatter for SZAs higher than 70∘.
This is due to the clock shift effect (see Sect. 3.2) that can affect the direct sun measurements from the Cimel sun photometer at high air masses, resulting in an
increased uncertainty on the retrieved TPW.
The difference of the TPW between the Cimel sun photometer and MWV radiometer has a small
dependence on the total
amount of TPW of -1.97 % per 10 mm for Cimel V2 and -1.38 % per 10 mm for Cimel V3 (Fig. c and d).
The lower dependence of TPW from Cimel V3 on the total amount
of TPW in comparison with Cimel V2 is an indication
that the changes applied in the newer version of the
algorithm are more correct.
Both versions show a higher variability for TPW values
lower than 10 mm due to the
increased uncertainty of both instruments for dry conditions.
However, this variability is based on a relatively
low number of observations and is highly affected by some
outliers (i.e. differences >20 %) observed for extremely
low TPW values (i.e. 1.5–2 mm). When the TPW values lower than 10 mm are
excluded from the analysis, the dependence of the difference between the Cimel sun photometer and MWV radiometer becomes
-1.69 % per 10 mm and -1.19 % per 10 mm, for V2 and V3, respectively.
In addition the very low variability for TPW values higher than 40 mm
cannot be evaluated because they are based on a very limited number of observations (i.e. six observations).
The new laboratory-based temperature coefficients for the sun photometer filters
improve the quality of the retrieved
TPW from the Cimel sun photometer, as can be depicted from the comparison with the MWV (Fig. f).
The dependence of the difference between Cimel V3 and the MWV from the temperature
recorded in the sensor of the Cimel sun photometer
is substantially improved in comparison with the one of Cimel V2 (the order of -0.61 % per 10 ∘C and
-1.07 % per 10 ∘C for Cimel V3 and Cimel V2, respectively; Fig. e and f). Thus the corrections from
the application of the new temperature coefficients are important, since they
significantly improve the quality of the retrieved TPW for all the operating temperatures.
Conclusions
In this study different measurement techniques for TPW (e.g. radiosonde,
microwave radiometer, Cimel sun photometer)
were compared over a period of 9 years. The microwave radiometer and Cimel
sun photometer operated at the RADO situated at a distance of approximately 10 km from the Bucharest city centre.
The radiosonde measurements
were provided by the Romanian National Meteorological Administration, approximately 30 km from the RADO facilities.
The main conclusions of this study can be summarised as follows.
All three instruments depict the same annual cycle of TPW despite
their different sampling rates. Some small
differences observed in the monthly mean values can be attributed to the
different schedule (i.e. the microwave radiometer
operates during both daytime and night-time, while the Cimel sun photometer operates only during daytime
and under clear-sky conditions) and their
different sample, partly due to the existing gaps in MWV and Cimel sun photometer.
The Cimel sun photometer measurements are affected by the clear-sky bias, which is more
pronounced during winter and can lead to values lower by up to 25 % for January compared to the
radiosondes. The clear-sky bias is almost negligible during summer months.
The measurements of the microwave radiometer are highly
correlated with those from radiosondes (i.e. R=0.98),
indicating that the microwave radiometer can capture the environmental changes that lead to variations in TPW.
Compared with the radiosondes, the microwave radiometer slightly
overestimates the TPW, especially during daytime measurements
(i.e. 3.12±9.93 % or 0.35±1.71 mm), due to the dry bias effect,
while the difference between the two datasets during
night-time is almost negligible (i.e. 0.50±9.10 % or 0.001±1.57 mm).
In addition, the differences between
the two datasets during night-time show a very small dependence
(i.e. -0.092±0.052 mm-1) on the total TPW
amount, in conjunction with the daytime differences that have an increased dependency (i.e. -0.169±0.057 mm-1).
Version 3 of the AERONET algorithm slightly underestimates TPW
with an overall difference of 0.60±0.91 % (0.08±0.14 mm), compared to version 2.
The differences of the TPW between versions 2 and 3 AERONET
algorithms for their individual common measurements are small (i.e. ±2 %).
The highest differences are observed for low temperatures of the internal sensor (i.e.<10∘C),
while the use of new laboratory-based temperature coefficients has an effect of up to 5 % for the whole range of
the temperatures recorded by the instrument (∼50∘C).
The V2 and V3 AOD 870 nm common values agree within 0.01 AOD and rare larger deviations are likely associated with different temperature coefficients applied in V2 and V3.
TPW from the Cimel sun photometer is highly correlated with the radiosonde
measurements (i.e. R2= 0.99) for both versions of the AERONET algorithm.
Compared with the radiosondes, the Cimel sun photometer underestimates the
TPW by 1.95±10.97 % (or 0.39±2.10 mm) for V2
and 2.74±10.54 % (or 0.50±2.05 mm) for V3.
This underestimation is in agreement with previous
studies comparing measurements from radiosondes and sun photometers for different regions.
When compared with the microwave radiometer, the Cimel sun photometer
underestimates
by 2.75±5.85 % (or 0.70±1.22 mm) for V2
and 3.57±5.54 % (or 0.81±1.17 mm) for V3. The two instruments
have the same daily cycle, which shows
the capability of the Cimel sun photometer to capture the daily variations in TPW.
However, some discrepancies are observed during
early morning or late afternoon, which are induced
from a shift in the Cimel clock resulting in a minor error
in the calculation of the optical air mass. However, changes in the Cimel TPW are within uncertainty estimates. While the difference
between the Cimel sun photometer and radiometer
does not show any pronounced dependence on SZA, for SZAs >70∘
the differences show an increased scatter.
V3 has a lower dependence from the total TPW amount
(i.e. -0.138±0.012 mm-1) compared with V2
(i.e. -0.197±0.013 mm-1). The new laboratory-based
temperature coefficients implemented in V3
reduced the dependence of the recorded differences between the Cimel sun photometer and the microwave radiometer
(i.e. -0.107±0.012∘C-1 and -0.061±0.011∘C-1
for V2 and V3, respectively).
The implementation of the new temperature coefficients in V3 has significantly
improved the quality of the retrieved TPW and AOD from Cimel sun photometer measurements, especially
before 2009, when the filter at 870 nm had higher sensitivity to temperature variations.
To our knowledge this is the first study to evaluate, in depth, the TPW
retrieval from the newly released version 3 of the AERONET algorithm. The
comparison with high-quality independent measurements from radiosondes and a
collocated radiometer shows that the absolute level of the differences in V3
from the other instruments is a little higher than in V2. However, the one-sigma uncertainty for V3 compared to the radiosondes is ∼10 %, which
is in accordance with previous studies for V2. This slightly increased
uncertainty could be attributed to the relatively high distance between
the Cimel sun photometer and the radiosonde launching site. Compared with the collocated MWV radiometer
the estimated uncertainty is further reduced to less than 6 %. V3 has a
lower dependence on the TPW and the internal sensor temperature, which in
principle should improve the TPW Cimel retrievals. Nevertheless, further
evaluation is needed, especially for sites with different characteristics
(i.e. mountain or marine environments). Although these findings are for a
specific site, they are likely representative for other continental sites as
well. A future study will investigate the accuracy of the night-time TPW from
Cimel lunar measurements, available at the RADO facilities since 2016,
following the methodology applied in this study. Finally, the microwave
radiometer shows a very good performance compared with the radiosondes,
especially during night-time when the differences between the two instruments
are almost negligible. Thus, the microwave radiometer can be used in future
studies related to the validation of satellite datasets during both daytime and
night-time.
Data availability
The data from the radiosondes for Bucharest (station ID:
15420) are publicly available through the upper air observations database of
the University of Wyoming at the link
http://weather.uwyo.edu/upperair/sounding.html (last access: 13 July 2018).
The Cimel sun photometer data can be found at the AERONET website
(https://aeronet.gsfc.nasa.gov/, last access: 14 January 2019) under the
label Bucharest_Inoe. The data from the microwave radiometer and the
relative humidity from the meteorological station are available upon request.
The sunshine duration and cloud faction are available through the EUMETSAT's
CM SAF web portal (https://wui.cmsaf.eu/safira/action/viewProduktSearch, last access: 29 November 2018).
Author contributions
KF and GAE initiated
the idea for this paper. KF performed the analysis for the biggest part of
the paper with the assistance of BA. DE was responsible for the calibration
of the microwave radiometer and produced the L2 data from the instrument. DMG
provided important information about the V3 AERONET and reviewed parts of the
comparison of the two algorithms and of Cimel sun photometer with the other instruments. MB
performed the analysis for the sunshine duration and cloud coverage. LV was
responsible in the past for the calibration of the microwave radiometer and
local site manager of the Cimel sun photometer. DN is the PI of the
Bucharest_INOE AERONET station. KF and BA prepared the paper with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank Ioannis Panagiotis Raptis and the second, anonymous, reviewer for their constructive comments that significantly
improved the paper. We thank AERONET (PHOTONS) for instrument calibration and maintenance of the Cimel
instrument and AERONET (GSFC) for processing and disseminating these data.
The authors would further like to acknowledge the EUMETSAT CM-SAF project
team for providing the climate variables used in this study. This work has
received funding from the European Union's Horizon 2020 Research and
Innovation Programme, under grant agreement no. 692014, project ECARS (East
European Centre for Atmospheric Remote Sensing). Part of the work performed
for this study was funded by the Ministry of Research and Innovation through
Program I – Development of the national research–development system,
Subprogram 1.2 – Institutional Performance – Projects of Excellence Financing
in RDI, contract no. 19PFE/17.10.2018 and by Romanian National Core Program
contract no. 18N/2019.
Review statement
This paper was edited by Laura Bianco and reviewed by
Ioannis Panagiotis Raptis and one anonymous referee.
ReferencesAmerican Meteorological Society: Precipitable Water, Glossary of Meteorology, available at:
http://glossary.ametsoc.org/wiki/Precipitable_water (last access: 10 July 2018), 2018.Barreto, A., Cuevas, E., Damiri, B., Romero, P. M., and Almansa, F.: Column
water vapor determination in night period with a lunar photometer prototype,
Atmos. Meas. Tech., 6, 2159–2167, 10.5194/amt-6-2159-2013,
2013.Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann,
T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property
data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434,
10.5194/essd-9-415-2017, 2017.Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware,
R. H.: GPS meteorology: Remote sensing of atmospheric water vapor using the
global positioning system, J. Geophys. Res.-Atmos., 97,
15787–15801, 10.1029/92JD01517, 1992.Campanelli, M., Mascitelli, A., Sanò, P., Diémoz, H., Estellés,
V., Federico, S., Iannarelli, A. M., Fratarcangeli, F., Mazzoni, A., Realini,
E., Crespi, M., Bock, O., Martínez-Lozano, J. A., and Dietrich, S.:
Precipitable water vapour content from ESR/SKYNET sun-sky radiometers:
validation against GNSS/GPS and AERONET over three different sites in Europe,
Atmos. Meas. Tech., 11, 81–94, 10.5194/amt-11-81-2018, 2018.Campmany, E., Bech, J., Rodríguez-Marcos, J., Sola, Y., and Lorente, J.:
A comparison of total precipitable water measurements from radiosonde and
sunphotometers, Atmos. Res., 97, 385–392,
10.1016/j.atmosres.2010.04.016, 2010.Carstea, E., Fragkos, K., Siomos, N., Antonescu, B., and Belegante, L.:
Columnar aerosol measurements in a continental southeastern Europe site:
climatology and trends, Theor. Appl. Climatol.,
10.1007/s00704-019-02805-z, 2019.Cheval, S., Dumitrescu, A., and Bell, A.: Spatial sampling requirements for
monitoring upper-air climate change with radiosondes, Theor. Appl. Climatol.,
97, 391–401, 10.1007/s00704-008-0088-3, 2009.Coddington, O., Lean, J. L., Pilewskie, P., Snow, M., and Lindholm, D.: A Solar
Irradiance Climate Data Record, B. Am. Meteorol. Soc., 97, 1265–1282, 10.1175/BAMS-D-14-00265.1, 2016.Ferrare, R. A., Melfi, S. H., Whiteman, D. N., Evans, K. D., Schmidlin, F. J.,
and Starr, D. O.: A Comparison of Water Vapor Measurements Made by Raman
Lidar and Radiosondes, J. Atmos. Ocean. Tech., 12,
1177–1195, 10.1175/1520-0426(1995)012<1177:ACOWVM>2.0.CO;2, 1995.Filioglou, M., Nikandrova, A., Niemelä, S., Baars, H., Mielonen, T.,
Leskinen, A., Brus, D., Romakkaniemi, S., Giannakaki, E., and Komppula, M.:
Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a
satellite and a model, Atmos. Meas. Tech., 10, 4303–4316,
10.5194/amt-10-4303-2017, 2017.Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas,
N., Stengel, M., Fuchs, P., Hollmann, R., and Werscheck, M.: CLAAS-2: CM SAF
CLoud property dAtAset using SEVIRI – Edition 2, Satellite Application
Facility on Climate Monitoring, 10.5676/EUM_SAF_CM/CLAAS/V002, 2016.Gaffen, D. J. and Elliott, W. P.: Column Water Vapor Content in Clear and
Cloudy Skies, J. Climate, 6, 2278–2287,
10.1175/1520-0442(1993)006<2278:CWVCIC>2.0.CO;2, 1993.Gamache, R. R., Roller, C., Lopes, E., Gordon, I. E., Rothman, L. S.,
Polyansky, O. L., Zobov, N. F., Kyuberis, A. A., Tennyson, J., Yurchenko,
S. N., Császár, A. G., Furtenbacher, T., Huang, X., Schwenke, D. W., Lee,
T. J., Drouin, B. J., Tashkun, S. A., Perevalov, V. I., and Kochanov, R. V.:
Total internal partition sums for 166 isotopologues of 51 molecules important
in planetary atmospheres: Application to HITRAN2016 and beyond, J.
Quant. Spectrosc. Ra., 203, 70–87,
10.1016/j.jqsrt.2017.03.045,
2017.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.Gui, K., Che, H., Chen, Q., Zeng, Z., Liu, H., Wang, Y., Zheng, Y., Sun, T.,
Liao, T., Wang, H., and Zhang, X.: Evaluation of radiosonde, MODIS-NIR-Clear,
and AERONET precipitable water vapor using IGS ground-based GPS measurements
over China, Atmos. Res., 197, 461–473,
10.1016/j.atmosres.2017.07.021, 2017.Halthore, R. N., Eck, T. F., Holben, B. N., and Markham, B. L.: Sun
photometric measurements of atmospheric water vapor column abundance in the
940-nm band, J. Geophys. Res.-Atmos., 102, 4343–4352,
10.1029/96JD03247, 1997.Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., 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.IPCC: Summary for Policymakers, in: Climate Change 2013 – The Physical
Science Basis, edited by: Intergovernmental Panel on Climate Change,
1–30, Cambridge University Press, Cambridge,
10.1017/CBO9781107415324.004, 2013.Kothe, S., Pfeifroth, U., Cremer, R., Trentmann, J., and Hollmann, R.: A
Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa,
Remote Sens.-Basel, 9, 429, 10.3390/rs9050429, 2017.Liang, H., Cao, Y., Wan, X., Xu, Z., Wang, H., and Hu, H.: Meteorological
applications of precipitable water vapor measurements retrieved by the
national GNSS network of China, Geodesy Geodynam., 6, 135–142,
10.1016/J.GEOG.2015.03.001, 2015.McCarthy, M. P.: Spatial sampling requirements for monitoring upper-air
climate change with radiosondes, Int. J. Climatol., 28, 985–993,
10.1002/joc.1611, 2008.Mears, C. A., Wang, J., Smith, D., and Wentz, F. J.: Intercomparison of total
precipitable water measurements made by satellite-borne microwave radiometers
and ground-based GPS instruments, J. Geophys. Res.-Atmos., 120, 2492–2504, 10.1002/2014JD022694, 2015.Miloshevich, L. M., Vömel, H., Whiteman, D., and Leblanc, T.: Accuracy
assessment and correction of Vaisala RS92 radiosonde water vapor
measurements, J. Geophys. Res.-Atmos., 114, D11,
10.1029/2008JD011565, 2009.Mlawer, E. J., Payne, V. H., Moncet, J.-L., Delamere, J. S., Alvarado, M. J.,
and Tobin, D. C.: Development and recent evaluation of the MT_CKD model of
continuum absorption, Philos. T. Roy. Soc. A, 370, 2520–2556,
10.1098/rsta.2011.0295, 2012.Paynter, D. and Ramaswamy, V.: Variations in water vapor continuum radiative
transfer with atmospheric conditions, J. Geophys. Res., 117, D16310,
10.1029/2012JD017504, 2012.Pérez-Ramírez, D., Whiteman, D. N., Smirnov, A., Lyamani, H.,
Holben, B. N., Pinker, R., Andrade, M., and Alados-Arboledas, L.: Evaluation
of AERONET precipitable water vapor versus microwave radiometry, GPS, and
radiosondes at ARM sites, J. Geophys. Res.-Atmos., 119,
9596–9613, 10.1002/2014JD021730, 2014.Pfeifroth, U., Kothe, S., Müller, R., Trentmann, J., Hollmann, R., Fuchs,
P.,
and Werscheck, M.: Surface Radiation Data Set – Heliosat (SARAH) – Edition 2,
Satellite Application Facility on Climate Monitoring,
10.5676/EUM_SAF_CM/SARAH/V002, 2017.Raptis, P.-I., Kazadzis, S., Gröbner, J., Kouremeti, N., Doppler, L.,
Becker, R., and Helmis, C.: Water vapour retrieval using the Precision Solar
Spectroradiometer, Atmos. Meas. Tech., 11, 1143–1157,
10.5194/amt-11-1143-2018, 2018.Reber, E. E. and Swope, J. R.: On the Correlation of the Total Precipitable
Water in a Vertical Column and Absolute Humidity at the Surface, J.
Appl. Meteorol., 11, 1322–1325,
10.1175/1520-0450(1972)011<1322:OTCOTT>2.0.CO;2, 1972.Román, R., Antón, M., Cachorro, V., Loyola, D., Ortiz de
Galisteo, J., de Frutos, A., and Romero-Campos, P.: Comparison of total
water vapor column from GOME-2 on MetOp-A against ground-based GPS
measurements at the Iberian Peninsula, Sci. Total Environ.,
533, 317–328, 10.1016/J.SCITOTENV.2015.06.124, 2015.Rose, T., Crewell, S., Löhnert, U., and Simmer, C.: A network suitable
microwave radiometer for operational monitoring of the cloudy atmosphere,
Atmos. Res., 75, 183–200,
10.1016/j.atmosres.2004.12.005, 2005.Sapucci, L. F., Machado, L. A. T., Monico, J. F. G., and Plana-Fattori, A.:
Intercomparison of Integrated Water Vapor Estimates from Multisensors in the
Amazonian Region, J. Atmos. Ocean. Tech., 24,
1880–1894, 10.1175/JTECH2090.1, 2007.Schneider, M., Romero, P. M., Hase, F., Blumenstock, T., Cuevas, E., and
Ramos, R.: Continuous quality assessment of atmospheric water vapour
measurement techniques: FTIR, Cimel, MFRSR, GPS, and Vaisala RS92, Atmos.
Meas. Tech., 3, 323–338, 10.5194/amt-3-323-2010, 2010.Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality
(complete samples), Biometrika, 52, 591–611,
10.1093/biomet/52.3-4.591, 1965.Sussmann, R., Borsdorff, T., Rettinger, M., Camy-Peyret, C., Demoulin, P.,
Duchatelet, P., Mahieu, E., and Servais, C.: Technical Note: Harmonized
retrieval of column-integrated atmospheric water vapor from the FTIR network
– first examples for long-term records and station trends, Atmos. Chem.
Phys., 9, 8987–8999, 10.5194/acp-9-8987-2009, 2009.Turner, D. D., Lesht, B. M., Clough, S. A., Liljegren, J. C., Revercomb, H. E.,
and Tobin, D. C.: Dry Bias and Variability in Vaisala RS80-H Radiosondes: The
ARM Experience, J. Atmos. Ocean. Tech., 20, 117–132,
10.1175/1520-0426(2003)020<0117:DBAVIV>2.0.CO;2, 2003.
Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady-Pereira,
K. E., and Gaustad, K. L.: Retrieving liquid water path and precipitable
water vapor from the Atmospheric Radiation Measurement (ARM) microwave
radiometers, IEEE T. Geosci. Remote, 45,
3680–3690, 2007.Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De
Mazière, M., Wagner, T., De Backer, H., and Bruyninx, C.: A multi-site
intercomparison of integrated water vapour observations for climate change
analysis, Atmos. Meas. Tech., 7, 2487–2512,
10.5194/amt-7-2487-2014, 2014.Vaquero-Martínez, J., Antón, M., Ortiz de Galisteo, J. P.,
Cachorro, V. E., Costa, M. J., Román, R., and Bennouna, Y. S.:
Validation of MODIS integrated water vapor product against reference GPS
data at the Iberian Peninsula, Int. J. Appl. Earth Obs., 63, 214–221,
10.1016/J.JAG.2017.07.008, 2017a.Vaquero-Martínez, J., Antón, M., de Galisteo, J. P. O., Cachorro,
V. E.,
Wang, H., Abad, G. G., Román, R., and Costa, M. J.: Validation of
integrated water vapor from OMI satellite instrument against reference GPS
data at the Iberian Peninsula, Sci. Total Environ., 580,
857–864, 10.1016/J.SCITOTENV.2016.12.032, 2017b.Vaquero-Martínez, J., Antón, M., Ortiz de Galisteo, J. P.,
Cachorro, V. E., Álvarez-Zapatero, P., Román, R., Loyola, D.,
Costa, M. J., Wang, H., Abad, G. G., and Noël, S.: Inter-comparison of
integrated water vapor from satellite instruments using reference GPS data at
the Iberian Peninsula, Remote Sens. Environ., 204, 729–740,
10.1016/J.RSE.2017.09.028, 2018.Vömel, H., Selkirk, H., Miloshevich, L., Valverde-Canossa, J.,
Valdés, J.,
Kyrö, E., Kivi, R., Stolz, W., Peng, G., and Diaz, J. A.: Radiation Dry Bias
of the Vaisala RS92 Humidity Sensor, J. Atmos. Ocean.
Tech., 24, 953–963, 10.1175/JTECH2019.1, 2007.Wagner, T., Andreae, M. O., Beirle, S., Dörner, S., Mies, K., and
Shaiganfar, R.: MAX-DOAS observations of the total atmospheric water vapour
column and comparison with independent observations, Atmos. Meas. Tech., 6,
131–149, 10.5194/amt-6-131-2013, 2013.
Westwater, E. R. and Guiraud, F. O.: Ground-based microwave radiometric
retrieval of precipitable water vapor in the presence of clouds with high
liquid content, Radio Sci., 15, 947–957, 10.1029/RS015i005p00947,
1980.