The main goal of this paper is to validate the total water vapour column
(TWVC) measured by the Global Ozone Monitoring Experiment-2 (GOME-2)
satellite sensor and generated using the GOME Data Processor (GDP) retrieval
algorithm developed by the German Aerospace Centre (DLR). For this purpose,
spatially and temporally collocated TWVC data from highly accurate sounding
measurements for the period January 2009–May 2014 at six sites are used.
These balloon-borne data are provided by the GCOS Reference Upper-Air Network
(GRUAN). The correlation between GOME-2 and sounding TWVC data is reasonably
good (determination coefficient,
Atmospheric water vapour is a key component for weather and the climate system because it plays a vital role in the formation of clouds and precipitation, the growth of aerosols and significantly contributes to the energy balance of the Earth when acting as a powerful greenhouse gas. Unlike most trace gases, the atmospheric water vapour exhibits a highly variable spatial and temporal distribution. Hence, close monitoring of its variability and long-term changes is a critical issue for the scientific community (e.g. Hartmann et al., 2013).
Remote sensing instruments aboard satellite platforms provide an effective way to monitor the geographical and temporal distribution of the column-integrated amount of atmospheric water vapour, called total water vapour column (TWVC), thanks to their global coverage, high spatial resolution and accurate observations (e.g. Kaufman and Gao, 1992; Bauer and Schlüssel, 1993; Noël et al., 1999, 2004; Maurellis et al., 2000; Wagner et al., 2006; Li et al., 2006; Deeter, 2007; Lang et al., 2007; Mieruch et al., 2008; Pougatchev et al., 2009). Within this framework, the European satellite-borne atmospheric sensor Global Ozone Monitoring Experiment 2 (GOME-2) aboard the Meteorological Operational satellite program (MetOp-A and MetOp-B) provides the potential for a detailed analysis of the global distribution of the atmospheric water vapour (Grossi et al., 2014). MetOp-A and MetOp-B were launched in 2006 and 2012, respectively, belonging to a series of three similar meteorological satellites from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) (MetOp-C is expected to be in orbit in 2018). The main objective of MetOp missions is to provide continuous and long-term observations of the most important trace gases, supporting operational meteorology, global weather forecasting and climate monitoring (Edwards et al., 2006). The three MetOp satellites will guarantee continuous TWVC time series using the same sensor (GOME-2) to at least the first half of the 2020s.
To assure the quality and accuracy of the operational TWVC data derived from satellite observations, validation exercises using independent measurements recorded by reference instruments are required. Among them, the atmospheric sounding through weather balloons equipped with pressure, temperature and humidity sensors is an essential technique to monitor the TWVC changes under all weather conditions (e.g. Ross and Elliott, 2001; Durre et al., 2009). Nevertheless, it is well known that radiosonde humidity records can contain sensor-dependent errors that vary notably over time and space (e.g. Vömel et al., 2007; Wang and Zhang, 2008; Dai et al., 2011). Therefore, the balloon-borne data used as reference in the validation of satellite observations must be generated by high-quality networks with identical instrumentation and a common mode of operation. For instance, the GCOS Reference Upper-Air Network (GRUAN) provides highly accurate sounding measurements complemented by ground-based instruments for the study of atmospheric processes (Seidel et al., 2009; Immler et al., 2010). GRUAN has developed a high-quality data product based on measurements of temperature, humidity, wind and pressure by the Vaisala RS92 radiosonde (Immler and Sommer, 2011; Dirksen et al., 2014).
This paper focuses on the validation of the TWVC data measured by the GOME-2/MetOp-A satellite instrument using as reference the balloon-borne data recorded between January 2009 and May 2014 from six GRUAN stations. In this satellite validation, we use the TWVC data inferred from the GOME Data Processor (GDP) retrieval algorithm (versions 4.6 and 4.7) generated by the German Aerospace Center, Remote Sensing Technology Institute (DLR-IMF) in the framework of the EUMETSAT Satellite Application Facility on Atmospheric Chemistry Monitoring (O3M SAF) (Valks et al., 2011). This retrieval algorithm is based on the classical Differential Optical Absorption Spectroscopy (DOAS) technique (Platt, 1994). Although some validation exercises of GOME-2 TWVC data have been separately carried out before (e.g. Kalakoski et al., 2011, 2014; Schröder and Schneider, 2012; Grossi et al., 2013, 2014), the present study should be considered as complementary since it works with a homogeneous high-quality data sets as reference (RS92 GRUAN Data Product, RS92-GDP) and with a focus on the analysis of the effects of cloudiness and geometrical properties that have not been studied in detail up to now. It is therefore expected that this paper will improve the understanding of the quality of the GOME-2 TWVC data retrieved by the GDP retrieval algorithm.
The satellite and sounding data employed in this paper are described in Sect. 2. Section 3 explains the methodology applied in the validation. The results obtained are presented and discussed in Sect. 4 and, finally, the conclusions are summarized in Sect. 5.
The GOME-2 is an across-track scanning nadir-viewing (from about 240 to
790 nm) spectrometer launched on board EUMETSAT MetOp-A in October 2006
(Munro et al., 2006). This satellite instrument is an enhanced version of its
antecessor GOME/ERS-2 launched in 1995 (Burrows at al., 1999) with an
improved temporal coverage (near daily global coverage at the equator) thanks
to a spatial resolution of 80 km
The operational algorithms for the retrieval of TWVC data from the
GOME-2/MetOp-A is the level-1-to-2 GOME Data Processor (GDP) (versions 4.6
and 4.7), integrated into the Universal Processor for Atmospheric
Spectrometers (UPAS, version 1.3.9) processing system at DLR-IMF. A detailed
description of GDP can be found in the Algorithm Theoretical Basis Document
of Valks et al. (2011) and in the work of Grossi et al. (2014). Here a brief
description is presented. In a first step, this algorithm retrieved the water
vapour slant column density (SCD) by means of the DOAS methodology applied in
the spectral range 614.0–683.2 nm. In a second step, correction factors
derived from numerical simulations are applied to the SCD values in order to
remove the absorption non-linearity effect which is related to the highly
fine structured water vapour (and O
According to Valks et al. (2011) and Grossi et al. (2014), the error budget in the TWVC retrieved from GOME-2 can be separated in errors affecting the retrieval of the slant columns (DOAS-related errors), and errors affecting the conversion of the slant into vertical column (AMF-related errors). Additionally, the general error contribution (e.g. cross-section, temperature, Ring effect, etc.) is taken as a constant of 10 %. Hence, the uncertainty of the GOME-2 TWVC data is elevated. GDP 4.6–4.7 provides an estimated error (1 standard deviation) for each satellite TWVC data.
The reference balloon-borne data used in this work to validate the GOME-2 TWVC observations are taken from the GRUAN network which aims to provide traceable measurements of atmospheric profiles for a detailed characterization of essential climate variables (e.g. pressure, temperature, water vapour) over a long-term period (Seidel et al., 2009; Immler et al., 2010).
The GRUAN data product (RS92-GDP) currently available is based on balloon-borne measurements using Vaisala RS92 sondes (Immler and Sommer, 2011; Dirksen et al., 2014). This instrument is equipped with a wire-like capacitive temperature sensor (“Thermocap”), two polymer capacitive moisture sensors (“Humicap”), a silicon-based pressure sensor, and a GPS receiver to measure position, altitude and winds. The RS92 transmits recorded data at 1 sec intervals, being received, processed and stored by the DigiCora ground-based station equipment. The “Humicap” sensors are used to measure the relative humidity and they consist of a thin hydrophilic polymer layer on a glass substrate which acts as the dielectric of a capacitor. These two humidity sensors are alternately measuring and being heated, thus removing coating of the sensor by ice or liquid inside clouds.
The RS92 radiosonde has a proven quality exhibiting the smallest systematic and random errors among the diverse types of radiosonde sensors (e.g. Miloshevich et al., 2006; Moradi et al., 2013). The high quality of this setup has enabled its participation in radiosonde inter-comparison campaigns under the auspices of the World Meteorological Organization (WMO) (e.g. Nash et al., 2011) and also its use in numerous inter-comparison exercises against both ground-based setups (e.g. Schneider et al., 2010; Buehler et al., 2012; Pérez-Ramírez et al., 2014) and satellite-based instruments (e.g. du Piesanie et al., 2013; Diedrich et al., 2015). Nevertheless, there are several error sources of RS92 measurements which may limit their quality, such as the solar radiation error related to the solar heating of the humidity sensor (see Miloshevich et al., 2009, and references within). Hence, the added value of the GRUAN product is associated with the implementation of an exhaustive data processing method including corrections for the different error sources which guarantees high quality sounding measurements (Immler and Sommer, 2011; Dirksen et al., 2014).
GRUAN stations with available sounding data within 100 km and 120 min GOME-2 overpass.
RS92-GDP provides vertical profiles of temperature (temp), relative humidity (rh), pressure (press), altitude, geopotential height and wind, together with other variables such as the water vapour volume mixing ratio derived from rh, temp and press. The TWVC used in this work is obtained by integrating vertical profiles of water vapour volume mixing ratio. Additionally, RS92-GDP is the first data set of balloon-borne measurements that provides vertically resolved uncertainty estimates which include, for humidity data, uncertainties in calibration, radiation correction, time lag correction and sonde preparation (Immler and Sommer, 2010; Dirksen et al., 2014).
To provide an uncertainty for the TWVC data used in our study, we use the “correlated uncertainty” (u_cor_rh) of the rh data provided by the RS92-GDP, which represents 1 sigma (i.e. 1 standard deviation from the mean). For each profile, a relative error associated with the corresponding TWVC is obtained as the weighted average of the ratio of u_ cor_rh to rh based on the contribution of each layer to the TWVC. The mean value of the relative errors determined for all sounding analysed in this work is 3.5 %, which evidences the high quality of the reference GRUAN TWVC data.
RS92-GDP is stored in NetCDF format, and processed soundings that have passed
quality control are freely disseminated through
In this work, two co-location criteria are followed to select TWVC data for
inter-comparison purposes. Firstly, the GOME-2 data are selected such that
the distance between the centre of the satellite pixel and the location of
the GRUAN station is always less than 100 km. Nevertheless, most cases
are substantially below this figure, the 50, 75 and 90 percentiles being
18, 29 and 51 km, respectively. The mean distance of all selected GOME-2
overpasses is 27 km. The second criterion is related to the measured time –
only those radiosondes with a difference between their launch
time and the satellite overpass time smaller than 120 min being selected. Additionally, all
GOME-2 TWVC data used in this work correspond to those cases which are not
flagged by the GDP 4.6–4.7 retrieval algorithms as contaminated by heavy
clouds (large fraction of pixel covered by clouds and, simultaneously, with
high cloud albedo). Thus, the “H2O flag” is set when cloud
albedo
RS92-GDP is currently available only for 14 GRUAN stations. Applying the two
co-location criteria and the “H2O flag”, a total of 1400 soundings of six
GRUAN stations (Table 1) were used to be compared against GOME-2 TWVC data
throughout the period 2009–2014. Detailed information about these six
stations can be found at
A linear regression analysis is performed between the TWVC values measured
by the radiosonde and those observed by the satellite instrument. Regression
coefficients, coefficients of determination (
Finally, the uncertainty (
First, a linear regression analysis between the GRUAN and GOME-2 TWVC data is
performed for each GRUAN station and for all stations together in order to
analyse their proportionality and similarity. Statistical parameters (the
slope of the regression,
Parameters obtained in the correlation analysis between GOME-2 TWVC
data and GRUAN radiosonding measurements during the period 2009–2014. Upper
(lower) rows show the parameters obtained for all-sky (cloud-free)
conditions. The parameters are:
Although a satellite “H2O flag” has been used to select those GOME-2 TWVC
data not contaminated by heavy cloudy conditions, the remaining cloudy cases
can introduce a notable bias in the inter-comparison between satellite and
sounding TWVC data. Thus, a correlation analysis between GOME-2 and GRUAN
data has been performed only for those cases with satellite cloud fraction
(CF) smaller than 5 % (called cloud-free cases). These cloud-free cases
represent about 39 % of all cases. The average (
Table 2 also reports the uncertainty of the weighted averages (Eq. 5) for
each station and all data derived from a combined uncertainty using the
estimated errors of the sounding and satellite TWVC data. The weight of the
GOME-2 estimated errors in these combined uncertainties is larger than
99 %. The large estimated errors of satellite data produce high
uncertainty values of the relative differences (
Figure 1 shows the relationship between satellite and sounding TWVC data for
all-sky conditions (top plot), revealing a good agreement in the correlation
but with a high degree of spread (RMSE
GRUAN TWVC against GOME-2 TWVC data for all-sky conditions (top plot) and cloud-free conditions (bottom plot). The solid line represents the unit slope to which the data comply.
The weighted mean relative differences between sounding and GOME-2 TWVC data
(Eq. 3) as a function of satellite ground pixel solar zenith angle (SZA) are
shown in Fig. 2 using 5
Differences between TWVC data retrieved by GOME-2 and GRUAN sounding data (Eq. 3) as function of the GOME-2 ground pixel solar zenith angle (SZA) for all, cloud-free and cloudy conditions.
The dependence on satellite SZA found for both cloud-free and cloudy data
sets is currently under investigation and could be due to some calibration
issues in the level 1B (calibrated radiances) satellite products. Another
possible error source for this SZA dependence may be related to the
correction factor applied to obtain the AMF of the water vapour, which is
derived from the measured AMF of O
The significant SZA dependence found on the GOME-2 TWVC data leads to a systematic seasonal dependence with respect to reference balloon-borne measurements which is shown in Fig. 3 for five out of six studied sites. This plot shows the evolution of the monthly averages of the weighted mean relative differences (Eq. 3). These monthly averages are determined only for those months with more than 10 available pairs of sounding-satellite data. It can be seen that the satellite observations remarkably underestimate the sounding data in spring–summer months, while this underestimation clearly decreases (even in some stations turns to overestimation) for the autumn–winter months.
Monthly averages of the differences between TWVC data retrieved by GOME-2 and GRUAN sounding data (Eq. 3) for five out of six GRUAN sites used in this study.
The satellite view zenith angle (VZA) also known as satellite scan angle is
another relevant geometry parameter. GOME-2/MetOp-A measured 24 scenes along
the ground swath, one for each satellite VZA stepping at 5
Differences between TWVC data retrieved by GOME-2 and GRUAN sounding
data (Eq. 3) as function of satellite view zenith angle (VZA) for cases with
SZA below 50
Cloudiness represents the most relevant atmospheric factor that can substantially decrease the accuracy of the trace gas column retrievals from satellite instruments (e.g. Koelemeijer and Stammes, 1999; Kokhanovsky et al., 2007; Antón and Loyola, 2011; du Piesanie et al., 2013). The previous section has shown the strong effect of the cloudy cases on the GOME-2–GRUAN inter-comparison. Therefore, it is highly interesting to study the effects of the cloud parameters (CF, cloud top albedo (CTA) and cloud top pressure (CTP)) in the satellite-sounding differences. While the GOME-2 CF is retrieved by the OCRA algorithm using broadband radiance measurements in the UV–visible range, GOME-2 CTP and CTA are both derived from the ROCINN algorithm using the spectral information in and around the oxygen-A band (Loyola et al., 2007). The GDP 4.6 and 4.7 also use improved cloud retrieval algorithms including detection of sun glint effects (Loyola et al., 2011).
Figure 4 showed a slight VZA dependence of the sounding-satellite differences
for the outermost west satellite pixels using GOME-2 data from GDP 4.6. Thus,
to minimize this dependence, the GDP 4.6 data with VZA higher than
The significant influence of cloudiness in the weighted relative differences
between satellite and balloon-borne TWVC measurements (Eq. 3) is confirmed by
Figs. 5–7 which show the remarkable dependence of these differences
with the satellite CF, CTA and CTP, respectively. Each plot exhibits three
curves corresponding to all cases (in black), and those cases with satellite
SZA
Differences between TWVC data retrieved by GOME-2 and GRUAN sounding
data (Eq. 3) as function of satellite cloud fraction for all cases, and those
with satellite SZA
The relative differences as a function of satellite CF are shown in Fig. 5
using bins of 10 %. It can be seen that the underestimation rises with
increasing CF up to a cloud coverage percentage of 20–30 %, showing a
more stable behaviour for the rest of higher CF values with relative
differences between
Differences between TWVC data retrieved by GOME-2 and GRUAN sounding
data (Eq. 3) as function of satellite cloud top albedo for all cloudy cases
(CF
The strong influence of cloudiness in the satellite TWVC retrieval is mainly associated with the so-called shielding effect as a result of which the amount of water vapour below clouds is hidden by them (Kokhanovsky and Rozanov, 2008). As most the water vapour is found in the troposphere, increasing its volume mixing ratio towards the surface, a large impact of the shielding effect on the satellite TWVC retrievals is expected (Mieruch et al., 2008, 2010). Thus, some retrieval algorithms make use of a cloud correction method to take into account the water vapour present below the clouds – e.g. the AMC-DOAS (Air Mass Corrected Differential Absorption Spectroscopy) method used to retrieve TWVC from SCIAMACHY (Scanning and Imaging Absorption Spectrometer for Atmospheric Chartography) measurements in the visible spectral range (Noël et al., 2004). Du Piesanie et al. (2013) checked this correction method by means of a detailed analysis of the sounding-satellite differences as a function of cloud fraction, cloud optical thickness and cloud top height. They found no significant dependencies with the former two cloud properties, but found a strong dependence when investigating the bias as a function of cloud top height.
Differences between TWVC data retrieved by GOME-2 and GRUAN sounding
data (Eq. 3) as function of satellite cloud pressure for all cloudy cases
(CF
Although the GDP retrieval algorithm provides a “H2O flag” for heavy cloudy conditions that invalidates the AMF determination and, consequently, the retrieved TWVC data, it does not apply any cloud correction method for the remaining cloudy cases (Valks et al., 2011; Grossi et al., 2014). Therefore, it is expected that the TWVC data derived from the GDP algorithm presents a larger dependence on cloud properties than other satellite retrieval algorithms with some implemented cloud correction method.
Figure 8 (top plot) shows the weighted relative differences between sounding
and satellite data as a function of the reference GRUAN TWVC values (using
bins of 10 mm). This dependence has been studied using those cases with SZA
smaller than 50
Differences between GOME-2 and GRUAN sounding data (Eq. 3) as
function of the GRUAN TWVC values for cases with SZA below 50
Figure 8 (bottom plot) shows only cloud-free cases for opposite SZA
conditions. It can be seen that GOME-2 data clearly overestimate the
reference data for those cases with SZA
The analysis of the relative differences between GOME-2 and GRUAN TWVC data
reported an average value (weighted with the combined uncertainty derived
from the estimated errors of both data sets) of
Nevertheless, the sounding-satellite differences obtained during cloud-free
conditions displayed a strong dependence on SZA for angles above 50
The influence of the cloud properties (CF, CTA and CTP) in the sounding-satellite differences were also studied in detail. Thus, GOME-2 data underestimate the reference GRUAN values when the satellite scene is contaminated with some degree of cloudiness, and this underestimation increases with increasing CF (up to 30 %) and CTA, and decreasing CTP (increasing cloud top height). Therefore, although heavy cloudy conditions were removed from the analysis using the “H2O flag” provided by the satellite algorithm, the remaining cloudy cases cause a significant bias in the satellite-sounding inter-comparison.
Overall, the recommendation given to potential users of the operational
GOME-2 TWVC data is to work with cloud-free data for SZA below 50
The GOME-2/MetOp-A products were generated at DLR under the auspices of the O3MSAF project funded by EUMETSAT and national contributions. The sounding measurements used in this study have been provided by the GCOS Reference Upper-Air Network (GRUAN). This work has been partially supported by Ministerio de Ciencia e Innovacion under project CGL2011-29921-C02-01. Manuel Antón thanks Ministerio de Ciencia e Innovación and Fondo Social Europeo for the award of a postdoctoral grant (Ramón y Cajal). Financial support to the University of Valladolid was provided by the Spanish MINECO (Ref. Projects CGL2011-23413 and CGL2012-33576). The authors appreciate the great contribution of the two anonymous referees in the revision process, and the help and useful comments from Agustín García and María L. Cancillo. Edited by: P. Stammes