AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-3339-2018A full-mission data set of H2O and HDO columns from SCIAMACHY 2.3 µm reflectance measurementsH2O and HDO from SCIAMACHYSchneiderAndreasa.schneider@sron.nlBorsdorffTobiashttps://orcid.org/0000-0002-4421-0187aan de BrughJoostHuHailiLandgrafJochenEarth science group, SRON Netherlands Institute for Space Research, Utrecht, the NetherlandsAndreas Schneider (a.schneider@sron.nl)12June20181163339335030December20172March20184May201824May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/3339/2018/amt-11-3339-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/3339/2018/amt-11-3339-2018.pdf
A new data set of vertical column densities of the water vapour isotopologues
H2O and HDO from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument for the whole
of the mission period from January 2003 to April 2012 is presented. The data are
retrieved from reflectance measurements in the spectral range 2339
to 2383 nm with the Shortwave Infrared CO Retrieval (SICOR)
algorithm, ignoring atmospheric light scattering in the measurement
simulation. The retrievals are validated with ground-based Fourier transform
infrared measurements obtained within the Multi-platform remote Sensing of
Isotopologues for investigating the Cycle of Atmospheric water (MUSICA)
project. A good agreement for low-altitude stations is found with an average
bias of -3.6×1021 for H2O and
-1.0×1018moleccm-2 for HDO. The a posteriori
computed δD shows an average bias of -8 ‰, even
though polar stations have a larger negative bias. The latter is due to the large amount of sensor noise in SCIAMACHY in combination with low albedo and high solar
zenith angles. To demonstrate the benefit of accounting for light scattering
in the retrieval, the quality of the data product fitting effective cloud
parameters simultaneously with trace gas columns is evaluated in a dedicated
case study for measurements round high-altitude stations. Due to a large
altitude difference between the satellite ground pixel and the mountain
station, clear-sky scenes yield a large bias, resulting in a δD bias
of 125 ‰. When selecting scenes with optically thick clouds
within 1000 m above or below the station altitude, the bias in a posteriori
δD is reduced from 125 to 44 ‰.
The insights from the present study will also benefit the analysis of the
data from the new Sentinel-5 Precursor mission.
Introduction
Atmospheric water vapour is a trace gas which is important for
the energy budget of the atmosphere. For instance, it is the strongest
natural greenhouse gas and transports energy through latent heat
e.g.. However, the uncertainties
regarding the effects of water vapour in the energy balance of the atmosphere
and regarding the interaction between water vapour and the atmospheric
circulation are still large e.g.. Measurements are
expected to contribute to a better understanding and quantification of these
processes. Observations of isotopologues of water are especially interesting,
because the ratio provides information about the history of the sampled
air parcel due to a temperature-dependent isotopic fractionation during
evaporation and condensation caused by different vapour pressure and
diffusion constants of the isotopologues e.g..
For instance, when water is evaporated from a reservoir that is not too small, heavy
isotopologues are depleted. Water vapour is also depleted by condensation
during the formation of clouds and precipitation. In all these cases the
amount of depletion depends on temperature.
The ratio of the isotopologues HDO and H2O for column
densities cHDO and cH2O is defined by RD=cHDO/cH2O. The depletion is usually described by the
relative difference between an observed ratio RD and a standard ratio
RD,std:
δD=RD-RD,stdRD,std⋅1000‰.
This is a column δD, which some authors refer to as
δD‾ to distinguish it from δD profiles. The
commonly used standard ratio is Vienna Standard Mean Ocean Water (VSMOW),
RD,std=3.1152×10-4.
Measurements of atmospheric water vapour isotopologues are rare. Observations
are made in situ with aircraft and balloons
e.g., in
situ on the ground e.g. and by using (ground- or
space-based) remote sensing techniques. Ground-based remote sensing of the
water vapour isotopologues is usually carried out using Fourier transform infrared
(FTIR) instruments, which observe the direct sunlight in the infrared,
determining the vertically integrated columns above the site. Many ground
stations are organised in networks, such as the Total Carbon Column Observing
Network (TCCON, https://tccondata.org/, last access: 6 June 2018) and the Network for the Detection of
Atmospheric Composition Change (NDACC, www.ndacc.org, last access: 6 June 2018). Within the latter, the
Multi-platform remote Sensing of Isotopologues for investigating the Cycle of
Atmospheric water (MUSICA) project provides a
validation against in situ measurements. Satellite-based measurements have
the advantage of global coverage. H2O and HDO were first
retrieved from satellite data by using thermal infrared
measurements from the Interferometric Monitor for Greenhouse gases (IMG)
sensor aboard the Advanced Earth Observing Satellite (ADEOS). Later, these
isotopologues were also inferred from the Tropospheric Emission Spectrometer
(TES) on the Earth Observing System (EOS) Aura satellite ,
the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard
European Space Agency (ESA)'s environmental satellite (ENVISAT)
, the Infrared Atmospheric Sounding Interferometer
(IASI) aboard the MetOP satellite and the Greenhouse
Gases Observing Satellite (GOSAT) .
used the short-wave infrared (SWIR) band of the
SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY
(SCIAMACHY) instrument on ENVISAT to obtain 3
years of simultaneous measurements of H2O and HDO. This data
set was extended to the period 2003–2007 by . In the
present study a new H2O and HDO data set for the whole of the mission
period of SCIAMACHY is presented.
Recently, the new Tropospheric Monitoring Instrument (TROPOMI) aboard the
Sentinel-5 Precursor satellite was launched, which
includes the SWIR in the same spectral range and with the same spectral
resolution as SCIAMACHY, but at much better signal-to-noise ratio (SNR) and
spatial resolution. It is expected that retrievals from this new instrument
will provide H2O and HDO data of unprecedented quality. The
present study is also preparation for the new mission, apart from providing
a data set for the whole of the mission period of SCIAMACHY.
The new data set is retrieved using the Shortwave Infrared CO Retrieval
(SICOR) algorithm as discussed by
. SICOR
is designed for the operational processing of carbon monoxide total column
retrieval from TROPOMI measurements. The algorithm has two
options. First, assuming a strict cloud filtering, the retrieval ignores
atmospheric scattering in the SWIR spectral range, and the atmospheric total
column of CH4, H2O, HDO and CO is inferred
from the measurement. This approach, called non-scattering retrieval in this
paper, is proposed by to generate the TROPOMI
H2O and HDO data product. In this study, the retrieval is
applied to SCIAMACHY observations, and the H2O and HDO data
product is validated against ground-based measurements from MUSICA.
Alternatively, one can choose a loose cloud clearing. Here, the optical
depth and height of a scattering layer is retrieved from the CH4
absorptions of the measurement using appropriate a priori information from
chemical transport models (CTMs). This approach, hereinafter referred to as
scattering retrieval, is the processing baseline for CO retrievals from
SCIAMACHY and TROPOMI as discussed by .
In the present paper, this approach is evaluated for the retrieval of water
vapour isotopologues by comparing ground-based observations at high-altitudes
with collocated SCIAMACHY retrievals using observations with clouds at a
height similar to that of the ground site.
The remainder of this article is structured as follows: in
Sect. the retrieval set-ups of both the non-scattering
and scattering retrieval are given. Section presents the
validation of the non-scattering data product, whereas Sect.
discusses the potential added value of the scattering retrieval of the water
isotopologues. Finally, a summary is given and conclusions are drawn in
Sect. .
Retrieval method
The SICOR algorithm and its application to CO and
HDO/H2O retrievals is discussed in detail by
. In this
section its main features that are relevant in the context of this study are
summarised.
Simulation of atmospheric absorption in the spectral range of SCIAMACHY's channel 8 for the absorbers taken
into account by the retrieval algorithm.
The grey shading marks the retrieval window for CO used by , the yellow
shading marks the window for H2O and HDO used by and the green shading marks the extension
of that window used in this work.
SICOR provides two options for the trace gas column retrieval from
SWIR radiance measurements. First, assuming clear-sky observations,
atmospheric scattering is ignored in the forward simulation of the
measurement. Subsequently the algorithm infers the total column of
H2O, HDO, CH4 and CO together with the
Lambertian surface albedo from SCIAMACHY's channel 8 measurements between
2338.5 and 2382.5 nm. This spectral fit window is an
extension of the one proposed by and includes more
absorption lines of HDO, which is beneficial for retrievals using
measurements at the end of SCIAMACHY's lifespan (which was not considered by
), when more and more pixels ceased to function. Spectral
absorption line data are taken from . The atmospheric absorption within this spectral range is
presented in Fig. .
The trace gas column retrieval utilises the profile-scaling approach as
described in detail by . The a priori profiles of water
vapour are adapted from the European Centre for Medium-Range Weather
Forecasts (ECMWF) reanalysis product. Since the ECMWF data product does not
provide the individual isotopologue profiles, H2O and HDO
profiles are obtained from the water vapour profile by scaling it with the
standard abundances of H2O and HDO. A priori
profiles for methane and CO are taken from TM5 simulations. Obviously, the
assumption of clear-sky observations requires cloud clearing of the data,
which is performed a posteriori to the retrieval. As proposed by
, only retrieved data which satisfy the following
conditions are considered:
0.9<cCH4cCH4,TM5<1.10.7<cH2OcH2O,ECMWF,
with the retrieved columns cCH4 and cH2O and the
corresponding model predictions cCH4,TM5 and cH2O,ECMWF. Moreover, due to radiometric performance issues with
SCIAMACHY in the SWIR spectral range, the data have to be filtered with
respect to outliers. Since water vapour in the troposphere is log-normally
distributed and (as seen in our data) so is its
uncertainty, the 15.9th and 84.1th percentiles P15.9 and P84.1 of the
logarithmic H2O and HDO uncertainty data distributions loge are considered to define
σ=12(P84.1-P15.9).
Only data within 5σ around the median μ of the logarithmic
distribution are used. A similar filter is applied to the root mean square of
the spectral fit residual, crms, where data points within
6σ around the median μ are kept. Thus,
μe(H2O)-5σe(H2O)<logeH2O<μe(H2O)+5σe(H2O)μe(HDO)-5σe(HDO)<logeHDO<μe(HDO)+5σe(HDO)μrms-6σrms<logcrms<μrms+6σrms.
Furthermore, the data are screened with respect to the number of iterations,
Niter≤12, and the solar zenith angle (SZA),
ϑsz<70∘.
Alternatively, SICOR allows atmospheric scattering in the
retrieval to be accounted for and so enhances the data yield of the retrieval. Based on the
numerically very efficient two-stream radiative transfer model
, the retrieval uses prior model information on
CH4 to infer the height hcld and optical depth
τcld of a scattering layer together with the total column of
CO, H2O and HDO and the Lambertian surface albedo. Using this
approach, our study considers, for the first time, the retrieval of the water
vapour isotopologues in the SWIR for cloudy atmospheres and demonstrates the
added value of the approach for the data product. The same quality filter as
described above is used. The cloud filtering is detailed in
Sect. .
Zero-order radiometric offset retrieved from cloud-free SCIAMACHY spectra above the Sahara (black) and
Australia (grey). The additive offset is given as a percentage of the total radiance.
The performance of SCIAMACHY suffered from an ice layer build-up on the
SWIR detector e.g.. Among other things, this caused
additional stray light in the instrument, meaning in a first approximation an
additive radiometric bias to the measurement. To compensate for this effect, a
radiometric correction as described by is applied.
Hence, a radiometric offset with spectral dependence described by a third-order polynomial is fitted for clear-sky scenes above the Sahara region with
a high albedo and so a high SNR while fixing methane to the
prior TM5 model information. Using the fitting window 2338.5–2382.5 nm, the
time series of the spectrally constant radiometric offset is plotted in
Fig. . It shows an increasing offset with a growing ice layer
which resets to zero at so-called decontamination events during which the
detector was heated to remove the ice. Since 2005 no regular decontamination
procedures have been carried out except for one additional decontamination at the
beginning of 2009. Due to that the offset eventually stabilised. To
demonstrate that the calibration is globally applicable, the figure also
shows the offset determined above Australia. The results are very similar and
the deviation between the radiometric biases is less than 10 % when
disregarding three outliers in the vicinity of decontamination events and a
short period at the beginning of 2011 with a deviation of ∼14 %.
The strategy for the data product presented in this paper is to provide
H2O and HDO columns for the whole period of the SCIAMACHY
mission. The ratio δD may be computed a posteriori from these data but
is not a primary product.
Validation of the non-scattering retrieval of H2O and HDO
To validate the SCIAMACHY H2O and HDO total columns, the observations
are compared to ground-based FTIR measurements from nine NDACC-MUSICA stations
, which are listed in
Table . Here, high-altitude stations like
Jungfraujoch (3580 m a.s.l.) and Izaña (2367 m a.s.l.) are not considered because the satellite and ground-based
measurements probe different altitude ranges for these cases.
List of MUSICA ground stations used for the validation.
For each site, all SCIAMACHY observations are selected that pass the filter described in Sect. 2 and are within a radius of 800 km
around the station. Finally for comparison, both for MUSICA and SCIAMACHY data,
monthly medians are taken, so that the data with different sampling can be
compared. Months in which the number of individual measurements contributing
to the median is less than 5 % of that in the month with most data are
excluded to avoid biases by bad statistics. The loose spatio-temporal
coregistration criteria are required to include a sufficient number of
measurements to overcome the large amount of measurement noise in SCIAMACHY. Even
though water vapour may change substantially over small distances, the
results suggest that representation errors due to the large spatial
collocation area average out in the monthly medians and their statistics.
Time series of monthly medians of H2O(b), HDO(c) and δD (d)
for the MUSICA ground station Bremen (blue) and collocated SCIAMACHY observations for clear-sky
conditions (red). Panel (a) shows the number of SCIAMACHY measurements in each month.
Correlation plots for monthly medians of MUSICA and SCIAMACHY observations of H2O(a),
HDO(b) and δD (c) at the station Bremen between January 2003 and April 2012.
Figure depicts a time series of monthly medians
for the station Bremen. For all three quantities H2O, HDO and δD, the
measurements of both instruments agree well. The biases, which are defined as
the average of the difference between SCIAMACHY and MUSICA results, are
-5.6×1020moleccm-2,
-1.4×1017moleccm-2 and +11 ‰. The conversion factor to precipitable water in mm is
2.99×10-22 for H2O and
3.16×10-22mmmolec-1cm2 for HDO. The time
series also shows that the data set is homogeneous throughout the whole
mission period. Figure shows a clear
correlation between MUSICA and SCIAMACHY measurements for all three
quantities with Pearson correlation coefficients of 0.81, 0.81 and 0.63 for
H2O, HDO and δD.
Statistics for the validation of the non-scattering retrievals: (a) average number of measurements per
month and their standard deviation. (b) Pearson correlation coefficient between MUSICA and SCIAMACHY monthly
averages of H2O (blue), HDO (red) and δD (green). (c) Bias of H2O and its
standard error. (d) Bias of HDO and its standard error. (e) Bias of δD and its standard error.
The statistics for all low-altitude MUSICA stations are presented in
Fig. . For H2O and HDO, the
correlation is good (between 0.74 and 0.97), with small biases for all
stations (on average -3.6×1021 and
-1.0×1018moleccm-2). Also, for δD,
a good correlation between SCIAMACHY and MUSICA data is achieved for most
stations, but it is only mediocre for Kiruna, Wollongong and Lauder. The
reason is that the seasonality in δD is weak for those stations. To
demonstrate that the low correlation coefficient does not indicate bad
results, the time series for Lauder (for which the correlation is lowest) is
shown in Fig. .
The polar stations Eureka and Ny Ålesund have a relatively high bias in
δD. Although the number of observations is large due to orbits
converging at polar latitudes, the results are dominated by difficult
measurement conditions with low surface albedos and high solar zenith angles
in combination with SCIAMACHY's high amount of sensor noise, making them imprecise. The average bias in δD over all stations is
-8 ‰.
Same as Fig. , but for the station Lauder.
The bias is relatively stable over the mission time. When computing the bias
for both halves of the mission from 2003 to 2007 and from 2008 to 2012, the
difference is relatively small for most stations. On average the relative
difference in bias in H2O between the halves is 20 %, with a
larger difference for Bremen, where it changes from -2.9×1021 to
-8.2×1021moleccm-2. On the other hand, the bias in
δD does not change in Bremen. The mean change in the bias of δD
is 11 %. Only for Ny Ålesund does the bias in δD become
significantly worse from -24 ‰ for 2003–2007 to
-52 ‰ for 2008–2012. The latter is attributed to the
degradation of the instrument, which especially plays a role for difficult
measurement conditions as described above. Low sun and low albedo result in
low SNR and thus higher error sensitivity. Outside polar
latitudes easier measurement conditions can make up for the degradation.
H2O column (a, b), HDO column (c, d) and δD (e, f) on a
1∘× 1∘grid averaged over the first half from 2003 to 2007 (left) and the second half from 2008
to 2012 (right) of the SCIAMACHY mission. The white points mark the locations of the MUSICA stations used for validation.
To demonstrate the coverage of the non-scattering retrieval data set, world
plots of H2O, HDO and δD averaged over the first and
second half of the mission period are presented in the left and right panels
of Fig. .
In H2O and HDO the latitudinal gradient with more water vapour
abundance in the tropics and drier air in polar regions is clearly visible.
Above mountain ranges such as the Andes and the Himalayas, the water vapour
abundance is reduced. This effect is discussed in more detail in the next
section in connection with high-altitude stations. A continental effect with
drier air inland can also be seen, e.g. in North America, North Africa and
Asia. In δD, the features described by and
are mostly reproduced. On a large scale, δD
decreases from the equator to the poles, which has already been mentioned by
. High altitudes also show strong depletion in
δD. The continental effect is visible, for example, in North and South
America, Asia and even Australia. Enhanced δD above the Red Sea seen
by is also reproduced. More importantly, the
comparison between the left and right panels shows that the data set is
consistent throughout the mission time.
The scattering retrieval of H2O and HDO
To demonstrate the benefit of accounting for light scattering by clouds in the
retrieval of H2O and HDO, the corresponding water vapour
isotopologue product of SCIAMACHY is compared with MUSICA measurements at
elevated sites above 2000 m a.s.l. Here, significant differences between
the mean surface height of the SCIAMACHY ground pixels and the surface
elevation of the ground site hampers the verification of the measurements
using clear-sky observations. However, when retrieving cloud height and
scattering optical depth jointly with the water columns, satellite and ground-based
measurements of similar altitude sensitivity can be selected. This is
demonstrated for the MUSICA site at Jungfraujoch (3580 m a.s.l.) by
selecting collocated SCIAMACHY observations for clear-sky and cloudy-sky
conditions using the selection criteria in Table .
Selection criteria for clear-sky and cloudy-sky conditions using scattering retrieval. Here hs is the
altitude of the MUSICA ground site and max(SNR) is the maximum of the signal-to-noise ratio of a spectrum.
Time series of monthly medians similar to those shown in Fig. but for scattering retrievals
near the high-altitude station Jungfraujoch (3580 m a.s.l.). The left panels (a), (d), (g) and (j) show clear-sky
measurements without altitude correction; the centre panels (b), (e), (h) and (k) show the same measurements with altitude
correction; and the right panels (c), (f), (i) and (l) show observations with optically thick clouds within an altitude range 1000 m
above and below the station height.
Please note that in the left panels the H2O and HDO axes are different than in the centre and right panels,
as indicated by the axis ticks.
A time series of clear-sky observations in the left panel of
Fig. shows large biases in the SCIAMACHY
measurements relative to MUSICA of 4.3×1022moleccm-2
in H2O, 1.1×1019moleccm-2 in HDO and
140 ‰ in δD. Atop the mountain at Jungfraujoch,
only the partial column above 3580 m is observed, while SCIAMACHY in a
collocation radius of 800 km around the site observes the whole column above
the ground pixel, which has a much lower elevation than Jungfraujoch for most
scenes. With the large vertical gradient of water vapour abundance in the
lower troposphere, this leads to the observed bias. Moreover, the
column-averaged δD has lower values over mountains due to the HDO
depletion with height.
To correct the SCIAMACHY observations for this altitude difference, the
collocated ECMWF water vapour profile is scaled to the total column observed
by SCIAMACHY, and subsequently the scaled profile is truncated at the height
of Jungfraujoch. The vertical integration of this truncated profile provides
the altitude-corrected H2O and HDO columns depicted in the
centre panels of Fig. . Obviously, the
correction eliminates the large biases for H2O and HDO for the
most part: the biases are reduced by more than factors of 400 and 36 to
1.0×1020moleccm-2 and
3.0×1017moleccm-2. However, for
δD computed from the corrected columns, the bias remains due to the
assumption in the same relative vertical distribution of both
isotopologues.
Example of averaging kernels for H2O for a clear-sky measurement (blue) and
a scene with optically thick clouds (red).
Next, cloudy scenes with cloud height around the station altitude are
evaluated, since for these the result for the whole column is dominated by
the part above the cloud due to the measurement sensitivity. The sensitivity
of the retrieved column to changes in the vertical distribution of the water
vapour isotopologues is described by the column averaging kernel
e.g.. An example of averaging kernels of clear-sky and cloudy
measurements is shown in Fig. . A detailed
discussion of the column averaging kernel is given by .
In the case of an optically thick cloud (in the example at 3.5 km
altitude), the retrieval is sensitive above the cloud but insensitive below
the cloud, which underlines the selection criteria for cloudy-sky
observations in Table . Furthermore, to compare MUSICA
data with cloudy SCIAMACHY observations, it is important to realise that
cloudy conditions usually involve a higher humidity than clear-sky
conditions. The ground-based measurements are taken under the latter and so
MUSICA data are first interpolated to the time of the satellite observations
using ECMWF, similar to the method described by Sect. 4.1
(compare Fig. e and f). A height difference
between SCIAMACHY ground pixel and MUSICA station is corrected as described
above. The right panel of Fig. depicts the
result. The bias in δD between SCIAMACHY and MUSICA is significantly
reduced and the difference between panels k and l demonstrates the altitude
dependence of δD. Only satellite measurements with similar altitude
sensitivity to the ground-based observations yield good agreement.
Like Fig. but for cloud retrievals for high-altitude stations. Panels (a), (d),
(g), (j) and (m) show
results for clear-sky measurements without altitude correction; (b), (e),
(h), (k) and (n) show those for clear-sky observations with altitude
correction; and (c), (f),
(i), (l) and (o) show those for altitude-corrected observations with optically thick clouds
within the station height plus or minus 1000 m.
Figure presents the statistics for the
scattering retrievals above the high-altitude stations Jungfraujoch and
Izaña.
The correlation in H2O and HDO is generally good for all
evaluations, but it is small for δD due to the noise in δD being
nearly as large as its seasonality. However, the correlation increases
slightly for the cloud retrieval. When looking at the bias, the behaviour
already seen in the time series is confirmed. It is large for all three
quantities for uncorrected clear-sky observations. The altitude correction
reduces significantly the bias in H2O and HDO, while no change
occurs in δD. The latter is significantly reduced by regarding scenes
with clouds at an altitude near the station height. The biases in H2O
and HDO increase slightly when going from clear-sky to cloudy scenes,
but are still much lower than for the uncorrected case. This may be explained
by the differences in cloud height compared to the station height accepted by
the data selection.
For cloud retrievals from SCIAMACHY observations at polar latitudes the data
quality is reduced. Due to the limited radiometric performance of SCIAMACHY,
the retrieval of cloud properties from methane absorption feature is hampered
by the low SNR for these challenging observation geometries and the dominance
of water vapour absorption in the considered spectral range.
Due to these instrumental problems at polar latitudes, the non-scattering
retrieval is preferred for the global SCIAMACHY data set. For the new TROPOMI
instrument, however, the cloud retrieval is expected to work for all
latitudes, because it has a much better signal to noise ratio and a much
better radiometric calibration, and the size of the ground pixel (and thus
the averaging over areas with potentially varying cloud cover) is much
smaller. The last point is also important, since water vapour is known to vary
considerably both spatially and temporally. In this light, the current study
is also preparation for future investigations with TROPOMI.
Summary and conclusions
In this paper a new data set of satellite observations of column densities
of the water vapour isotopologues H2O and HDO from the
SCIAMACHY instrument is presented, which spans the whole mission period from
January 2003 to April 2012. This is an addition of more than 4 years compared
to . To this end, the degradation of the instrument
has to be mitigated by enlarging the retrieval window compared to
and by eliminating the radiometric offset via
calibration with the Sahara region as a natural target. Atmospheric light
scattering is ignored in the forward simulation of the measurements.
Additionally, the possibility of inferring simultaneously effective cloud
parameter and the water vapour abundance is considered in a dedicated case
study for measurements around high-altitude stations.
Due to the high amount of sensor noise in SCIAMACHY, averaging in time
and/or space has to be adequate to obtain reasonable results. For instance,
monthly medians are meaningful when averaging over a circle with 800 km
radius. More spatial resolutions, such as 1∘×1∘ global
coverage, can be achieved when sacrificing temporal resolution by averaging
over several years.
The satellite observations are validated against low-altitude ground-based
FTIR measurements from the MUSICA project within the NDACC network. In
general, the agreement is good. The Pearson correlation coefficient for
H2O and HDO is between 0.74 and 0.97, and the average bias is
only -3.6×1021 for H2O and
-1.0×1018moleccm-2 for HDO. For δD, the
correlation is also good for most stations (between 0.56 and 0.79), with three
exceptions where the seasonal variation is small. The bias is low except at
polar stations (Eureka and Ny Ålesund). The decreased performance at high
latitudes stems from imprecise measurements caused by the large amount of sensor noise
in SCIAMACHY in combination with difficult measurement geometries. The
average bias in δD over all stations is -8 ‰, but
when excluding Eureka and Ny Ålesund it is only +1 ‰.
High-altitude stations (Jungfraujoch and Izaña) are treated separately in
a case study with a retrieval that takes scattering into account and
additionally infers cloud parameters. Since both instruments observe
different altitude ranges, the bias is large for clear-sky measurements with
3.8×1022moleccm-2 for H2O,
1.0×1019moleccm-2 for HDO and
125 ‰ for δD.
To correct for the altitude effect, partial columns above the station height are computed for SCIAMACHY by cutting an ECMWF
water vapour profile scaled to the retrieved column. This yields good agreement between SCIAMACHY and MUSICA in H2O and
HDO, the mean biases are reduced to -2.2×1020 and 3.4×1017moleccm-2. The bias for the a posteriori δD cannot be corrected that way, however, because the profiles for H2O
and HDO have the same shape, neglecting the increasing depletion with height.
The average bias in δD is drastically reduced to
44 ‰ by considering scenes with optically thick clouds
between 1000 m below and 1000 m above the station height and applying the
altitude correction, since for these scenes the retrieval is sensitive
predominantly above the cloud. For this approach, the results from SCIAMACHY agree well with those from MUSICA in
all three quantities, H2O, HDO and δD. The corresponding biases in H2O and HDO are
1.4×1021 and
4.5×1017moleccm-2.
The scattering retrievals work well for low latitudes and midlatitudes, as
demonstrated in the case study for high-altitude stations, but are unreliable
for polar latitudes. This is caused by the limited radiometric performance of
SCIAMACHY in combination with difficult observation geometries. For the newly
launched TROPOMI instrument, which measures in the SWIR range with a spectral
resolution similar to that of SCIAMACHY but with a much better signal to
noise ratio and a much higher spatial resolution, the method is expected to
work for all latitudes. Moreover, the inferred columns are expected to have a
higher precision, eliminating the necessity to average over long periods of time or
large areas. Thus, future investigations will concentrate on TROPOMI.
The full-mission SCIAMACHY H2O/HDO data set from the non-scattering retrieval described in this paper
is available for download at ftp://ftp.sron.nl/pub/pub/DataProducts/SCIAMACHY_HDO/ (Schneider et al., 2017).
The MUSICA data were downloaded from ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA/
(Barthlott et al., 2016).
The authors declare that they have no conflict of interest.
Acknowledgements
SCIAMACHY is a joint project between the German Space Agency DLR and the Dutch Space Agency NSO with contributions from the Belgian Space Agency.
The data on the ground-based measurements have been provided by the project MUSICA, which has been funded by the European Research
Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement number 256961.
Edited by: Jun Wang
Reviewed by: two anonymous referees
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