AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-1111-2015Total column water vapour measurements from GOME-2 MetOp-A and MetOp-BGrossiM.margherita.grossi@dlr.deValksP.LoyolaD.https://orcid.org/0000-0002-8547-9350AberleB.SlijkhuisS.WagnerT.BeirleS.https://orcid.org/0000-0002-7196-0901LangR.Institut für Methodik der Fernerkundung (IMF), Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, GermanyMPI Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, GermanyEUMETSAT, Allee 1, 64295 Darmstadt, GermanyM. Grossi (margherita.grossi@dlr.de)5March2015831111113318December201328March201415January20152February2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-meas-tech.net/8/1111/2015/amt-8-1111-2015.htmlThe full text article is available as a PDF file from https://www.atmos-meas-tech.net/8/1111/2015/amt-8-1111-2015.pdf
Knowledge of the total column water vapour (TCWV) global distribution is
fundamental for climate analysis and weather monitoring. In this work, we
present the retrieval algorithm used to derive the operational TCWV from the
GOME-2 sensors aboard EUMETSAT's MetOp-A and MetOp-B satellites and perform
an extensive inter-comparison in order to evaluate their consistency and
temporal stability. For the analysis, the GOME-2 data sets are generated by
DLR in the framework of the EUMETSAT O3M-SAF project using the GOME Data
Processor (GDP) version 4.7. The retrieval algorithm is based on a classical
Differential Optical Absorption Spectroscopy (DOAS) method and combines a
H2O and O2 retrieval for the computation of the trace gas vertical
column density. We introduce a further enhancement in the quality of the
H2O total column by optimizing the cloud screening and developing an
empirical correction in order to eliminate the instrument scan angle
dependencies. The overall consistency between measurements from the newer
GOME-2 instrument on board of the MetOp-B platform and the GOME-2/MetOp-A
data is evaluated in the overlap period (December 2012–June 2014).
Furthermore, we compare GOME-2 results with independent TCWV data from the
ECMWF ERA-Interim reanalysis, with SSMIS satellite measurements during the
full period January 2007–June 2014 and against the combined SSM/I + MERIS
satellite data set developed in the framework of the ESA DUE GlobVapour
project (January 2007–December 2008). Global mean biases as small as ±0.035 g cm-2 are found between GOME-2A and all other data sets. The
combined SSM/I-MERIS sample and the ECMWF ERA-Interim data set are typically
drier than the GOME-2 retrievals, while on average GOME-2 data overestimate
the SSMIS measurements by only 0.006 g cm-2. However, the size of these
biases is seasonally dependent. Monthly average differences can be as large
as 0.1 g cm-2, based on the analysis against SSMIS measurements, which
include only data over ocean. The seasonal behaviour is not as evident when
comparing GOME-2 TCWV to the ECMWF ERA-Interim and the SSM/I+MERIS data sets,
since the different biases over land and ocean surfaces partly compensate
each other. Studying two exemplary months, we estimate regional differences
and identify a very good agreement between GOME-2 total columns and all three
data sets, especially for land areas, although some discrepancies (bias
larger than ±0.5 g cm-2) over ocean and over land areas with high
humidity or a relatively large surface albedo are observed.
Introduction
Water vapour is a key component of the Earth's atmosphere and has a strong
impact on the Earth's radiative balance (Trenberth et al., 2007). It is the
most potent natural greenhouse gas, owing to the presence of the hydroxyl bond
which strongly absorbs in the infrared region of the light spectrum (Learner
et al., 2000). As climate warms, the water vapour content in the atmosphere,
which is described by the Clausius–Clapeyron equation, is expected to rise
much faster than the total precipitation amount, which is governed by the
surface heat budget through evaporation (Trenberth and Stepaniak, 2003). This
means that there is a “positive water vapour feedback” which is expected to
further amplify the original climate warming. On the other hand, the net
effect of clouds on the climate is to cool down the Earth surface, at least
under the current global distribution of clouds. Still unclear is the net
cooling or warming effect of clouds in a changing atmosphere. In order to
study this complex interaction and evaluate climate models, observations of
the effective distribution of total column water vapour (TCWV) on a global
scale are fundamental.
The water vapour distribution plays a major role for both meteorological
phenomena and climate via its influence on the formation of clouds and
precipitation, the growth of aerosols, and the reactive chemistry related to
ozone and the hydroxyl radical. Hence, advancing our understanding of the
variability and changes in water vapour is vital, especially considering that,
in contrast to most other greenhouse gases, the H2O distribution is highly
variable.
Despite the important role of water vapour, for a long time very little effort
was spent on the validation and harmonization of experimental water vapour
data sets. Only in 1993 was water vapour included in the list of greenhouse
gases by the World Meteorological Organization (WMO) and difficulties in
observing the water vapour in the troposphere have long hampered observations
and modelling studies. In the 1990s, accurate measurement
techniques began to be developed and, today, a large variety of in situ and
remote sensing techniques for the measurement of integrated water vapour can
be operated from different platforms. Nonetheless, significant limitations
still remain in the coverage and reliability of humidity data sets.
Traditional humidity profiling with a ground-based radiosonde can provide water vapour profiles with
good resolution under all weather conditions, but they are usually available
only twice a day, at sparse locations over the globe (mostly industrialized
areas and land surfaces), and they often contain systematic biases (Wang et
al., 2002) and spurious changes (Gaffen et al., 1991).
Sources of possible random errors and bias include sampling problems, bias due to the non-linear relationship
among moisture variables (i.e. relative humidity, vapour pressure and temperature) and daytime versus nighttime
soundings. Since 1994, when the
global positioning system (GPS) became fully operational, considerable efforts
have been made to develop and improve methods of deriving atmospheric water
vapour using ground-based GPS measurements (e.g. Bevis et al., 1992, 1994;
Rocken et al., 1993, 1997, 2000) at very high temporal resolution (about 30 min).
Complementary to ground-based measurements, which provide accurate
information on the H2O concentration, satellite observations offer the
unique opportunity to study the spatial and temporal variability of water vapour on a global scale.
They also allow us to assess the distribution of the column-integrated (the so-called total column) water
vapour in remote places with none or only few in situ measurements, but they are typically limited
in their vertical and temporal resolution. Most commonly used for the retrieval of water vapour from space
are microwave sensors, e.g. the Special Sensor Microwave Imager (SSM/I), which are able to provide
measurements at high spatial (horizontal) resolution (Bauer and Schluessel, 1993), but are usually
constrained over ice-free ocean areas. Data from these instruments are operationally assimilated into
numerical weather prediction reanalysis models like the ERA-Interim from the European Centre for
Medium Range Forecasts (ECMWF, Dee et al., 2011a, b) and, until the beginning of this century,
represented the only consistent long-timescale data set for water vapour.
Sensors operating in the near infrared, like the Medium Range Resolution
Imager Spectrometer (MERIS) on ENVISAT (Li et al., 2006), can also derive water
vapour over land, but cannot retrieve this product in cloudy conditions.
Moreover, the very low albedo of the ocean surface in the near infrared
limits retrieval in these areas. However, measurements are possible in
sun-glint or above-cloud conditions over ocean, since these two conditions
increase the surface albedo. Long-term water vapour observations in infrared
bands are available from instruments such as Television Infrared Observation
Satellite Program (TIROS) Operational Vertical Sounder (TOVS), Advanced TIROS
Operational Vertical Sounder (ATOVS) and Atmospheric Infrared Sounder (AIRS)
(e.g. Chaboureau et al., 1998; Li et al., 2000; Susskind et al., 2003).
Temperature and moisture profiles with a vertical resolution of about 2–5 km can also be obtained from the Interferometric Monitor for Greenhouse gases
(IMG, e.g. Ogawa et al., 1994), the Tropospheric Emission Spectrometer (TES,
e.g. Shephard et al., 2008; Worden et al., 2012) and the Infrared
Atmospheric Sounding Interferometer (IASI, e.g. Clerbaux et al., 2009;
Hilton et al., 2012). Satellite infrared observations can distinguish
different tropospheric layers, but have the disadvantage of being less
sensitive to the surface emission from the lowest layers, where most of the
atmospheric water vapour is present. This type of observation also requires
model input for the retrieval. A recently developed method for the retrieval
of water vapour distribution is the utilization of data from the GPS
satellites (see, e.g. Dai et al., 2002). Despite the relatively small spatial
coverage, GPS measurements from space and the ground are valuable because their
information complements that provided by satellite radiance measurements.
Sensors covering the ultraviolet, visible and near infrared range (UVN) with
a relative high spectral resolution, e.g. the Global Ozone Monitoring
Experiment (GOME) on European Remote Sensing (ERS) satellite ERS-2 (Burrows
et al., 1999), can accurately map the column densities of the atmospheric
H2O over all surfaces. The analysis is performed in the visible spectral
range, where the radiation comes mainly from surface reflection, or, above
dark surfaces, from tropospheric Rayleigh scattering. These measurements are
thus very sensitive to the H2O layers close to the surface, but, similar
to MERIS, the retrievals are typically hampered by clouds. GOME data have
been used, among others, for the study of long-term variations in
tropospheric water vapour trends (Mieruch et al., 2008; Wagner et al., 2006)
and to monitor and investigate inter annual climate variability phenomena
observed on Earth, such as El Niño/La Niña (Wagner et al., 2005; Loyola et
al., 2006). A second generation of this kind of instrument is represented by
the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography
(SCIAMACHY, Bovensmann et al., 1999), on the ENVISAT platform. Current
operational UVN sensors are the Global Ozone Monitoring Experiment-2 (GOME-2)
instruments, the subject of the current study, on board the MetOp-A and
MetOp-B satellites (hereafter GOME-2A and GOME-2B sensors). The GOME-2
spectrometers lay the foundation for a consistent data record of H2O
GOME-type observations, which already spans more than 18 years and will be
further extended by GOME-2/MetOp-C, a third satellite which is planned to be
launched in 2018.
TCWV from measurements of the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites has already proved to be a
valuable input quantity for climate models (Noël et al., 2008; Slijkhuis et al., 2009; Kalakoski et al., 2011; Mieruch et
al., 2010), and could be useful for assimilation into numerical weather prediction models, e. g. for following dynamical structures in water vapour when a high absolute accuracy is not required.
In contrast to other satellite data sets, the GOME-2 product has the
advantage that it covers the entire Earth, including both ocean and
continents, leading to a more consistent picture of the global distribution
of the atmospheric humidity. Long-term satellite data sets are essential for
atmospheric monitoring and the impact of human intervention in a changing
environment has brought about increasing concern for detecting trends in
water vapour.
In this paper, we present the H2O retrieval algorithm used for the
operational EUMETSAT's Satellite Application Facility on Ozone and
Atmospheric Chemistry Monitoring (O3M-SAF) water vapour products from the
GOME-2 sensors and we compare it with independent satellite instruments and
model data. On the basis of this comparison, we are able to estimate the
accuracy of the retrieval algorithm and we can make an assessment of the
quality and consistency of GOME-2 TCWV product.
The validation of the GOME-2 TCWV produced with an earlier version of the
retrieval algorithm was already presented in Kalakoski et al. (2011). From
the comparison with radiosondes, a mean positive bias of 0.11 g cm-2 was
found, while from the comparison with SSM/I products typical biases of about
0.2 g cm-2 were retrieved for monthly global averages. More recently, the
ESA DUE GlobVapour project (Schröder et al., 2012a) has focused on the
development of multi-annual global water vapour data sets and, among other
deliverables, has provided a first version of a consistent TCWV data set from
the GOME, SCIAMACHY and GOME-2 sensors for the time period 1996–2008. In the
framework of the GlobVapour project, extensive validation activities were
carried out, pointing to large differences with positive and negative bias
values on regional scales (Schröder et al., 2012c). The variability of the
bias was found to be generally large (on the order of 0.2 g cm-2). It was
observed that the GOME/SCIAMACHY/GOME-2 product tends to be drier than the
compared ground-based and satellite data (including Global Upper-Air Network
(GUAN) stations, three Atmospheric Radiation Measurement (ARM) radiosonde
sites and ATOVS data) with the exception of AIRS (Aqua) (mean bias 0.16 g cm-2). Larger differences on a regional basis were observed in the
comparison with SSM/I+MERIS with negative bias (-0.1 g cm-2) over ocean
and smaller positive bias in land regions (0.03 g cm-2). Also in this
case, a previous version of the GOME-2 TCWV algorithm was used.
A detailed description of the global validation of the newest operational
GOME-2 TCWV product, using radiosonde data from the Integrated Radiosonde
Archive (IGRA) and GPS data from the COSMIC/SuomiNet network, can be found in
Kalakoski et al. (2014). The comparison was performed for the period December
2012–July 2013, using the latest operational water vapour product. A good
agreement of both GOME-2A and GOME-2B with ground-based data sets is
observed. GOME-2 data show small negative (dry) median difference against
radiosonde (GOME-2A: -2.7%; GOME-2B: -0.3%) and positive (wet) median
difference against GPS observations (GOME-2A: 4.9%; GOME-2B: 3.2%). For
TCWV below 1 g cm-2, large wet biases are observed, especially against
GPS observations. Conversely, at values above 5 g cm-2, GOME-2 generally
underestimates both ground-based observations. In Antón et al. (2014), the authors validate the GOME-2 data set against six reference atmospheric
sounding data sets obtained from the GCOS Reference Upper-Air Network (GRUAN).
They found a reasonably good correlation between GOME-2 and sounding TCWV
data (determination coefficient (R2) of 0.89). A remarkable improvement
of the correlation was found by selecting cloud-free cases (R2= 0.95). Also
in this study, the satellite-sounding differences showed a strong negative
dependence on the magnitude of the reference TCWV values.
Summary of the GOME-type instrument characteristics, illustrating
the main improvement of GOME-2 compared to its predecessor GOME/ERS-2.
(*) GOME-2A tandem operation since 15 July 2013. (**) GOME global
coverage lost in June 2003.
The remainder of this paper is organized as follows. After a short
description of the GOME-2 instruments in the following, Sect. 3 gives a
detailed overview of the H2O retrieval algorithm and introduces the TCWV
data used for the comparison with model data and independent satellite
measurements. In Sect. 4, the GOME-2 water vapour columns from MetOp-A and
MetOp-B are compared during their overlapping time frame January 2013 through
June 2014. A quantitative analysis of the distribution of daily and monthly
mean biases is performed. The results of the comparisons with ECMWF
ERA-Interim data and satellite measurements from SSMIS, SSM/I and MERIS for
the full period January 2007–June 2014 are illustrated in Sect. 5.
Finally, conclusions are drawn in Sect. 6.
GOME-2 instruments
The GOME-2 sensor (Callies et al., 2000) is the follow up of the Global
Monitoring Experiment (GOME), launched in 1995 on ERS-2 (Burrows at al.,
1999), and the SCIAMACHY sensor, launched in 2002 on ENVISAT (Bovensmann et
al., 1999). GOME-2 is a nadir viewing scanning spectrometer which covers the
same spectral range as GOME, i.e. from 240 to 790 nm, with a spectral
resolution of about 0.54 nm in the visible spectral region. Additionally,
two polarization components are measured with polarization measurement
devices (PMDs) using 30 broadband channels covering the full spectral
range at higher spatial resolution. The German Aerospace Centre (DLR) plays a
major role in the design, implementation and operation of the GOME-2 ground
segment for trace gas products, including TCWV, as well as cloud properties
in the framework of the EUMETSAT O3M-SAF project.
We can identify important differences between the GOME instrument on the
ERS-2 satellite and the GOME-2 sensors (Munro et al., 2006). First, the
spatial resolution of the GOME data is 320×40 km2, whereas the
GOME-2 instruments have a smaller nominal ground pixel size (typically 80×40 km2). Because of the improved spatial resolution, GOME-2 data
are less influenced by partly cloudy scenes and the instruments are also able
to detect strong spatial gradients in the H2O distribution. Second, the
default swath width of the GOME-2 scan is 1920 km, while both GOME and
SCIAMACHY have a scan width of 960 km. Therefore, the GOME-2 instruments
employ only about 1.5 day to reach global coverage at the equator, while
GOME/ERS-2 requires about three days
After the failure of the ERS-2
tape recorder in June 2003, GOME measurements have been limited to the
northern hemisphere and the Antarctic.
. In Table , we
summarize the characteristics of the different GOME-type sensors.
The first GOME-2 instrument was mounted on the MetOp-A satellite (GOME-2A),
which follows a sun-synchronous orbit with a mean altitude of 817 km. The
overpass local time at the equator is 09:30 Local Time (LT) with a repeat
cycle of 29 days. MetOp-A was launched on 19 October 2006 and GOME-2 TCWV
products are available from January 2007 onwards. A second GOME-2 type sensor
on board of the MetOp-B satellite (GOME-2B) was launched on the 17
September 2012 and has been fully operational since December 2012. GOME-2 tandem
operations started on 15 July 2013. In the tandem mode, GOME-2A operates on a
reduced swath width of 960 km, thereby increasing its spatial resolution
(40 by 40 km), while GOME-2B continues to operate on a nominal wide swath
of 1920 km. This configuration allows the use of the higher spatial
resolution data to further study the consistency of the two products in the
overlap regions of the GOME-2A and GOME-2B orbits.
The third and final satellite of the EUMETSAT Polar System series,
GOME-2/MetOp-C, is planned to be launched in 2018, guaranteeing the
continuous delivery of high-quality H2O data until 2023.
GDP 4.7 H2O column algorithm
In the framework of the EUMETSAT O3M-SAF project, the algorithm used to
generate the operational water vapour product is the level-1-to-2 GOME Data
Processor (GDP) version 4.7, integrated into the Universal Processor for
Atmospheric Spectrometers (UPAS, version 1.3.9) processing system at DLR and
developed at the Max Planck Institute for Chemistry (MPI-C, Mainz).
Various retrieval methods of the TCWV from space-born spectrometers operating
in the visible region have been developed (AMC-DOAS: Noël et al., 1999,
Lichtenberg et al., 2010; ERA: Casadio et al., 2000; OCM: Maurellis et al.,
2000; IGAM: Lang et al., 2003, 2007; Classical DOAS: Wagner et al., 2003). In
contrast to most other methods, the GDP 4.7 algorithm for the retrieval of
water vapour is directly based on a classical Differential Optical Absorption
Spectroscopy (DOAS, Platt, 1994), performed in the wavelength interval
614–683 nm, and does not include explicit numerical modelling of the
atmospheric radiative transfer. One specific advantage of the DOAS method is
that it is only sensitive to differential absorptions, which makes the
retrievals less sensitive to instrument changes or instrument degradation.
The algorithm consists of three basic steps (described in detail by Wagner et
al., 2003, 2006): (1) DOAS fitting, (2) non-linearity absorption correction
and (3) vertical column density (VCD) calculation.
In the first step, the spectral DOAS fitting is carried out, taking into
account absorption by O2 and O4, in addition to that of water vapour. A
single H2O cross section is used, based on line-by-line computations using
HITRAN (Rothman et al., 2009) H2O line parameter for a fixed temperature
and pressure of 290 K and 900 hPa, followed by a GOME-2 slit function
convolution. In Wagner et al. (2003), the authors investigated the
temperature and pressure dependence of the H2O absorption structure by
varying the temperature by ±20 K and the pressure by ±100 hPa. The
analysis of the GOME-2 measurements using these different H2O spectra
yielded H2O SCDs varying by only ±3 %. To improve the broadband
filtering, three types of vegetation spectra are included in the fit. They
are included also over water, as marine chlorophyll-containing substances may
show similar effects and can cause strong interference with atmospheric
absorbers (Wagner et al., 2007). In addition, we use a synthetic ring
spectrum calculated from the Sun's spectrum (Gomer et al., 1993; Wagner et
al., 2009) to correct for the ring effect (filling-in of well-modulated solar
and absorption features in the Earth shine spectra) and, finally, an inverse
solar spectrum to compensate for possible offsets, e.g. caused by
instrumental stray light.
Since the highly fine structured H2O (and O2) absorption bands cannot
be spectroscopically resolved by the GOME-2 instrument, the water vapour
slant column density (SCD: the concentration integrated along the light path)
is no more a linear function of the atmospheric H2O column density
(Solomon et al., 1989; Wagner et al., 2000). In the second step of our
retrieval, we therefore apply a correction for the absorption non-linearity
effect. The correction factors are calculated from numerical simulations of
this effect by mathematical convolution of the high resolved H2O spectrum
with the instrument slit function (Van Roozendael et al., 1999; Wagner et
al., 2003). This effect can become important especially in the tropics, for
large H2O SCDs. For example, for an atmospheric H2O SCDs of
1.5× 1023 mol cm-2 (∼4.5 g cm-2), the underestimation is about
30%.
In the last step, the corrected water vapour slant columns determined with
the DOAS fitting are converted to geometry-independent vertical column
densities (VCDs) through division by an appropriate air mass factor (AMF),
which, in this case, is derived from the measured O2 absorption. We divide
the H2O SCD by a “measured” AMF, which is defined as the ratio between
the simultaneously retrieved SCD of O2 and the known VCD of O2 for a
standard atmosphere. The desired TCWV is computed as follows:
ΩH2O,0=ΩH2O,θAO2=ΩH2O,θΩO2,θ/ΩO2,0,
where Ωx,0 is the VCD, Ωx,θ is the SCD and Ax
is the AMF of the chemical species x. This simple approach has the
advantage that it corrects in first order for the effect of varying albedo,
aerosol load and cloud cover using the satellite observations themselves,
without additional independent information which is usually also not
available. However, the underlying assumption that the AMF of O2 is
similar to the AMF for water vapour can produce systematic differences in the
retrieval. Because the vertical profile of H2O is much more peaked in the
troposphere with respect to that of O2 (the H2O scale height is only
about 2 km compared to 8 km for O2), the measured AMF derived from the
O2 absorption is in general larger than the AMF for water vapour. In the
case of low lying clouds, for example, the dominant part of the H2O total
column is located near the surface and therefore shielded, while most of the
O2 contribution is still above the clouds.
The errors in the individual TCWV measurements due to the application of an
O2 AMF can be quite large. One possibility for reducing these errors would be
to use the appropriate H2O AMFs derived from radiative transfer (RT)
calculations instead. In the future, we plan to identify, and possibly
correct, the influence of clouds and surface albedo on the TCWV using the
LIDORT RT model (Spurr et al., 2008). However, such calculations are complicated
because typically the atmospheric aerosol extinction profile is not known,
and clouds strongly affect RT calculations. Because of these difficulties, we
follow a different approach here: we introduce a correction factor look-up
table in the AMF computation:
ΩH2O,0=ΩH2O,θAO2×Cratio=ΩH2O,θΩO2,θ/ΩO2,0×Cratio.
The factor Cratio depends on the solar zenith angle (SZA), on the line
of sight angle (LOS) and relative azimuth (RAZ) of the satellite instrument
and on the surface albedo (Alb). Moreover, the exact vertical profile of
H2O in the troposphere and the cloud cover have a strong impact. The
correction factors were derived from radiative transfer calculations using
the Monte Carlo Atmospheric Radiative Transfer Inversion Model (McArtim,
Deutschmann et al., 2011), taking into account an average H2O profile
calculated from relative humidity profiles assuming an average lapse rate
(Minschwaner and Dessler, 2004; Wagner et al., 2006) and an O2 profile
from the US standard atmosphere. The relative sensitivity of the measured
O2 absorption compared to H2O absorption also varies significantly
depending on surface albedo values. In the radiative transfer model (RTM), the correction factor was
computed assuming a fixed surface albedo of 2% and cloud-free conditions.
Similar results are obtained assuming 3% surface albedo over ocean and
5% cloud fraction (Wagner et al., 2011). The albedo database derived from
GOME observations (Koelemeijer et al., 2003) at high latitude (>50∘) and
from SCIAMACHY observations (Grzegorski, 2009) at mid and low latitudes (>40∘) was used in order to derive the dependency of the computed AMFs to the
actual surface albedo. It should be mentioned that the global surface albedo
map described above is the only external information needed in the retrieval
algorithm (in addition to the average H2O and O2 profiles). Since it
does not rely on other external input data, the GOME-2 TCWV product is
especially valuable for long-term series and climatological studies.
Error budget and cloud masking
The error budget in the H2O product can be separated into two parts: errors
affecting the retrieval of the slant columns (DOAS-related errors), and
errors affecting the conversion of the SCD into VCD (AMF-related errors).
However, these latter errors are difficult to quantify, because the water
vapour AMF is not based on explicit RT calculations, and there may be
compensating effects. For example, in the case of snow surfaces, the high
surface reflectivity would lead to a relatively high sensitivity for H2O
in the lower troposphere, and hence a lower AMF-ratio of O2 to H2O, but
above cold surfaces the tropospheric H2O column is reduced, causing the
opposite effect. The following potential error sources are taken into
account: relative fit error of H2O and O2, uncertainties in the
spectroscopic data (about 10%) and especially uncertainties due to clouds.
The total, relative error can be derived by the following formula (Wagner et
al., 2011):
Δtotal=ΔH2O2+ΔO22+(0.1)2+ΔRTM2.
The source of error due to clouds (ΔRTM2) increases with decreasing O2 SCD, indicating
strong cloud shielding. Therefore, on the GOME-2 H2O product, cloudy conditions are flagged.
In our latest version of the retrieval algorithm (GDP 4.7), two cloud
indicators are used to identify and flag cloudy pixels. This is necessary to
remove potential systematic cloud effects due to the different altitude
profiles of H2O and O2 which might still appear in the water vapour
product. The first cloud flag is set if the product of cloud fraction and
cloud-top albedo exceeds 0.6 (anomalously high cloud-top reflection). In this
case, the H2O total column is also set to “invalid” as the pixel might be
considered fully clouded. The GOME-2 cloud fraction is determined with the
OCRA algorithm using broadband radiance measurements in the UV/VIS range,
while cloud-top height and cloud-top albedo are retrieved with the ROCINN
algorithm using the spectral information in the Oxygen-A band in and around
760 nm (Loyola et al., 2007 and 2010). The GOME-2 detector sequential
read-out may induce spatial aliasing effects for highly inhomogeneous scenes
in the case that the retrievals use measurements far away from the O2
band. The PMD measurements are aligned to the O2 A-band measurements (end
of channel 4) to avoid spatial aliasing effects between the OCRA/ROCINN
derived cloud properties. Possible spatial aliasing effects between the cloud
properties and the water vapour measurements (beginning of channel 4) are
minimized by using a conservative cloud screening scheme.
The second H2O cloud flag is set if the retrieved O2 slant column is
below 80% of the maximum O2 SCD for the respective solar zenith angle
(roughly when about 20% from the column to ground is missing). Especially
for low and medium high clouds, the relative fraction of the VCD from the
ground which is shielded by clouds for O2 and H2O can be quite
different. Therefore, we require that the main part of the O2 column is
present. The maximum values of the O2 SCD have been derived from measured
optical depth of the O2 absorption along GOME satellite orbits as a
function of the solar zenith angles and implemented in a look-up table in the
retrieval code. We also consider the line of sight dependence of the O2
threshold for mainly cloud-free observations by multiplication for an
additional function (Wagner et al., 2011). The choice of having a threshold
of 80% of the maximum values represents a good compromise with respect to
the number of measurements still available after selection and the correction
of the strongest cloud effects on the TCWV product. This second cloud flag
also rejects observations with high surface elevation, e.g. the Himalayas or
the Andes.
Scan angle dependency correction
GOME-2A total column water vapour as a function of the number of the
pixel index within the scan (0 = east, 24 = west) averaged in different latitude
bands (20–50∘ S, 20∘ S–20∘ N,
20∘–50∘ N) before (solid line) and after (dashed line
with points) the SAD correction for January 2013. We show
separately the empirical correction applied over land measurements (left
panel) and over ocean measurements (right panel). The error bars represent
the spread of the water vapour data points. The statistical bias as a
function of the scan angle is well determined due to the large number of
measurements.
As already mentioned, the GOME-2 observations have a much wider swath
compared to GOME and SCIAMACHY (see Table ). While this
broader swath results in a largely improved coverage, some modifications
to the H2O retrieval become necessary. In particular, we observe that the
GOME-2 total column water vapour presents a significant Scan Angle Dependency
(SAD), which strongly affects the quality of the product. This scan angle
dependency is very similar for MetOp-A and MetOp-B, while a SAD is also
observed in other trace gas retrievals from GOME-2, such as O3 and NO2
columns.
There is a bias up to 1 g cm-2 between the H2O total columns for the
west and east part of the swath and the central ground pixels. This effect is
particularly strong over ocean areas, while the land surface is less
affected. There are two major contributing factors. First, the accuracy of
the retrieved TCWV is reduced because of sun-glint over ocean regions which
may strongly enhance the back-scattered radiation, especially at low wind
speed (highly specular reflection). In this case, the observations are
contaminated by the bright pattern of the specular reflection of the Sun by
the wavy sea surface. The GOME-2 algorithm can distinguish sun-glint areas by
analysing the broadband polarization measurements (Loyola et al., 2011), but
the pixels we select with this method (typically less than 4% of the
total) represent only few measurements in extreme sun-glint geometry.
Therefore, we still require a correction for the small signal of
water-leaving radiances in directions away from the glitter. Second, the
accuracy of the surface albedo data available for the oceans is limited, and
therefore a constant albedo (0.03) is used in the AMF calculation for the
sea surface (Grzegorski et al., 2004).
An accurate analysis of the GOME-2 H2O total columns retrieved with a
previous version of the GDP algorithm (GDP 4.6) revealed a systematic SAD
already in the H2O SCD, especially for cloud-free pixels. This suggested a
correlation between a simplified Lambertian assumption used to describe the
Earth reflectivity and the SAD. From radiative transfer calculations using
bidirectional reflectance distribution function (BRDF) kernels based on a
Cox–Munk distribution (Cox and Munk, 1954), we found that using a simple
Lambertian approach and ignoring the BRDF, we underestimate the AMF over
ocean in the east regions of the scan (and overestimate it in the west
regions) up to 30% (Valks et al., 2012). Some residual line of sight
dependence is likely due to the Rayleigh single scattering contribution,
since the instrument is polarization sensitive. Moreover, in order to compute
the H2O total column, we use the simultaneously observed O2 slant
column density. A correction factor accounts for the different altitude
profile of H2O and O2 (the factor Cratio mentioned in Sect. 3).
Since the look-up tables containing the correction factors are computed for
average conditions of cloud cover, albedo, and a single H2O profile, some
residual SAD might remain, especially in more extreme atmospheric scenarios.
In GDP 4.7, we introduce an empirical statistical correction for the scan
angle dependency, based on the full six-year time series of GOME-2/MetOp-A
measurements (2007–2012). Multi-annual monthly mean H2O total columns are
created and employed to select the latitudinal binned regions which contain
a sufficiently large number of measurements. We require that, for a latitude
band (1∘), the ratio between the number of water vapour measurements
with a given pixel number and with pixel number used for the normalization
does not vary by more than 20%. In this way, we avoid the correction being affected by natural variability in the H2O total columns. We use scan
angle read-outs toward the nadir scan angle (scan pixel numbers 9-10-11) as
reference values to normalize the H2O total column for every forward angle
position and derive a self-consistent correction. This is done because scan
angle measurements close to the nadir direction show the best agreement in
comparisons with ground-based and satellite observations. Finally, a
polynomial is fitted to the normalized measurements in order to remove
outliers and obtain a smooth correction function. With our procedure,
residuals are on the order of a few percent. Outside the valid latitudinal
range, an interpolation between the last valid value and 1 (i.e. no
correction) for ±90∘ latitude is performed. A similar algorithm was
originally developed for correcting the scan-angle dependency of GOME-2 total
ozone product (Loyola et al., 2011).
Two different corrections are implemented over land and over sea, to take
into account the diverse reflectivity properties of the surface. We found
that the biases between east and west pixels are related to the viewing
geometry. The correction values depend on the surface type (land or ocean),
the scattering angle (pixel scan number) and the latitude, and vary from
month to month. In the left panel of Fig. , we depict the SAD
correction for land, while in the right panel the correction applied over
ocean regions is shown. In both figures we can distinguish the TCWV before
(solid line) and after (dashed line) the empirical correction for the scan
angle dependency. The lines refer to latitudinal averaged quantities in the
northern, tropical and southern hemisphere regions for January 2013. While in
austral summer (December–February) the correction is larger in the 20∘–50∘ south
regions, in the northern hemisphere summer months (June–August), it is larger for
the 20∘–50∘ north region. The error bars in Fig.
represent the spread of the water vapour data points (defined as SE =S/N, where S is the standard deviation of the sample mean and N the number of
measurements). Because of the large natural variability in the spatial
distribution of the water vapour data, the standard deviation is quite large.
Nevertheless, the statistical bias as a function of the scan angle is well
determined due to the large number of measurements.
H2O total columns derived from GOME-2B measurements for the 7
January 2013 without the SAD correction (on the left) and using the SAD
correction (on the right). Only cloud-screened data corresponding to solar
zenith angles smaller than 87∘ are shown.
Difference between H2O total columns derived from GOME-2B
measurements for the 7 January 2013 using the SAD correction and without the
SAD correction.
Figure shows the global distribution of the H2O total
columns derived from GOME-2B measurements for the 7 January 2013 (before the
tandem operation mode) with (right panel) and without (left panel) SAD
correction, for cloud-screened measurements only. The empirical SAD
correction based on 6 full years of GOME-2A data is also consistently applied
to the GOME-2B product (the scan angle dependency of the TCWV product is
similar for both GOME-2 sensors). The white regions in the map show the areas
where the product of cloud fraction and cloud-top albedo exceeds 0.6, while
the O2 cloud screening rejects mostly GOME-2 measurements over the west
part of scan, since these are measurements with small AMF and low GOME-2
sensitivity for H2O. The net effect of the empirical correction is a
reduced bias in the total column water vapour distribution between the east
and west part of the GOME-2 orbit. Differences between TCWV product derived
with and without SAD correction for the 7 January 2013 are shown in
Fig. . The bias is especially high in the equatorial region,
where the H2O total column presents lower values in the east part of the
scan when applying the SAD correction (e.g. over the Indian Ocean, east of
Madagascar) and smaller and positive values in the west part of the scan (see the
orange-red regions over South America, and the Pacific and Indian Oceans). In all
subsequent analyses, the GOME-2 data are generated with the new version of
the retrieval algorithm including the SAD correction, unless otherwise
stated.
GOME-2 Level 2 TCWV and cloud products generated using the GDP 4.7 algorithm
are available from the DLR ftp server in HDF5 format. Information about the
operational water vapour product can be found at
http://atmos.caf.dlr.de/gome2. Documents, reports, quick-look maps and links to related
information are also available on this website.
GOME-2A vs. GOME-2B
We compare the GOME-2/MetOp-B H2O total columns with those from its
predecessors GOME-2/MetOp-A, based on more than one and a half years of
overlap between the two satellites, from December 2012 to June 2014. We
perform the inter-comparison between GOME-2A and GOME-2B data taking into
account either (mostly) cloud-free or all available measurements for one
particular day and monthly means. For the monthly comparison, we first
analyse the spatial distribution of the bias from gridded monthly mean
GOME-2A and GOME-2B water vapour columns. Then, in order to make the data
selection in the two instruments as similar as possible, a comparison using
only co-located measurements is performed. A quantitative analysis of the
bias between GOME-2A and GOME-2B as a function of the latitude concludes this
section.
Daily GOME-2 comparison
Top panels: daily averages of H2O total columns from GOME-2A and
GOME-2B for the 7 January 2013 with SAD correction applied. Only data
corresponding to solar zenith angles lower than 87∘ are used. GOME-2A
and GOME-2B measurements are separated by approximately 48 min in time.
Bottom panels: geographical distribution of the differences between GOME-2A
and GOME-2B total column water vapour for the 7 January 2013 when the SAD
correction is applied to the two data sets (right panel, GDP 4.7) and without
SAD correction (left panel, GDP 4.6). Cloud-free
co-located measurements are shown in the plot.
In the top panels of Fig. , we show a map of the
H2O total columns for the 7 January 2013 from GOME-2A (left panel) and
GOME-2B (right panel) measurements to provide a first illustration of the
geophysical consistency of the TCWV products from the different instruments.
In both cases, we applied a SAD correction over ocean and land areas.
Overall, we observe a very good agreement between the two data sets and the
same spatial patterns in the humidity distribution, with high values in the
tropics and low humidity at higher latitudes. Since the GOME-2 products are
only derived from daylight observations, a large area around the Arctic is
blanked out in the northern hemispheric winter. Here, we do not apply any
cloud mask to the data to show the daily coverage of the two GOME-2
instruments.
In the bottom panels of Fig. , we investigate the
differences between GOME-2A and GOME-2B TCWV for the 7 January 2013, when the
SAD correction is applied to the two data sets (right panel), and without SAD
correction (left panel). The inter-comparison has been performed using
cloud-free and co-located pixels. Co-location areas are determined applying
the following criteria: 55 km for the maximum distance between two
measurements in the chosen day. In the tropics, the number of measurements is
drastically reduced mainly because we have the smallest overlap between the
GOME-2A and GOME-2B orbits, but also because of the larger chance of clouds.
On average, the TCWV for GOME-2B is slightly higher than for the GOME-2A product,
independent of the presence of a SAD correction in the two data sets (i.e.
if we use GDP 4.6 or GDP 4.7 retrieval), with mean bias values of
-0.05 g cm-2 and a standard deviation of about 0.5 g cm-2.
The GOME-2A and GOME-2B co-planar orbits are 174∘ out of phase. This results
in a temporal separation of the measurements at co-locations of approximately
48 min, and leads to differences in the TCWV because of tropospheric
dynamics. The overall mean bias does not change significantly using the
GOME-2 data with and without the SAD correction. However, because of the
additional scan-angle bias, at single locations the difference between
GOME-2A and GOME-2B TCWV is larger without SAD correction, as we can see by
comparing the left and right plots of Fig. . This
is due to the fact that, when looking at the daily co-locations, we are
comparing data from different parts of GOME-2A and GOME-2B swaths (and thus
different lines of sight). Using the data sets without SAD correction (left
panel of Fig. ), we can see that differences alternate between positive and negative values, depending on whether the
east part of the GOME-2A swath is collocated with the west part of the
GOME-2B swath or vice versa. This effect is reduced in the GDP 4.7 data sets
(right panel of Fig. ). There we can observe null
bias (in green) in extended sub-tropical regions, such as continental northern
Africa and Asia. The remaining differences in the tropics are mainly related to
the presence of low clouds, the asymmetric cloud screening (due to the O2
cloud flag indicator, see Sect. 3.2) and low statistics (because of the
smaller overlap region).
Monthly GOME-2 comparison
The global average monthly mean bias between GOME-2A and GOME-2B data sets
for the period January 2013–June 2014 is shown in Fig. .
The analysis is performed comparing gridded monthly mean data. From the 15
July 2013 GOME-2A operates in tandem mode, and the overlapping area between
the orbits of the two satellites is reduced. However, the mean bias values
are consistent with the one retrieved in previous months. Averaging over the
full time period, we find a small mean negative bias of -0.006±0.018 g cm-2, while the biggest discrepancies are observed in January 2013 (mean
bias of -0.025 g cm-2). GOME-2B tends to produce slightly larger H2O
total column values than GOME-2A, but not more than 1.25%. The standard
deviation for water vapour data is dominated by natural variability and
is therefore quite large (see error bars in Fig. ). Very
similar results are obtained using only co-located data, since the GOME-2A
and GOME-2B data sets are processed with the same algorithm and the same
cloud screening criteria.
Global monthly mean H2O total column bias between GOME-2/MetOp-A
and GOME-2/MetOp-B for the period January 2013–June 2014. The large error
bars represent the standard deviation of the monthly averaged bias and are
dominated by natural variability.
Studying the spatial distribution of the bias in January 2013, we observe
that less than 3% of the locations present a bias bigger than 0.5 g cm-2 in absolute value. The mean difference between GOME-2A and GOME-2B
H2O total columns is within the optimal accuracy threshold (5%)
specified in the O3M-SAF Service Specification Document (Hovila and Hassinen, 2013). This document presents the requirements for operational product and
services of the EUMETSAT's O3M-SAF. The accuracy value is defined as the root
mean square difference between the measurements and the reference data set.
This shows that the GOME-2B H2O total column product can be used for
scientific purposes to extend the GOME-type H2O time series.
To access the consistency between the two samples, we performed an orthogonal
regression using gridded monthly GOME-2A and GOME-2B data. The grid cells
used to bin the GOME-2 measurements have an extent of 0.5∘ latitude ×0.5∘ longitude. Figure shows the scatter
plot of cloud-screened GOME-2A data against GOME-2B for January 2013 together
with the histogram of the distribution of the differences GOME-2A –
GOME-2B. The slope of the regression is very close to unity (0.992) and the
offset is very small and negative (-0.009 g cm-2), consistent with the
mean bias results.
Left panel: scatter plot of GOME-2A monthly mean total columns
against GOME-2B monthly mean total columns, for January 2013 and cloud-free
sky. The slope of the orthogonal regression is 0.992 with an offset of
-0.009 g cm-2. Right panel: histogram of the difference GOME-2A –
GOME-2B, for the points in the scatter plot. The mean bias is
-0.0249 g cm-2 with a root mean square error of
0.297 g cm-2 and a negative skewness.
Zonal mean H2O total column from GOME-2A (red points) and from
GOME-2B (green points) as a function of latitude for January 2013 and bias
between GOME-2B and GOME-2A monthly averaged H2O total column. The results
refer to daily co-located GOME-2A and GOME-2B measurements with cloud mask
(left plot) and without cloud mask (right plot).
To investigate the differences between the GOME-2A and GOME-2B TCWV as a
function of latitude, we have repeated the inter-comparison exercise for
co-located (within 24 h) measurements, with and without cloud mask, and
we further computed the zonal averages for 2.5∘ latitude intervals.
Figure shows the comparison of zonal TCWV values for January
2013 in two different cases: for (mostly) cloud-free measurements (left
panel) and for all measurements (right panel). The points in the left panels
of each plot represent the individual mean water vapour measurements as a
function of latitude (red for GOME-2A, green for GOME-2B). From these plots,
we can infer that there is a very good agreement between GOME-2A and GOME-2B
measurements for all latitudes. In order to examine more clearly the
latitudinal variations, in the right panels of Fig. we
show the difference GOME-2B–GOME-2A H2O total column. The largest
absolute deviations occur near the equator (10∘ N–10∘ S). On average,
at these locations the GOME-2B total columns are slightly larger than the
GOME-2A columns (about 2–3 % larger in relative value), as inferred also
from the scatter plots (Fig. ). The relative
difference is always positive, especially in the tropical area, which means
that the GOME-2B data present a small wet bias with respect to GOME-2A. The
maximum bias reaches 0.117 g cm-2 (2.7%), and the mean bias is higher
in the southern hemisphere than in the northern one. We can also notice that
the scatter in the differences is generally bigger for cloud-free
measurements than for unfiltered data. The smaller number of data points due
to the cloud selection translates as a larger root mean square error (RMSE)
in the former case (see Grossi et al., 2013).
Comparison results and discussion
To assess the quality of the satellite products, both the GOME-2A and the
GOME-2B H2O total column product are compared to independent satellite
observations and ECMWF ERA-Interim reanalysis data. Each of these data sets
has its own advantages and disadvantages and therefore, from the different
comparisons, we can study different properties of the GOME-2 data sets.
Comparison data sets
First, GOME-2A and GOME-2B measurements are compared with corresponding data
from the European Centre for Medium Range Weather Forecasts (ECMWF). The
H2O total column data used here are based on the ECMWF ERA-Interim
reanalysis data set (Dee et al., 2011a, b). ERA-Interim is the latest
global atmospheric reanalysis produced by ECMWF and provides a coherent
record of the global atmospheric evolution constrained by the observations
during the period of the reanalysis (1979–present). An advantage of using
reanalysis data for the comparison is that they provide a global view that
encompasses essential climate variables in a physically consistent framework.
The results are produced with a sequential data assimilation scheme, in which
available observations are combined with prior information from forecast
models, in order to estimate the evolving state of atmospheric water vapour.
Gridded data products include a large variety of three-hourly surface parameters,
describing weather as well as ocean-wave and land-surface conditions, and
six-hourly upper-air parameters covering the troposphere and stratosphere. The
accuracy of the data assimilation scheme, however, will depend on the quality
and availability of observations in the selected time frame. Large errors in
reanalysis products can originate from the lack of observations, changes in
the observing system and shortcomings in the assimilation model.
The improved atmospheric model and assimilation system used in ERA-Interim
significantly reduces several of the inaccuracies exhibited by the previous
ERA-40 reanalysis, such as too-strong precipitation over oceans from the
early 1990s onwards and a too-strong Brewer–Dobson circulation in the
stratosphere. Known key limitations of the ECMWF ERA-Interim data set are a
very intense water cycling (precipitation, evaporation) over the oceans and
positive biases in temperature and humidity (below 850 hPa) compared to
radiosondes in the Arctic.
In this study, we use model outputs between January 2007 and April 2014. We
combine the ECMWF ERA-Interim forecast 12 h values produced from forecasts
beginning at 00 and 12 coordinated universal time (UTC) to derive a daily
mean H2O total column. Forecast data are produced by the forecast model,
starting from an analysis, and are available at various forecast steps from
the analysis date and time. It is important to note that, since the SSM/I
and SSMIS temperature radiance observations have been assimilated into
ERA-Interim over ocean, the products are not completely independent from each
other.
The second data set is based on passive microwave observations from the
Special Sensor Microwave Imager Sounder (SSMIS) orbits of the F16 satellite.
These data are produced by the remote sensing system and sponsored by the
NASA earth science MEaSUREs DISCOVER projects (REMSS,
http://www.ssmi.com/ssmi). The series of seven Special Sensor Microwave/Imager
(SSM/I) have been in orbit since 1987 on various platforms, predominantly
those of the Defense Meteorological Satellite Programs (DMSP) F-platforms,
and now the SSM/I series has been replaced by a combined imager/sounder
called SSMIS. In this study, we use SSMIS measurements of the F16 polar
orbiting satellite between January 2007 and June 2014.
The SSMIS data products are generated using a unified algorithm to
simultaneously retrieve ocean wind speed, atmospheric water vapour, cloud
liquid water, and rain rate (Wentz, 1997). This algorithm is based on a
physical model for the brightness temperature of the ocean and intervening
atmosphere, and is the product of 20 years of refinements, improvements and
verifications. Radiative transfer theory provides the relationship between
the Earth's brightness temperature and the geophysical parameters (surface
temperature, near-surface wind speed and vertically integrated cloud liquid
water), which are used for the retrieval. TCWV data are available over ocean
only and rely on independent calibration against radiosonde (Wentz, 2013).
However, they also include TCWV for cloudy scenes, both day and night
overpasses and span a very large time range.
Top panel: global monthly mean bias between GOME-2/MetOp-A and three
independent TCWV data sets for the period January 2007–June 2014, depending
on availability of the data. The comparison is performed against ECMWF
ERA-Interim reanalysis (blue points), SSMIS F16 satellite (magenta points,
only over ocean) and the combined SSM/I+MERIS data set (green points). Coloured
squares and grey lines show the bias between the most recent GOME-2/MetOp-B
observations and the ECMWF ERA-Interim and SSMIS data sets. Bottom panel:
global monthly mean TCWV values for the GOME-2/MetOp-A and the GOME-2/MetOp-B
data sets. The time series are computed for all surfaces (global: land and
ocean together) and only for ocean measurements.
The third sample we analyse relies on the GlobVapour combined SSM/I + MERIS
TCWV Level 2 data set (Schröder et al., 2012b), derived within the ESA DUE
GlobVapour project. Both products were processed independently and combined
afterward to fit in daily and monthly files. The combined data set is based
on TCWV retrievals from measurements in the microwave range taken by SSM/I
(Fennig et al., 2012) over ocean, and measurements of the visible and near
infrared by MERIS over land and coastal regions, to provide a global
coverage. For the comparison with GOME-2 TCWV, we used gridded daily data,
which have a spatial resolution of 0.5∘×0.5∘ degrees, in the
period January 2007–December 2008 (SSM/I+MERIS products from the GlobVapour
project are available only for the time frame 2003–2008).
The MERIS algorithm (Lindstrot et al., 2011) retrieves TCWV amounts for cloud-free scenes for daytime overpasses over land with a very good spatial
resolution. As for GOME-2, the quality of the product is mainly determined by
uncertainties in cloud detection. Since MERIS retrieves data only during
daytime and at a fixed equator crossing time (10:00 a.m.), to provide a
consistent data set, the SSM/I products were created from morning overpasses
(descending path) of the F13 and F14 satellites. The DMSP F13 and F14
descending orbit cross the equator between 06:00 a.m. and 08:00 a.m. local time.
In the framework of the GlobVapour project, an improved version of the Hamburg Ocean Atmosphere
Parameters and Fluxes from Satellite Data (HOAPS) algorithm has been developed for the SSM/I TCWV
retrieval (Phalippou, 1996; Deblonde, 2001). It is important to note that the SSMIS TCWV measurements
from the REMSS are retrieved using a different algorithm. Finally, the bias between the SSM/I and
MERIS data sets has been assessed by comparing the results of both retrievals over sun-glint areas,
in order to assure a smooth transition from ocean to land and island sites (Schröder et al., 2012b).
Mean bias time series
All comparisons between GOME-2 TCWV measurements and the three data sets described above use the same gridding and filtering procedure in
order to reduce sampling related issues. Daily water vapour measurements are
first gridded on a regular 1.5∘×1.5∘ spatial grid. Then, daily
co-located data are used to compute the monthly mean bias between GOME-2 and
all the data sets analysed here (SSM/I+MERIS data are only available as
gridded monthly and daily mean). The comparisons are performed for GOME-2
H2O total columns which are not flagged as cloud-contaminated on the Level
2 data product. Pixels flagged as cloudy are also removed on a daily basis from the data sets selected for the comparison.
Figure (top panel) shows a time series of globally
averaged total bias of the TCWV distribution between GOME-2A and the
comparison data sets for the time period January 2007–June 2014. Since
January 2013 we have also computed the bias between the most recent GOME-2B
results and the ECMWF ERA-Interim and SSMIS retrievals. The inter-comparison
has been performed in such a way that positive and negative bias imply
respectively larger and lower GOME-2 data. The agreement between GOME-2 data
and the independent measurements considered here is very good for all
comparisons: the mean bias for the full time series is very close to 0,
while the RMSE varies between 0.3 and 0.4 g cm-2 (see Table ). The RMSE for the water vapour measurements is evaluated in
the following way:
RMSE=∑N[(ΩH2OGOME-2,0-ΩH2Ocomp,0)2]/N,
where (ΩH2OGOME-2,0-ΩH2Ocomp,0)2 is the difference
between the GOME-2 sensor and the data set used for the comparison in each
grid point. Because these deviations are squared before they are averaged,
the RMSE gives a relatively high weight to large deviations. This means that
the RMSE for the water vapour measurements is relatively high due to the high
water vapour natural variations. The uncertainty margins provided for the
bias and the RMSE statistics result from the spread of the bias and RMSE
values in the time series. Since the GOME-2B total column data are typically
larger than the GOME-2A data (see Sect. 4), the bias is also shifted
towards higher values in this case. In the bottom panel of Fig. , we report the monthly averaged TCWV values for the
GOME-2A and GOME-2B measurements in order to assist the interpretation of the
bias results. The time series are computed for the ocean data set only and
for all surfaces. We note that the H2O products exhibit a minimum
during the northern hemispheric winter and a maximum in the summer months and
that the TCWV values are typically larger over ocean surfaces.
Bias and RMSE statistics. The computations refer to the average
difference GOME-2 data. The time period analysed is January 2007–April 2014
for the comparison GOME-2A – ECMWF ERA-Interim, January 2007–June 2014 for
GOME-2A – SSMIS and January 2007–December 2008 for GOME-2A – SSM/I+MERIS.
We use GOME-2B data starting from January 2013.
As an exemplary time series, we further analyse the inter-comparison between
GOME-2A and SSMIS data (the magenta line and points in the top panel of Fig. ). More than six years overlap between GOME-2A and SSMIS
data provides a very good opportunity to investigate the seasonal dependence
of the results. In this case, the bias is high in the northern
hemisphere summer and low in the northern hemisphere winter,
with the averaged TCWV for SSMIS being slightly higher than GOME-2 (0.006 g cm-2, see Table ). The monthly averaged bias ranges from
-0.083 g cm-2 in January 2010 to 0.094 g cm-2 in July 2013. Since the
microwave instruments can also measure the water vapour below clouds, we
expect some residual difference between GOME-2 data (based on visible
observations, where cloud blocks the radiation) and SSMIS data, which also deliver
results in cloudy conditions. In Fig. , the global
monthly mean bias between GOME-2 and the three data sets is computed
separately for land (top panel) and for ocean surfaces (bottom panel). Large
seasonal variations in the distribution of the mean bias are also evident in
the SSM/I+MERIS and ECMWF ERA-Interim comparisons, when
analysing ocean surfaces alone. We can infer a seasonal cycle of the
geographic distribution of the bias, which is probably caused, among other
reasons, by the seasonality of cloud properties, as well as the variability of
the geographic distribution of major cloud structures as the Intertropical Convergence Zone (ITCZ).
For the SSM/I + MERIS data set (green line and points in the top panel of
Fig. ), the seasonal behaviour is not as evident as for
SSMIS, as a result of the different biases over land (MERIS) and sea (SSM/I).
In general, the MERIS measurements present a wet bias with respect to the
ECMWF ERA-Interim data over land, which might be partly caused by
spectroscopic uncertainties in the MERIS algorithm, such as the description
of the water vapour continuum (Lindstrot et al., 2012). When interpreting
these results, we should keep in mind the limitations of the GOME-2
retrieval. Although, as discussed before, a specific advantage of the visible
spectral region is that it is sensitive to the water vapour concentration
close to the surface and that it has almost the same sensitivity over land
and ocean, the accuracy of an individual observation is reduced for cloudy
sky observations. In addition, the GOME-2 observations, which are made at
09:30 LT, cannot be representative of the daily, and therefore monthly,
average H2O values in regions with a pronounced water vapour diurnal
cycle. When repeating the comparison for ECMWF ERA-Interim and SSMIS outputs
closest in time with GOME-2A measurements, differences in the mean bias of up
to 0.02 g cm-2 are found. However, the global distribution of the
affected areas is similar in both cases.
Global monthly mean bias between GOME-2/MetOp-A and three independent
TCWV data sets for the period January 2007–June 2014, depending on
availability of the data. The bias is computed separately for land (top
panel) and for ocean surfaces (bottom panel). Coloured squares and grey lines
show the bias for the GOME-2/MetOp-B data set.
Monthly mean maps of total column water vapour from GOME-2A (on the
left) and ECMWF ERA-Interim (on the right) co-located data for February 2008
(on the top) and August 2008 (on the bottom). Only cloud-screened data have
been used.
Finally, the ECMWF ERA-Interim data set (blue line and points in the top
panel of Fig. ) also shows a smaller oscillation around the
mean bias against GOME-2A measurements, because of the compensating effect of
having land and ocean retrievals. The amplitude of the winter–summer
oscillation is 0.07 g cm-2 at most. The global mean bias is slightly
positive (0.035 g cm-2) and very close to the SSM/I+MERIS result (mean
bias of 0.032 g cm-2). As for the SSMIS and SSM/I+MERIS comparison, we
studied co-locations in order to derive conservative estimates for the
precision of our water vapour retrieval. This is important to remove part of
the bias introduced by the presence of TCWV data retrieved in cloudy
conditions in microwave measurements and simulated data. As already discussed
in Sect. 5.1, for the comparison we used the ECMWF ERA-Interim 12 h
forecast based on 00:00 and 12:00 UTC analysis in order to have a more independent
data set, since they include modelling. However, we have redone the same
comparison using the analysis data set and obtained similar results
(slightly larger bias, 0.039 g cm-2 instead of 0.035 g cm-2).
In order to interpret these results and to assess the observed biases and
seasonal cycle, in the following sections we further discuss the method used and show the global distribution of the bias between GOME-2A and the
three independent data sets for two exemplary months (February and August
2008).
Comparison with ECMWF ERA-Interim TCWV model data
The top plots of Fig. present the monthly mean TCWV product
in February 2008 obtained from daily co-locations of ECMWF ERA-Interim and
GOME-2A data. We choose this month as representative of the water vapour
distribution in the northern hemisphere winter season. In the bottom plots of
Fig. , one can see the corresponding ECMWF ERA-Interim and
GOME-2A measurements in August 2008. In all panels, we can observe a high
humidity in the tropics and low humidity at higher latitudes. Also, the
movement of the Intertropical Convergence Zone with seasons is clearly
visible from the shift of the high TCWV values in the tropics between
February and August 2008. In both hemispheres, the TCWV distribution follows
the seasonal cycle of the near surface temperature: the H2O total column
has a maximum during the northern hemisphere summer, and a minimum in winter.
Looking at the monthly mean differences between GOME-2A and ECMWF
ERA-Interim, we can distinguish only a few regions with obvious discrepancies,
e.g. the Amazon Basin and Central Africa in February 2008, or Southeast
Asia in August 2008. Overall, we find similar spatial patterns in the H2O
distribution in the ECMWF ERA-Interim and GOME-2A data sets. These results
confirm that the GOME-2 retrievals capture the overall spatial variability in
the H2O total column values quite well both over ocean and land surfaces.
In order to quantify the discrepancies between ECMWF ERA-Interim data and
GOME-2A TCWV retrieval, in Fig. we show the spatial
distribution of the bias for co-located and (mostly) cloud-free measurements.
The mean bias between the two data sets is 0.017 g cm-2 in February and
0.044 g cm-2 in August 2008.
Geographical distribution of the differences between GOME-2A and
ECMWF ERA-Interim total column water vapour in February 2008 (top panel) and
August 2008 (bottom panel). Only cloud-screened co-located data have been
used.
Geographical distribution of the differences between GOME-2A and
SSMIS total column water vapour in February 2008 (top panel) and August 2008
(bottom panel). Only cloud-screened co-located data have been
used.
In February, the bias is overall very low. Any deviation below the typical
scatter of water vapour data of 0.4 g cm-2 (i.e. the light red and light
blue areas in the plot) can be considered as a good agreement. GOME-2
exhibits a number of dry and wet spots in southern Africa and the South American
Amazonian regions, not visible in the ECMWF ERA-Interim product, which are
probably related to the very low number of co-locations in these regions due
to cloud screening, typically less than eight measurements. Also, the problems of
the ECMWF ERA-Interim data cannot be excluded, since remote regions may
present larger errors due to paucity of observational information in the
reanalyses, such as shown in Dee and Uppala (2009) for locations at latitudes
greater than 70∘ north. The differences over ocean, e.g. along the
ITCZ and the Pacific Warm Pool region, on the other hand, might be caused by
the rather high cloud tops in these regions, leading to low measured AMF and
consequently to rather high H2O total columns. Even though we consider
only grid boxes without severe cloud cover on a daily basis, some cloud
effects are still present.
Relative large differences between GOME-2A and ECMWF ERA-Interim data can be
seen in August 2008. For example, in summer 2008, the humidity in Central
Africa is much lower in the GOME-2 data than that estimated in the ECMWF
ERA-Interim data (absolute and relative differences larger than -1 g cm-2
and 20%, respectively). A negative bias can be observed in the region from India to the east coast of China and reaches values between
-1.5 and -2.1 g cm-2 in the northern part of the Indian
Subcontinent. Looking at the lower panel of Fig. , we
note that the underestimation (blue regions denote negative bias) is
located in land areas with a very high humidity in the northern hemisphere
summer months. From a correlation analysis, we found that the bias between
GOME-2A and ECMWF ERA-Interim data over land areas decreases (larger negative
values) with increasing humidity. This is consistent with the results of the
validation against ground-based measurements (Kalakoski et al., 2014).
Dry bias is also observed in arid areas, such as southern regions of the Sahara
desert, the coast of Somalia, the Arabian Desert in the Arabian Peninsula and
the Thar desert in the north-western part of the Indian Subcontinent. Regions
with relatively high surface albedo values (in the range 0.3–0.5) which
present dry bias include northern Africa, the Arabian Peninsula, India and
parts of East Asia and Central America. A possible explanation for the
discrepancies is that, because of absorbing aerosols over deserts, the
surface albedo we measure there is lower than the real value and, therefore,
we underestimate the water vapour content (Fournier et al., 2006). In the
future, we plan to further study the effect of the surface albedo database on
the water vapour retrieval and refine this choice. However, we should keep in
mind that the determination of the “real” surface albedo over desert regions
is still a field of discussion, because of the uplifting of large amounts of
dust, which lower the reflectivity (Herman et al., 1997; Torres et al.,
1998). Finally, we observe a larger scatter in northern latitude ocean areas.
The atmospheric transport or motion coupled to strong spatial gradients is
one of the possible origins of this bias.
Comparison with SSMIS TCWV observations
Geographical distribution of the differences between GOME-2A and the
combined SSM/I+MERIS total column water vapour data set in February 2008
(top panel) and August 2008 (bottom panel). Only cloud-screened co-located
data have been used.
Figure shows the global monthly bias between GOME-2A and
SSMIS observations in February and August 2008. The land regions are masked
in the comparison, because the SSMIS data set is available only over ocean
scenes, but microwave sensors can also retrieve TCWV in the presence of
clouds and for nighttime satellite overpasses. We used outputs from the
ascending and descending F16 orbit from the daily binary SSMIS data files in
order to compute gridded daily mean data used for co-locations. Ascending
local equator crossing time is 16:39 LT as of 16 October 2014, and descending
time 04:39 LT. If we evaluate the bias between GOME-2 and SSMIS from monthly
mean data, we would find a larger and negative bias because of the cloud
influence. Thus, as for ECMWF ERA-Interim data, we select only daily
co-locations and we reject the SSMIS data if the corresponding GOME-2
measurement is contaminated by clouds (applying the cloud flag selection
described in Sect. 3.1). This selection minimizes the effect of temporal
change and cloud contamination in the GOME-2 vs. SSMIS comparison. The number
of co-locations is further reduced since the TCWV retrieval is not possible
in situations with high precipitation or near land areas (<25 km).
In the data from February 2008, a small negative mean bias between GOME-2A
and SSMIS (-0.041 g cm-2) is derived (see Fig. ).
Looking at the top panel of Fig. , we observe very small
discrepancies for most ocean regions, with the exception of some coastal
areas, where the bias reaches values on the order of ±0.5 g cm-2. We
retrieve a larger mean bias of about 0.074 g cm-2 in August 2008 (bottom
panel of Fig. ). A large positive bias is clearly
visible in regions at high latitude, in particular the northern areas of the
Atlantic and Pacific Ocean (bias values typically between 0.5 and 0.9 g cm-2) and is the dominating cause for the pronounced seasonal component
in the SSMIS against GOME-2A comparison results. These differences were also
observed in the comparison with the ECMWF ERA-Interim data set (see Fig. ) and are thus likely related to the GOME-2 measurements.
Analysing the cloud parameters retrieved by GOME-2A for daily co-located
measurements, we found that larger biases are typically associated with
higher cloud fractions (>0.5). No clear dependence of the bias on the cloud
top height parameter is found, in contrast to the validation between
radiosonde and SCIAMACHY data retrieved with the AMC-DOAS algorithm (du
Piesanie et al., 2013).
Among the limitations of the SSMIS data, we should mention that the model and
algorithm for the retrieval are calibrated using an in situ database
containing overpasses of buoys and radiosonde sites. The accuracy of the TCWV
product depends on the quality of these observations, and not all the regions
and atmospheric situations may be equally represented in the training data
set (Andersson et al., 2010). It was already shown that the maximum bias
between satellite and ship data (of about 0.25 g kg-1; average bias of
approximately 2%) was found precisely over the North Atlantic Ocean during
the summer season (Bentamy et al., 2003). Also, depending on location and
season, systematic differences of atmospheric humidity of about 1% for 1 h time difference between the GOME-2A and SSMIS retrieval might be
expected (Kalakoski et al., 2011), and in regions with a particularly high
diurnal variability, as for instance over the North Atlantic, they can be
even larger.
Comparison with the SSM/I+MERIS TCWV data set
The comparison of the GOME-2 product with the combined SSM/I+MERIS GlobVapour
data set for February and August 2008 is shown in Fig. .
The agreement between GlobVapour data and GOME-2 measurements seems to be
somewhat better over land than over ocean. The difference plot in February
2008 (top panel) is quite noisy and the GOME-2 data over ocean tend to be
lower than the corresponding SSM/I+MERIS monthly mean. This is in line with
the results we obtain from the comparison with SSMIS data for the same month.
An interesting ocean area is the one west of Central America and Colombia, and
the coast of Africa, where we have positive differences, not seen in the ECMWF
ERA-Interim comparison (Sect. 5.3), and associated with higher cloud-top
albedo values.
Over the continents, the agreement between both data sets is generally very
good, as seen in the comparison with ECMWF ERA-Interim data. A specific
advantage of the MERIS instrument is the very high spatial resolution (1×1.2 km2 in the reduced resolution mode) and therefore the ability
to retrieve sharp gradients in water vapour abundance with great accuracy. We
can observe extended regions with very small biases, close to zero,
especially in Asia and Africa. Exceptions are found in some specific small
areas where GOME-2 columns are higher than the MERIS values. A slight
overestimation of water vapour content by GOME-2 (or underestimation by
SSM/I + MERIS) seems to occur preferably over Europe and the western part of
North America. Major differences are located in coastal areas, where neither
SSM/I, nor MERIS provide accurate estimates. For MERIS, this is due to the
weak reflectance of the ocean in the near infrared and on the resulting
uncertainties introduced by the unknown contribution of aerosol scattering
and absorption, while SSM/I measurements cannot be used in case of relative
large footprint contaminated by land. Significant differences over European and
North American coasts (e.g. in the southern part of Sweden, along the coasts
of the Baltic Sea) are not seen in the comparison with ECMWF ERA-Interim
data. Thus, it is not clear whether the discrepancies observed at high
latitudes result from difficulties with the retrieval over ice-covered
regions (Schröder et al., 2012d). Finally, as for GOME-2, the quality of
the MERIS TCWV retrieval algorithm strongly depends on the reliability of the
cloud screening procedure, and we can expect a weak dry bias where the cloud
detections fail.
The average bias between GOME-2A and SSM/I+MERIS in February 2008 is 0.02 g cm-2, while we found a slightly larger positive bias (0.03 g cm-2) in
August 2008. As shown in Fig. , for this data set we
can observe a systematic variation in the bias between winter and summer
months over land and ocean. The same effects was also observed in Schröder
et al. (2012c), when comparing the SSM/I+MERIS GlobVapour product with the
homogenized GOME/SCIAMACHY/GOME-2 time series. In the northern hemisphere
winter months, mostly negative bias over sea and positive bias over land is
observed. In the northern hemisphere summer months (see bottom panel of Fig. ), on the other hand, the MERIS data tend to be more wet
than the corresponding GOME-2A data, with a large bias (between -0.4 and
-2.2 g cm-2) in Southeast Asia, Central Africa and part of
Saudi Arabia and North America. In the aforementioned comparison by
Schröder et al. (2012c), dry bias features located over northern Africa,
part of the Arabian Peninsula and the north-western part of the Indian
Subcontinent were observed in July 2006 and July 2007. Similar patterns were
also reported in the comparison with the ECMWF ERA-Interim data set, hinting
at problems in the GOME-2 data. About 7.5% of the grid boxes present a
bias larger than 0.5 g cm-2 (only 4.4% in the comparison with ECMWF
ERA-Interim). The discrepancies are inversely correlated with GOME-2A regions
with high surface albedo (0.3–0.5) or high humidity values. In previous
studies (Lindstrot at al., 2012), a potential underestimation of the
absorption at 900 nm was identified as a possible source of a wet bias in the
MERIS data set.
An orthogonal regression analysis of the scatter between GOME-2 and
SSM/I+MERIS monthly mean measurements (as opposed to co-located data sets
presented before) showed a good correlation between both data sets. We found
an almost ideal slope of 0.981 and 1.006 in February and August 2008,
respectively. Also, the offset is very small, especially for the summer
comparison (-4× 10-4 g cm-2). Although the majority of data shows very
good correlation, SSM/I+MERIS mid-value water columns (i.e. 1–3 g cm-2) are often lower than the GOME co-located products. The average mean bias for February in this case is negative (-0.021 g cm-2). Since
microwave instruments can also retrieve the water vapour in cloudy
conditions, comparing the GOME-2 measurements with the SSM/I on a monthly
base, means also using SSM/I observations with large cloud cover. If we do a
daily co-location, on the other hand, the results of the two satellites are
closer, because in this case we reject all SSM/I measurements in regions
flagged as cloudy by the GOME-2 instruments. In August 2008, the largest
scatter occurs for values around 2 g cm-2, which are observed in the
transition zone between tropics and extra-tropics, where large natural
variability is observed.
Summary and conclusions
In this paper, we present an algorithm for the retrieval of water vapour
total columns from the Global Ozone Monitoring Experiment-2 (GOME-2) on board
the MetOp-A and MetOp-B platforms, and we perform an analysis and
evaluation of this data set against independent satellite observations and
the latest ECMWF ERA-Interim reanalysis data.
The operational GOME-2 TCWV product used in this study has been developed in
the framework of EUMETSAT's O3M-SAF project in co-operation with MPI-C
Mainz and DLR Oberpfaffenhofen, and generated using the UPAS environment and
the GDP 4.7 algorithm. The retrieval algorithm is based on a classical DOAS
method to obtain the trace gas slant column. Subsequently, the H2O total
column is derived, making use of the simultaneously measured O2 absorption
and radiative transfer calculations. This procedure is robust (it provides
similar sensitivity over land and ocean), very fast and, in contrast to other
satellite retrieval methods (as from TOVS, from SSM/I and SSMIS microwave
observations and from GPS TCWV measurements), is independent from a priori
assumptions on atmospheric properties.
In GDP 4.7, the quality of the GOME-2 H2O total column has been enhanced
with respect to two major aspects: we improve the cloud selection criteria
used in the retrieval algorithm, and we eliminate the dependency of the data
set on the viewing angle conditions by applying a distinct empirical
correction for land and ocean surfaces, both to GOME-2A and GOME-2B
measurements. We present exemplary results from about one and a half year
measurements of the new GOME-2B instrument, launched on 17 October 2012, and
an inter-comparison with the GOME-2A data for the overlap period. We found
that the GOME-2B water vapour total columns are only slightly wetter than the
GOME-2A measurements and present a small, positive bias of about 0.006 g cm-2 (less than 1%), when averaging all the results from December 2012
to June 2014. Latitudinal averaged differences can be as large as 0.117 g cm-2 at low latitudes, since the orbits of the GOME-2A and the GOME-2B
sensors have the smallest overlap in the tropical regions.
TCWV estimates from the GOME-2A and GOME-2B instruments are collocated and
compared with SSMIS satellite F16 measurements and with ECMWF ERA-Interim
model data during the full period January 2007–June 2014. Comparisons
against a combined SSM/I + MERIS data set (as developed in the framework of
the ESA DUE GlobVapour project) in 2007 and 2008 conclude our analysis.
Within our study, a surprisingly good agreement between GOME-2 type
instruments and the three independent data sets analysed here is found, with
a mean bias within ±0.035 g cm-2 for the time interval January
2007–June 2014. As a reference value, the bias obtained by Kalakoski et al. (2011),
comparing the GOME-2 TCWV data produced using an earlier algorithm version
(GDP 4.5) with SSM/I data, was typically between 0.17 and 0.25 g cm-2
for monthly global averages. While the annual variability over land and
coastal areas is low, over ocean we observe a clear seasonal cycle with the
highest values during the northern hemisphere summer. Slightly lower than in
summer, and negative biases are found in the northern hemisphere winter
months. These variations can mainly be related to the impact of clouds on the
accuracy of the GOME-2 observations and to the different sampling statistics
of the instruments.
Collocated GOME-2A data present a mean bias of 0.017 g cm-2 (0.4%) and
0.044 (1.1%) with TCWV data from ECMWF ERA-Interim in February and
August 2008, respectively. In August 2008, the comparison between the GOME
observations and the SSMIS F16 satellite measurements yields an average bias
of 0.074 g cm-2, and the differences in TCWV measured by the two systems is
possibly dominated by residual cloud effects and the diurnal variability of
the water vapour data over the North Atlantic Ocean. Global monthly averaged
differences between the combined SSM/I+MERIS data sets and GOME-2 data are
distributed between 0.0 and 0.05 g cm-2. GOME-2A data are typically
drier than MERIS data over land areas with high humidity or a relatively
large surface albedo (bias values between -0.4 and -2.2 g cm-2), a
circumstance which may indicate an influence of the surface albedo correction
in the AMF calculation. Finally, GOME-2B measurements are in general biased
high compared to the other water vapour data set. However, this discrepancy
might be corrected to first order based on the results of the comparison with
the GOME-2A data.
Recently, Kalakoski et al. (2014) performed a global validation of the
GOME-2A and GOME-2B TCWV product presented in this study using radiosonde
data from the IGRA archive and GPS data from the COSMIC/SuomiNet network.
Overall, they found a good general agreement between GOME-2 and ground-based
measurements. In their study, they observed small dry median differences
against radiosondes (GOME-2A: -2.7%, GOME-2B: -0.3%) and small wet
median differences against GPS data (GOME-2A: 4.9%, GOME-2B: 3.2%). Dry
bias was observed especially over land in the northern hemisphere
(co-locations over northern Africa and India showed generally a negative
bias), while wet bias was found preeminently over ocean and in coastal areas.
Consistent with our results, they remarked that pronounced negative biases
are correlated with high H2O values (>5 g cm-2) and with high surface
albedo (>0.3).
GOME-2/MetOp-A and GOME-2/MetOp-B TCWV obtained with the GDP 4.7 algorithm
continues the GOME and SCIAMACHY time series started in 1995. With the launch
of the new GOME-2/MetOp-C instrument in 2018, the GOME-type data record will
be further extended to cover a period of at least 25 years of water vapour
measurements. This unique data set has now reached high accuracy and
stability and is expected to provide important information on long-term
changes in our atmosphere.
Acknowledgements
Development of the GOME-2 water vapour products and their validation has been
funded by the O3M-SAF project with EUMETSAT and national contributions. The
authors thank the DFD colleagues S. Kiemle, K. H. Seitz, T. Padsuren and M. Schwinger, who are responsible for day-to-day operations of the O3M-SAF
facility at DLR. We thank EUMETSAT for the ground segment interfacing work
with the O3M-SAF systems and for the provision of GOME-2 Level 1 products.
The service charges for this open-access publication
have been covered by a research centre of the Helmholtz Association.
Edited by: P. Stammes
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