The objective of this study is to derive methane
(CH4) emissions from three landfills, which are found to be the most
significant CH4 sources in the metropolitan area of Madrid in Spain. We
derive CH4 emissions from the CH4 enhancements observed by
spaceborne and ground-based instruments. We apply satellite-based
measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the
Infrared Atmospheric Sounding Interferometer (IASI) together with
measurements from the ground-based COllaborative Carbon Column Observing
Network (COCCON) instruments.
In 2018, a 2-week field campaign for measuring the atmospheric
concentrations of greenhouse gases was performed in Madrid in the framework
of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project.
Five COCCON instruments were deployed at different locations around the
Madrid city center, enabling the observation of total column-averaged
CH4 mixing ratios (XCH4). Considering the prevalent wind regimes,
we calculate the wind-assigned XCH4 anomalies for two opposite wind
directions. Pronounced bipolar plumes are found when applying the method to
NO2, which implies that our method of wind-assigned anomaly is suitable
to estimate enhancements of trace gases at the urban level from satellite-based
measurements. For quantifying the CH4 emissions, the wind-assigned
plume method is applied to the TROPOMI XCH4 and to the lower
tropospheric CH4/ dry-air column ratio (TXCH4) of the combined
TROPOMI+IASI product.
As CH4 emission strength we estimate 7.4 × 1025± 6.4 × 1024 molec. s-1 from the TROPOMI XCH4 data and
7.1 × 1025± 1.0 × 1025 molec. s-1 from
the TROPOMI+IASI merged TXCH4 data. We use COCCON observations to
estimate the local source strength as an independent method. COCCON
observations indicate a weaker CH4 emission strength of 3.7 × 1025 molec. s-1 from a local source (the Valdemingómez waste
plant) based on observations from a single day. This strength is lower than
the one derived from the satellite observations, and it is a plausible
result. This is because the analysis of the satellite data refers to a
larger area, covering further emission sources in the study region, whereas
the signal observed by COCCON is generated by a nearby local source. All
emission rates estimated from the different observations are significantly
larger than the emission rates provided via the official Spanish Register of
Emissions and Pollutant Sources.
Introduction
Methane (CH4) is the second most important anthropogenic greenhouse gas
(GHG) after carbon dioxide (CO2) and contributes about 23.4 % to the
radiative forcing by long-lived GHGs in the atmosphere (Etminan et al.,
2016). The amount of atmospheric CH4 has increased 260 % with respect
to pre-industrial levels, reaching 1877 ppb in 2019 (World Meteorological
Organization, 2020). The global atmospheric CH4 emissions are
approximately 40 % caused by natural sources (e.g., wetlands and termites),
and about 60 % of emissions are from anthropogenic sources (Saunois et al., 2020). The anthropogenic sources of CH4 mainly originate from production and
burning of fossil fuels, ruminant animals, agriculture and waste management
(Bousquet et al., 2006; Chynoweth et al., 2001; Kirschke et al., 2013;
Saunois et al., 2020). The waste management sector accounts for 21.5 % of
the total anthropogenic CH4 emissions (Crippa et al., 2019), in which
∼ 44 % of emissions are from landfills. The global
uncertainty share of landfills is about 55 % (Solazzo et al., 2021). The
metropolitan cities are continuously growing due to population movements,
industries, etc., and, thus, more and more cities incorporate landfills (and
other potential CH4 sources) into their limits and influential areas,
making landfills become one of the main CH4 sources. Since CH4
emissions from landfills can vary over several orders of magnitude due to
different factors, e.g., the texture and thickness of cover soils, as well as seasonal
climate, they become complex sources (Cambaliza et al., 2015). Therefore,
the quantification of CH4 emission from landfills using spaceborne and
ground-based observations is of importance for future climate emission
scenarios and for monitoring changes in emissions.
Many studies have demonstrated the capabilities of satellite observations to
estimate CH4 emissions, e.g., from oil and gas sector, including
accidental leakages (e.g., Pandey et al., 2019; Varon et al., 2019; De Gouw et
al., 2020; Schneising et al., 2020) and from coal mining (Varon et al.,
2020). Launched in October 2017, the TROPOspheric Measuring Instrument
(TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite provides
complete daily global coverage of CH4 with an unprecedented resolution.
Compared to previous satellite instruments, TROPOMI is able to capture
CH4 enhancements due to emissions on fine scales and to detect large
point sources (Varon et al., 2019; De Gouw et al., 2020; Schneising et al.,
2020). Satellite retrievals using thermal infrared nadir spectra as observed
by IASI (Infrared Atmospheric Sounding Interferometer) or TES (Tropospheric
Emission Spectrometer) are especially sensitive to CH4 concentrations
between the middle troposphere and the stratosphere (e.g., Siddans et al.,
2017; García et al., 2018; De Wachter et al., 2017; Kulawik et al.,
2021; Schneider et al., 2021a). Schneider et al. (2021a) developed an a
posteriori method for combining the TROPOMI and IASI products to detect
tropospheric CH4, which has a positive bias of ∼ 1 %
with respect to the reference data. The Total Carbon Column Observing
Network (TCCON), a network of high-resolution Fourier-transform infrared spectroscopy (FTIR) spectrometers
(Washenfelder et al., 2006), has been designed to provide accurate and
long-lasting time series of column-averaged dry-air molar fractions of GHGs
and other atmospheric constituents (Wunch et al., 2011). Recently, TCCON GHG
observations have been extended by the COllaborative Carbon Column Observing
Network (COCCON, Frey et al., 2019), which is a research infrastructure
using well-calibrated low-resolution FTIR spectrometers (Bruker EM27/SUN,
Gisi et al., 2012) and a common data analysis scheme. Due to the ruggedness
of the portable devices used and simple operability, COCCON is well suited
for implementing arrays of spectrometers in metropolitan areas for the
quantification of local GHG sources (Hase et al., 2015; Luther et al., 2019;
Vogel et al., 2019; Dietrich et al., 2021).
Madrid, Spain, is one of the biggest cities in Europe and has almost 3.3
million inhabitants, with a metropolitan area population of approximately 6.5
million. Thus, the waste is one of the main CH4 emission sources. To
measure atmospheric concentrations of GHGs in this urban environment, a
2-week campaign was carried out in the framework of the Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project (García et al.,
2019) from 24 September to 7 October 2018 in Madrid.
In this study we analyze nearly 3 years of TROPOMI total column-averaged
dry-air molar fraction of CH4 (XCH4) measurements, TROPOMI+IASI
TXCH4 measurements together with COCCON spectrometer observations made
during the MEGEI-MAD campaign, in an attempt to quantify the CH4
emissions from major emission sources – namely three landfills in Madrid,
the most important metropolitan area of Spain. In Sect. 2 our methodology
is described, which is as follows: we calculate the difference of the
satellite data maps for two opposite wind regimes (we refer to the resulting
signals as wind-assigned anomalies). A simple plume model is then applied to
predict the wind-assigned anomalies for a chosen position and strength of a
source. The results of our study are presented and discussed in Sect. 3,
and the conclusions from these results are given in Sect. 4.
MethodGround-based and spaceborne instrumentationsCOCCON XCH4 data set
The Bruker EM27/SUN is a robust and portable FTIR spectrometer, operating at
a medium spectral resolution of 0.5 cm-1. The EM27/SUN FTIR
spectrometer has been developed by the Karlsruhe Institute of Technology
(KIT) in cooperation with Bruker Optics GmbH for measuring GHG
concentrations (Gisi et al., 2012; Hase et al., 2016). An InGaAs
(indium gallium arsenide) photodetector is used as the primary detector,
covering a spectral range of 5500–11 000 cm-1. A decoupling mirror
reflects 40 % of the incoming converging beam to an extended InGaAs
photodetector element, covering the spectral range of 4000–5500 cm-1
for simultaneous carbon monoxide (CO) observations. The recording time, for
a typical measurement consisting of five forward and five backward scans, is
about 58 s in total.
Several successful field campaigns and long-term deployments have
demonstrated that the Bruker EM27/SUN FTIR spectrometer is an excellent
instrument with good quality, robustness and reliability, and its performance
offers the potential to support TCCON (Frey et al., 2015, 2019;
Klappenbach et al., 2015; Chen et al., 2016; Butz et al., 2017; Sha et al.,
2020; Jacobs et al., 2020; Tu et al., 2020a, b; Dietrich et al.,
2021). The Bruker EM27/SUN spectrometers have become commercially available
from April 2014 onwards, and currently about 70 spectrometers are operated by
different working groups in Germany, France, Spain, Finland, Romania, USA,
Canada, UK, India, Korea, Botswana, Japan, China, Mexico, Brazil, Australia
and New Zealand. The development of the COCCON
(https://www.imk-asf.kit.edu/english/COCCON.php, last access: 22 December 2020) became possible by
continued European Space Agency (ESA) support. COCCON intends to become a
supporting infrastructure for GHG measurements based on common standards and
data analysis procedures for the EM27/SUN (Frey et al., 2019).
All the Bruker EM27/SUN spectrometers used in the MEGEI-MAD project were
operated in accordance with COCCON requirements. The resulting XCH4
data used in this work were generated by the central facility operated by
KIT for demonstrating a centralized data retrieval for the COCCON network.
For these reasons, we refer to the Bruker EM27/SUN spectrometers as COCCON
spectrometers in the following. The COCCON XCH4 data product is derived
from the co-observed total column amounts of CH4 and oxygen (O2),
and the assumed dry-air molar fraction of O2 (0.2095) (Wunch et al.,
2015):
XCH4=columnCH4columnO2×0.2095.
TROPOMI XCH4 data set
The TROPOMI data processing deploys the RemoTeC algorithm (Butz et al.,
2009, 2011; Hasekamp and Butz, 2008) to retrieve XCH4 from TROPOMI
measurements of sunlight backscattered by the Earth's surface and atmosphere
in the near-infrared (NIR) and shortwave-infrared (SWIR) spectral bands (Hu
et al., 2016, 2018; Hasekamp et al., 2021; Landgraf et al., 2019). This
algorithm has been extensively used to derive CH4 and CO2 from
GOSAT (Butz et al., 2011; Guerlet et al., 2013). The TROPOMI XCH4 is calculated from the CH4 vertical sub-columns xi and the dry-air column. The dry-air column is obtained from the surface pressure from the European Centre for Medium-Range Weather Forecasts (ECMWF) and from the altitude from the Shuttle Radar Topography Mission (SRTM) (Farr et al., 2007) digital elevation map with a resolution of 15 arcsec (Lorente et al., 2021a):
XCH4=∑i=0nxicolumndryair.
This study uses the TROPOMI data set of XCH4 from Lorente et al.
(2021a), for which an updated retrieval algorithm was implemented to obtain a
data set with less scatter. This updated XCH4 has been demonstrated to
be in good agreement with TCCON (-3.4± 5.6 ppb) and GOSAT (-10.3±16.8 ppb), with a bias and precision below 1 %. Here the TROPOMI
XCH4 between 30 April 2018 and 30 December 2020 within the
rectangular area of 39.5–41.5∘ N and
4.5–3.0∘ W (125 km × 220 km) over Madrid
is analyzed. In addition, we apply strict quality control to TROPOMI
XCH4 (quality value q=1.0) to exclude data of questionable quality
and to assure data under clear-sky and low-cloud atmospheric conditions
(Lorente et al., 2021a).
IASI CH4 data and their synergetic combination with TROPOMI data
The IASI sensors are currently orbiting aboard three Metop
(Meteorological Operational) satellites and offer global coverage twice
daily with high horizontal resolution (ground pixel diameter at nadir is 12 km). The IASI CH4 products have a particular good quality and
sensitivity as documented in validation studies (e.g., Siddans et al., 2017;
De Wachter et al., 2017; García et al., 2018; Schneider et al., 2021a).
Here we use the IASI CH4 product as generated by the latest MUSICA IASI
processor version (Schneider et al., 2021b). Combining these IASI profile data with the TROPOMI total column data causes strong synergies. Schneider et al. (2021a) developed an a posteriori method for such a synergetic combination
and documented the possibility to detect tropospheric partial
column-averaged dry-air molar fractions of CH4 (TXCH4)
independently from the upper tropospheric/stratospheric dry-air molar
fractions of CH4 (UTSXCH4). This is not possible by either the
TROPOMI or IASI product individually. In this study we use a tropospheric
product averaged from ground to 7 km a.s.l. and an upper
tropospheric/stratospheric product averaged from 7 to 20 km a.s.l.
COCCON Madrid campaign
Madrid is located on the Meseta Central and 60 km south of the Guadarrama
mountains with a considerable altitude difference across the city, ranging
from 570 to 700 m a.s.l.
This work was made in the framework of the MEGEI-MAD project (García et
al., 2019), which aimed to measure atmospheric concentrations of GHGs in an
urban environment combining FTIR instruments and ground-level analyzers.
Another objective of MEGEI-MAD was to analyze the possible use of portable
COCCON instruments to shape an operational network for Madrid in the future.
The MEGEI-MAD project was initiated by the Izaña Atmospheric Research
Center (AEMet), in cooperation with two German research groups (the
Karlsruhe Institute of Technology and the University of Heidelberg) and two
Spanish research groups (the Autonomous University of Barcelona and the
University of Valladolid).
Within MEGEI-MAD, a 2-week field campaign was carried out from 24 September
to 7 October 2018 in Madrid, where five COCCON instruments were located
at five different places circling the metropolitan area (see
Fig. 1). Table 1 summarizes
the coordinates, altitudes of the COCCON locations and auxiliary
meteorological data collected for data analysis of the observations. The
locations have been chosen by considering the prevailing winds and the
emission sources of CO2 and CH4, as well as other technical and
logistic criteria (García et al., 2019, 2021).
Locations of the five COCCON instruments and meteorological records
for the MEGEI-MAD field campaign during 24 September–7 October 2018.
StationEM27/SUNLatitudeLongitudeAltitudeMeteorological records(∘ N)(∘ W)(m a.s.l.)Tres OlivosKIT SN5340.4993.689736Data logger from AEMet Barajas AirportBarajasAEMet SN8540.4653.581637Barajas AirportJose EchegarayDLR SN6940.3793.613633Data logger from DLR Cuatro Vientos AirportCuatro VientosKIT SN5240.3683.780703Cuatro Vientos AirportAEMetKIT SN8140.4523.724685AEMet headquarters
Emission strength calculation using a simple plume model
The daily plume is modeled as a function of wind direction and wind speed.
The schematic dispersion model for describing emissions assumes an expanding
cone-shaped plume with the tip at the plume source at location 0,0. The plume cone has an opening angle of size α, and any grid
cell within the cone is affected by the emission (see
Fig. 2). The angle α is a technical
parameter to schematically describe a spreading of the plume and is
empirically adjusted to a value of 60∘. Different opening
angles are modeled and presented in Fig. A1.
The modeled plume has the most similar shape compared to the TROPOMI
measured NO2 plume (see Sect. 3.3) when α>=60∘. If the grid cell x,y locates
inside the cone, the column enhancement for this cell can be calculated by
Δcolunm(x,y)=εv⋅d(x,y)⋅α,
where ε is the emission strength at the source point in molec. s-1, v is the wind speed in m s-1, d is the distance between
the downwind point and the source, and α is the opening angle of the
plume in rad (here assumed to be 60∘).
The distance from a general grid cell x,y from the source
is
dx,y=x2+y2.
The enhanced dry-air volume mixing ratio for target species (ΔXVMR)
at the center of the grid cell x,y can then be calculated
by dividing the column enhancement by the total column of dry air
(columndryair):
ΔXVMR=Δcolunm(x,y)columndryair.
The columndryair is computed from the surface pressure:
columndryair=Psmdryair⋅g(φ)-mH2Omdryair⋅columnH2O,
where Ps is the surface pressure, mdryair and mH2O are the
molecular masses of dry air (∼28.96 g mol-1) and water vapor (∼18 g mol-1), respectively, columndryair and
columnH2O are the total column amount of dry air and water vapor,
and g(φ) is the latitude-dependent surface acceleration due to
gravity.
In this study, each individual landfill is considered an individual point
source. The daily plumes from the individual landfills are super-positioned
to have a total daily plume. The averaged enhancement of XVMR (plume) over
the study area is computed for the selected wind sector. The plume for the
opposite wind regime is also constructed in the same manner. The differences
between these two data sets are therefore the wind-assigned anomalies (see
Sect. 3.3). By fitting the modeled wind-assigned anomalies to the anomalies
as observed by the satellite, we can estimate the actual emission strength
(see Sect. B2). Note that the applied calculation scheme would also be
extendible to areal sources by superimposing such calculations using
different locations of the origin.
Sketch of the simple plume model used to explain the CH4
emission estimation method. The methane at the point source is distributed
along the wind direction (wind speed: v) in the cone-shaped area
with an opening angle of α. The point source emits the methane at an
emission rate of ε. We assumed the methane molecules are evenly
distributed in the dotted area A, and the distance from area A to the point
source is d. Therefore, the emitted methane in dt time period equals the
amount of methane in the area A. It yields the equation ε×dt≈Δcolumn×απ×π×d×v×dt.
Results and discussionIntercomparison of TROPOMI and COCCON XCH4 measurements
To detect whether TROPOMI is capable of measuring XCH4 precisely in the
Madrid area, we perform intercomparison between TROPOMI and COCCON XCH4 measurements. Figure 3 shows the correlation
between COCCON and TROPOMI measurements. The mean value of TROPOMI XCH4
is calculated by collecting observations within a radius of 5 km around each
COCCON station. The coincident COCCON mean XCH4 is calculated from the
measurements within 30 min before or after the TROPOMI overpass. The
distance between two stations ranges between 6 and 14.2 km. The TROPOMI
data within a circle with a larger radius might cover the information from
other nearby stations, which brings an error in the correlation between the
coincident data. Therefore, we choose a collection circle with a radius of 5 km for TROPOMI. The coincident data at each station show generally good
agreement. Note that there are 1 to 2 TROPOMI measurements located within a
circle of 5 km radius around each station. The mean bias in XCH4
between TROPOMI and COCCON is 2.7 ± 13.2 ppb, which is below the
absolute bias between TROPOMI and TCCON (3.4 ± 5.6 ppb, Lorente et
al., 2021a). The higher scatter of the validation with COCCON reflects the
shorter temporal and spatial collocation, but the agreement indicates that
TROPOMI data have good quality and a low bias.
Correlation plot between TROPOMI observations collected within 5 km radius around each COCCON station and coincident COCCON measurements (30 min before and after the TROPOMI overpass) at five stations in 2018.
The coincident data on 25 September 2018 and 4 October 2018 show large
biases at Jose Echegaray station where the SN69 COCCON instrument is
located. Due to its coarser spatial resolution, the TROPOMI XCH4
observations do not capture the local enhancements detected by the COCCON
instrument in the vicinity of the source. Figure 4
illustrates the 2 example days of the time series of COCCON SN69 and
coincident TROPOMI observations. Obvious enhancements are observed at around
13:00 UTC by the COCCON instrument in the downwind site on 25 September and
at around 12:30 on 4 October 2018 (see Fig. A2
for the other days). Note that the XCH4 enhancements can also be
observed by the instruments at other stations when the CH4 plume passes
over Madrid. We only discuss the 2 representative days with obvious
enhancements here, as we focus on the specific source near the Jose
Echegaray station. The Valdemingómez and Pinto waste plants are located
nearby, with a distance of 4.5 and 12 km, respectively. These five COCCON
stations can serve as an independent source of information for constraining
the wind speed. For example, the distance between the Jose Echegaray and
Barajas is about 10 km. The highest anomalies of XCH4 arrived around
1.5 h later at Barajas station than they appeared at the Jose Echegaray
station on 25 September 2018, which indicates an averaged wind speed of 1.8 m s-1. This value fits well with the wind velocity observed at the Cuatro
Vientos Airport.
Time series of COCCON measurements at five stations on 2 d in
2018. Star symbols represent the averaged TROPOMI observations within a
radius of 5 km around each station. Lower panels show the wind direction and
wind speed measured at the Cuatro Vientos Airport.
TROPOMI detected 10 ppb higher XCH4 at Jose Echegaray station than at
Barajas station on 25 September 2018. However, COCCON observed a much
higher amount of XCH4 (53 ppb) at Jose Echegaray station than at
Barajas station (and other stations) at around 13:00 UTC. The delayed
enhancements at AEMet and Barajas stations at the downwind direction are
found after the wind direction changed from north more towards south
direction. Another obvious enhancement of XCH4 is observed at Jose
Echegaray station by the COCCON SN69 instrument at around 12:30 on 4 October
2018, with about 97 ppb higher XCH4 than COCCON measurements at the
other four stations. However, TROPOMI only measured about 13 ppb higher
XCH4 at Jose Echegaray station compared to the TROPOMI measurements at
the other stations. These considerable enhancements at Jose Echegaray
station observed by the COCCON instrument are likely due to the local source
(the nearby Valdemingómez waste plant). The plume is in close vicinity
to the source narrower than the pixel scale of the satellite, and therefore it
is only detected as an attenuated signal by TROPOMI. The full width at the
half maximum (FWHM) of the enhancement peak on 4 October 2018 roughly
covers a temporal window of 30 min, with a corresponding wind direction
change of 22.5∘ (∼ 0.4 rad) and an averaged wind
speed of 1.0 m s-1. The distance between the COCCON SN69 to the
Valdemingómez waste plant is about 4500 m. The 97 ppb enhancement
measured by COCCON SN69 instrument yields an estimated emission strength of
3.7 × 1025 molec. s-1.
According to the Spanish Register of Emissions and Pollutant Sources (PRTR,
http://www.en.prtr-es.es/, last access: 20 February 2021), more than 95 %
of total CH4 emissions are from three waste treatment and disposal
plants in the Madrid region (locations showed in
Fig. 1). The annual CH4 emission rates from
the PRTR for each plant are listed in Table 2. The
total emission strength for each plant is about 2.5 × 1025 molec. s-1. This value only considers the “cells” in production, i.e.,
those where the waste is not yet covered with soil. The emissions from
sealed cells are not included in the total emissions, but they still emit
CH4 for years after sealing. So, the estimated emission rates from the
inventories are expected to underestimate the true emissions, which fits
reasonably with the estimated emission rate derived from COCCON
measurements. The COCCON instruments show a very good ability to detect the
source. Based on this evidence we investigate the potential of the TROPOMI
and IASI CH4 products for detecting CH4 sources in the following.
CH4 emission rates in three waste treatment and disposal
plants in Madrid from PRTR.
To better represent the whole area of Madrid, the hourly ERA5 model wind at
a height of 10 m around Madrid is used. ERA5 is the fifth-generation climate
reanalysis produced by the European Centre for Medium-Range Weather
Forecasts (ECMWF) (Copernicus Climate Change Service, 2017). The TROPOMI
overpasses over Madrid cover the time range from 12:00–14:30 UTC
(IASI overpasses are typically from 09:30–10:30 UTC), but the
dispersion of emitted CH4 is influenced by the ground conditions (e.g.,
wind speed and wind direction) over a wider time range (Delkash et al.,
2016; Rachor et al., 2013). Therefore, the wind information between daytime
(08:00–18:00 UTC) is chosen to define the predominant wind direction
for each day. Figure 5 presents the wind roses for
daytime between 10 November 2017 and 10 October 2020 (the first and last day
with valid TROPOMI data). The dominating wind direction was southwesterly.
The Guadarrama mountains and the
Jarama and Manzanares river basins are located the northwest of Madrid, and they influence the air flow. Therefore,
we use a wider wind range for the specific wind area in this study to cover
the dominant wind directions, i.e., SW for the range of 135–315∘ and NE for the remaining direction. If a wind direction
dominates 60 % of records for 1 d, i.e., if the wind direction belongs
to one specific area more than 60 % of the daytime (08:00–19:00 UTC), then this predominant wind direction is selected for that day. The SW
and NE wind fields are used for constructing wind-assigned anomalies in this
study, and we demonstrate this construction by using TROPOMI nitrogen
dioxide (NO2) data in the next section. Table 3
summarizes the number of days and wind speed for each specific wind area. The
wind direction during the TROPOMI overpasses was 61.8 % in the SW wind field
and 28.4 % in the NE wind field, and their averaged wind speed is similar.
Wind roses for daytime (08:00–19:00 UTC) from 10 November
2017 to 10 October 2020 for the ERA5 model wind. Panel (a) covers all
days and panel (b) covers the days with TROPOMI overpasses.
Number of days and the averaged ERA5 wind speed (± standard
deviation) per specific wind area in daytime (08:00–18:00 UTC) from
10 November 2017 to 10 October 2020. Columns 2 and 3 are for all days, and
columns 4 and 5 are for days with TROPOMI overpass.
TROPOMI overpass Number of daysAveraged wind speed ±Number of daysAveraged wind speed ±Wind direction rangein total (%)standard deviation (m s-1)in total (%)standard deviation (m s-1)NE/>315∘ or <135∘30.42.6 ± 1.528.42.3 ± 1.2SW/135–315∘68.42.8 ± 1.761.82.3 ± 1.4Demonstration of the wind-assigned anomaly method
When fossil fuels are burned, nitrogen monoxide (NO) is formed and emitted
into the atmosphere. NO reacts with O2 to form NO2 and with
ozone (O3) to produce O2 and NO2. NO2 is an extremely
reactive gas with a short lifetime of a couple of hours and has lower
background levels than CH4 (Kenagy et al., 2018; Shah et al., 2020). It
is measured by TROPOMI with excellent quality. Therefore, it is a suitable
proxy for demonstrating the method developed for the wind-assigned anomaly.
TROPOMI offers simultaneous observations of NO2 columns. The
recommended quality filter value for the analysis of TROPOMI NO2
columns is qa_value > 0.75
(http://www.tropomi.eu/sites/default/files/files/publicSentinel-5P-Level-2-Product-User-Manual-Nitrogen-Dioxide.pdf,
last access: 11 November 2021). Based on the predominant wind direction in
Madrid (see Sect. 3.2), the averaged wind-assigned anomalies are defined
here as the difference of the mean TROPOMI NO2 column under the wind
direction from NE and the mean TROPOMI NO2 column under the predominant
wind direction of SW in Madrid.
Figure 6a illustrates the wind-assigned anomalies
of TROPOMI NO2 (ΔNO2) on a 0.1∘× 0.135∘ latitude–longitude grid during 2018–2019. Pronounced
fusiform-shaped plumes are observed along NE–SW wind direction as
expected. Figure 6b shows the wind-assigned
anomalies derived from the simple model introduced in Sect. 2.3, using
Madrid city center as the point source with an assumed emission rate (ε) of 5.0 × 1024 molec. s-1 and using ERA5 10 m wind data. The similar symmetrical positive and negative plumes to those
in Fig. 6a imply that our method of
wind-assigned anomaly is working as anticipated, that the ERA5 10 m data
are indeed representative for the area and that the implementation of the
satellite data analysis is correct. Figure 6c
shows the strong correlation between the wind-assigned anomalies derived
from the TROPOMI measurements and the simple plume model (ε=5.0× 1024 molec. s-1). Using the fitting method as
described in Sect. B2, we estimate an emission rate of 3.5 × 1024 molec. s-1± 3.9 × 1022 molec. s-1.
Here the uncertainty is due to the noise of the observations and is
calculated according to Eq. (B15) (Appendix B). This estimated source strength
is weaker than the strength obtained by Beirle et al. (2011), where the
reported NOx emission is around 150 mol s-1 in Madrid,
corresponding to a NO2 emission of 6.8 × 1025 molec. s-1. It is because our model does not consider the decay of NO2,
which results in a lower emission rate.
The result of this test using NO2 also allows the angular spread
parameter used in the plume model to be adjusted (see Sect. 2.3 and Eq. 3). As
it can be seen from Fig. A1, assuming an angular
spread of 60∘ reasonably reproduces the shape of the plume.
Wind-assigned anomalies derived from (a) TROPOMI tropospheric
NO2 column, (b) our simple plume model (ε=5× 1024 molec. s-1) over Madrid in the NE–SW direction on a
0.1∘× 0.135∘ latitude–longitude grid during
2018–2020, and (c) the correlation plot between observed ΔNO2 and modeled ΔNO2 (ε=5× 1024 molec. s-1) during 2018–2019.
XCH4 and TXCH4 anomaly
CH4 has a relatively longer lifetime as compared to NO2, and its
background in the atmosphere is high. An increasing trend with obvious
seasonality and strong day-to-day signals for XCH4 is seen in
Fig. 7 (upper panels). Therefore, these background
signals need to be removed before simulating the wind-assigned anomalies
(see Sect. B1). After removing the background, the anomalies (raw data –
background) represent more or less the emission from local area
(Fig. 7 lower panels).
Figure 8 illustrates the anomalies of XCH4,
TXCH4 and UTSXCH4 for all measurement days, days with
predominating SW wind field and days with predominating NE wind field. The
distributions over the whole area for XCH4 and TXCH4 are similar,
and no obvious enhancement is observed in UTSXCH4, as expected, since
CH4 abundances dominate in the troposphere. The areas where the three
waste plants are located show obvious high anomalies in the figures
(Fig. 8a and d) when the data are averaged over
all days for all wind directions and in the downwind direction
(Fig. 8b, c, e and f), demonstrating that our
method of removing the background works well and the satellite products can
detect the local pollution sources after removing the background. Enhanced
plumes of XCH4 and TXCH4 are better visible on the downwind side
of SW than on the downwind side of NE wind field. This is because the SW is the
most dominant wind direction, and the SW plume signal is based on a higher
number of data and thus less noise.
Time series of (a) XCH4, (b) TXCH4 and (c) UTSXCH4, showing raw data and background in each upper panel and anomalies
in each corresponding lower panel.
(a–c) XCH4, (d–f) TXCH4 and (g–i) UTSXCH4 anomalies averaged for all days, days with SW wind and NE wind directions. The
triangle symbols represent the location of waste plants.
Estimation of CH4 emission strengths from satellite data sets
The wind-assigned anomalies derived from XCH4 anomalies and TXCH4
anomalies on a 0.1∘× 0.135∘ latitude–longitude
grid are presented in Fig. 9. The XCH4 and
TXCH4 wind-assigned anomalies show similar bipolar plumes but are more
disturbed compared to those derived from NO2. This is because the
CH4 signal is weak compared to the background concentration, so the
noise level of the measurement and the imperfect elimination of the
background are significant disturbing factors.
Based on the knowledge of the locations of the three waste plants, we choose
their locations as point sources to model the enhanced XCH4 according
to the wind information. The initial emission strength is 1 × 1026 molec. s-1 in total, and the emission rate at each point source is repartitioned among these three sites according to
Table 2. The modeled and observed wind-assigned
anomalies show a reasonable linear correlation (coefficient of determination
R2 of about 49 % and 44 % for XCH4 and TXCH4,
respectively) with observed ΔXCH4. Based on Eq. (B12) (Appendix B), we obtained an estimated emission rate of 7.4 × 1025± 6.4 × 1024 molec. s-1 for XCH4 and
7.1 × 1025± 1.0 × 1025 molec. s-1 for
TXCH4. The uncertainty values given here are the square root sum of the
uncertainty due to the background signal and the data noise, which are
calculated according to Eqs. (B14) and (B15) (Appendix B).
Figure 9g, h and i show the wind-assigned
anomalies for UTSXCH4. For the modeled UTSXCH4 anomalies we
assume here the CH4 enhancement to occur at altitudes between 7 and 20 km a.s.l. As expected, the fit of these model data to the observed
UTSXCH4 data yields emission rates close to zero (1.4 × 1025± 7.2 × 1024 molec. s-1), revealing that there is no significant plume signal above 7 km a.s.l. The fact that for
TXCH4 we obtain practically the same emission rates as for XCH4
and that in the UTSXCH4 data we see almost no plume nicely proves the
quality of our careful background treatment method and the low level of
cross sensitivity between the TXCH4 and UTSXCH4 data products. The
applied background treatment allows detecting the near-surface emission
signal consistently in the total column XCH4 data and in the
tropospheric TXCH4 data.
Figure 10 illustrates the estimated emission
strengths for the different products. The emission strengths derived from
the satellites are higher than the ones derived from COCCON measurements, as
TROPOMI covers a larger area, while COCCON measurements are only sensitive to
local sources from the nearby waste plant. The PRTR inventory document gives
lower values than our results. This is probably because it only lists the
active landfill cells and does not include the closed ones in Madrid, which
probably still emit for many years (Sánchez et al., 2019).
Wind-assigned XCH4 plume derived from (a) TROPOMI XCH4
anomalies, (d) synergetic TXCH4 anomalies, and (g) UTSXCH4
anomalies and their corresponding modeled plume (b, e, h) over Madrid in the NE–SW direction on a 0.1∘× 0.135∘
latitude–longitude grid. The correlation plots between observed ΔXCH4 and modeled ΔXCH4 (ε=1× 1026 molec. s-1) for different products (c, f, i). Here we use the
three waste plants as the point sources (blue triangle with red edge color).
The initial emission rate in the plume model is 1 × 1026 molec. s-1. This value is proportionally distributed into three point sources
based on the a priori knowledge of emission rate in each waste plant. For
the modeled UTSXCH4 anomalies we assume the CH4 enhancements to
occur at altitudes between 7 and 20 km a.s.l.
Emission strengths for the different products and for the
sensitivity tests. Also included are the COCCON observations which
characterize the Valdemingómez waste plant contribution and the total of
all three sources according to the PRTR inventory.
Sensitivity study for emission strength estimates
The point sources and their proportion in the total emission rate in this
study are based on the a priori knowledge of three different waste plant
locations. If we use a single source located at the Pinto waste disposal
site only, it yields an emission rate of 6.3 × 1025 molec. s-1, ∼ 15 % lower than that of the three point sources
for CH4 and 6.0 × 1025 molec. s-1 (-15 %) for
tropospheric CH4 (see Fig. 10). The opening
angle (α) is experimentally selected based on the comparison between
the TROPOMI measured and modeled NO2 plume, which results in some
uncertainties as well. Using 90∘ instead of 60∘ for
α in the plume model results in an emission strength of
7.6 × 1025 molec. s-1 (+3 % change) for CH4 and of
7.4 × 1025 molec. s-1 (+4 % change) for
tropospheric CH4.
The surface wind can be influenced by the topography, and the actual
transport pathway from the emission source to the measurement station is
difficult to know (Chen et al., 2016; Babenhauserheide et al., 2020). To
study the wind sensitivity, the hourly wind information measured at the
Cuatro Vientos Airport at 10 m height is used instead of the ERA5 10 m wind.
There are other in situ measurements available but not used here, as the
AEMet headquarter station is affected by nearby buildings and the Barajas
Airport station is very close to a river (Jarama) that determines a specific
wind pattern. The wind measured at the Cuatro Vientos Airport is quite
different compared to the ERA5 wind, as in situ-measured NE wind becomes
dominant as well, and the wind speed in SW wind field increases by
∼ 50 % compared to that of ERA5 wind
(Figs. A3, A4
and Table A1). Using the wind measured at the
Cuatro Vientos Airport results in an emission rate of 7.7 × 1025 molec. s-1 (+4 %) for CH4 and 9.5 × 1025 molec. s-1 (+34 %) for tropospheric CH4.
In summary, the uncertainties derived from the source location, opening
angle or wind cannot be ignored, but nevertheless the emission rates
estimated from the spaceborne observations are clearly larger than the
values reported in Table 2 and are larger than the
ones estimated from the COCCON SN69 observations in October 2018.
Conclusions
The present study analyzes TROPOMI XCH4 and IASI CH4 retrievals
over an area around Madrid for more than 400 d within a rectangle of
39.5–41.5∘ N and 4.5–3.0∘ W (125 km × 220 km) from 10 November 2017 until 10
October 2020. During this time period, a 2-week field campaign was
conducted in September 2018 in Madrid, in which five ground-based COCCON
instruments were used to measure XCH4 at different locations around the
city center.
First, TROPOMI XCH4 is compared with co-located COCCON data from the
field campaign, showing a generally good agreement, even though the
radius of the collection circle for the satellite measurements was as small
as 5 km. However, there are 6 d when obvious enhancements due to local
sources were observed by COCCON around noon at the most southeast station
(Jose Echegaray), which were underestimated by TROPOMI. The ground-based
COCCON observations indicate a local source strength of 3.7 × 1025 molec. s-1 from observations at Jose Echegaray station on
4 October 2018, which is reasonable compared to the emissions assumed for
nearby waste plants. The waste plant locations are later used as the point
sources to model the emission strength for CH4.
According to the ERA5 model wind at 10 m height, SW (135–315∘) winds (NE covering the remaining wind field) are dominant
in the Madrid city center in the time range from November 2017 to October
2020. Based on this wind information, the wind-assigned anomalies are
defined as the difference of satellite data between the conditions of the NE
wind field and SW wind field. We use the simultaneously measured
tropospheric NO2 column amounts from TROPOMI as a proxy to evaluate the
wind-assigned anomaly approach due to its short lifetime and clear plume
shape, by using ERA5 model wind. Pronounced and bipolar NO2 plumes are
observed along the NE–SW wind direction, and a tropospheric NO2
emission strength of 3.5 × 1024± 3.9 × 1022 molec. s-1 is estimated. This implies that our method of wind-assigned
anomaly is working reliably, that the ERA5 wind data used are indeed
representative of the area and the implementation of the satellite data
analysis is correct.
CH4 is a long-lived gas and so there are strong CH4 background
signals in the atmosphere. Therefore, the background values need to be
removed and the anomalies have to be determined before calculating emission
strengths. In this study, the removed background values include the linear
increase, seasonal cycle, daily variability and horizontal variability. The
areas where the three waste plants are located show obvious high anomalies,
demonstrating that satellite measurements can detect the local sources after
removing the background. Enhanced plumes are more pronounced in the downwind
side of SW, whereas the observed downwind plume signal for NE wind is
noisier, partly due to the lower number of NE wind situations.
The wind-assigned TROPOMI XCH4 anomalies show a less clear bipolar
plume than NO2. This is because CH4 has a long lifetime, and its
high background is difficult to totally remove. Based on the
wind-assigned anomalies, the emission strength estimated from the TROPOMI
XCH4 data is 7.4 × 1025± 6.4 × 1024 molec. s-1. In addition, this method is applied to the tropospheric
partial column-averaged (ground – 7 km a.s.l.) dry-air molar fractions of
methane (TXCH4, obtained by combining TROPOMI and IASI products), yielding
an emission strength of 7.1 × 1025± 1.0 × 1025 molec. s-1. We show that in the upper troposphere/stratosphere
there is no significant plume signal (1.4 × 1025± 7.2 × 1024 molec. s-1). The estimation of very similar
emission rates from XCH4 and TXCH4 together with the estimated
negligible emission rates when using data representing the upper
troposphere/stratosphere proves the robustness of our method. The emission
rates derived from satellites (XCH4 and TXCH4) are higher than
that derived from COCCON observations, as satellites cover larger areas with
other CH4 sources and COCCON likely measures local sources.
The surface wind is easily influenced by the topography, which introduces
uncertainties in the estimated emission strengths. Using in situ-measured
wind at the Cuatro Vientos Airport instead of ERA5 model wind results in an
estimated emission rate of 7.7 × 1025 molec. s-1 (+4 %)
for CH4 and 9.5 × 1025 molec. s-1 (+34 %) for
tropospheric CH4. Uncertainties can also be caused by the choice of
the opening angle in the plume model. The estimated emission rates with
α=90∘ are 7.6 × 1025 molec. s-1
(+3 %) for CH4 and 7.4 × 1025 molec. s-1 (+4 %) for tropospheric CH4. When using a single source located
in the Madrid city center, the emission strengths are 6.3 × 1025 molec. s-1 (-15 %) for CH4 and 6.0 × 1025 molec. s-1 (-15 %) for tropospheric CH4.
In summary, in this study for the first time TROPOMI observations are used
together with IASI observations and the ground-based COCCON observations to
investigate CH4 emissions from landfills in an important metropolitan
area like Madrid. The COCCON instruments show a promising potential for
satellite validation and an excellent ability for observation of local
sources. The data presented here show that TROPOMI is able to detect the
tropospheric NO2 and XCH4 anomalies over metropolitan areas with
support from meteorological wind analysis data. This methodology could also
be applied to other source regions, space-based sensors and sources of
CO2.
Number of days and the averaged wind speed (±standard
deviation) per specific wind area in daytime (08:00–18:00 UTC) from
10 November 2017 to 11 September 2020 measured at the Cuatro Vientos
Airport. Columns 2 and 3 are for all days, and columns 4 and 5 are for days
with TROPOMI overpass.
TROPOMI overpass Number of daysAveraged wind speed ±Number of daysAveraged wind speed ±Wind direction rangein total (%)standard deviation (m s-1)in total (%)standard deviation (m s-1)NE/>315∘ or <135∘35.42.4 ± 1.536.02.2 ± 1.3SW/135–315∘49.34.2 ± 2.544.43.4 ± 2.1
Examples of wind-assigned NO2 plume based on the simple
plume model (ε=5.0× 1024 molec. s-1)
using Madrid as the point source in the NE–SW direction on a 0.1∘× 0.135∘ latitude–longitude grid with a different opening
angle (α) from 10 to 90∘.
Time series of COCCON measurements at five stations and
corresponding time series of wind fields (direction and speed) measured at
the Cuatro Vientos Airport on 8 d during MEGEI-MAD campaign in 2018.
Star symbols represent the TROPOMI observations within a radius of 5 km
around each station.
Percentage of occurrence for wind direction measured at the
Cuatro Vientos Airport between 2000 and 2020. The predominant wind direction
is southwest and up to 35 % of time.
Wind roses for daytime (08:00–19:00 UTC) from 10
November 2017 to 11 September 2020 from the wind measurements at the Cuatro
Vientos Airport. Panel (a) covers all days and panel (b) covers
the days with TROPOMI overpasses.
CH4 background signal
The satellite data can be written as a vector y, where each element
corresponds to an individual satellite data point. This signal is caused by
a CH4 background signal and the CH4 plume due to the emissions
from the waste disposal sites near Madrid:
y=yBG+yplume.
It is of great importance to adequately separate both components for
estimating the emission strength from the satellite data.
For determining the background signal (yBG), we set up
a background model:
mBG=yBG=KBGxBG.
The matrix KBG is a Jacobian matrix
that allows us to reconstruct the background according to a few background
model coefficients (the elements of the vector xBG).
We also create a Jacobian
KBG∗, which is the
same as KBG but set to zero for
observations where the wind data suggest a significant impact of the
CH4 plume on the satellite data. The calculations of the plume CH4
signals are made according to Sect. 2.3. With the use of
KBG∗ we make sure
that the estimated background signal is not affected by the CH4 plume.
The KBG is a Jacobian matrix where
each row represents an individual satellite observation and each column represents a
component of the background model. The background model considers a smooth
background, which is a constant CH4 value, a linear increase with time,
and a seasonal cycle described by the amplitude and phase of the three
frequencies 1/year, 2/year and 3/year. Furthermore, we fit a daily anomaly,
which is the same for all data measured during a single day, and a horizontal
anomaly, which is the same for any time but dependent on the horizontal
location. For the latter we use a 0.1∘× 0.135∘
(latitude × longitude) grid.
We invert the problem in order to estimate the background model coefficients
(elements of the vector xBG):
x^BG=GBGy,
with GBG being the so-called gain
matrix,
GBG=(KBG∗TSy,n-1KBG∗+Sa-1)-1KBG∗TSy,n-1.
Because KBG∗ (and thus GBG) is set
to zero whenever yplume=0, we can use in Eq.
(B3) y instead of yBG. The matrix
Sy,n stands for the noise covariance of the satellite
data. For constraining the problem, we use a diagonal
Sa-1 (no constraint between different
coefficients) with a very low constraint value for the coefficient
determining the constant and higher constraint values for the other
coefficients. For calculating the uncertainty of the background signal, we
calculate the vector
y-KBG∗x^BG and then the mean square value from its elements
that represent observations not affected by the plume. This mean square
value is then used as the diagonal entries of the diagonal matrix
Sy,BG. In this context, Sy,BG
considers the deficits of the background model and the uncertainty in the
background if determined from data with a certain noise level. As an
alternative, we could use modeled high-resolution XCH4 fields (e.g.,
from CAMS high-resolution greenhouse gas forecast, Barré et al., 2021)
for these calculations. We can assume that the model data have no noise and
perform an exclusive estimation of the deficits of the background model
calculation in form of a full Sy,BG covariance matrix. This more
sophisticated uncertainty estimation can be a task for future work.
The uncertainty of the background model coefficients can be calculated as
Sx^BG=GBGSy,BGGBGT.
For each day there is an uncertainty in the background coefficients and the
uncertainty is correlated with the uncertainty at other days. All this
information is provided in the uncertainty covariance
Sx^BG.
With the full Jacobian KBG we can
now model the background for the measurement state (also for the
measurements that are assumed to be affected by the CH4 waste disposal
plume),
yBG=KBGx^BG,
and calculate the plume signal according to Eq. (B1)
as
yplume=y-KBGx^BG.
The uncertainty of these plume signal is the sum of the uncertainties of the
satellite data Sy,n and the uncertainty of the
estimated background:
Sy,plume≈Sy,n+KBGSx^BGKBGT.
It notes that Eq. (B8) is an approximation, because the two error components
are not completely independent (Sy,BG and
thus Sx^BG depend also on
the noise of the observations; see description for calculating
Sy,BG in the context of Eq. B5).
Fitting of CH4 emission rates
Because the CH4 plume signal is rather weak compared to the CH4 background uncertainty and the noise level of the satellite data, we have
to work with averages in order to reduce the data noise. The averaging is
made by classifying the observation in two predominant wind categories. We
calculate the average plume maps for the southwest and northeast wind
situations (see Figs. 6 and
8). Then we calculate the difference between
the southwest and northeast plume maps (the wind-assigned anomalies or
Δ-maps). All the calculations are made by binning all observations
that fall within a certain 0.135∘× 0.1∘
(longitude × latitude) area. In order to significantly reduce the
data noise, we only consider averages for the 0.135∘× 0.1∘ areas based on at least 25 individual observations made under
southwest wind conditions and 25 individual observations made under
northeast wind conditions. The binning, the averaging, the wind-assigned
Δ-maps calculations and the data number filtering are achieved by
operator D, and we can write
Δyplume=Dyplume
and
ΔSy,plume=DSy,plumeDT.
Here Δyplume is a column
vector whose elements capture the different signal of the two wind
directions at the different locations, and ΔSy,plume is the corresponding
uncertainty covariance.
For modeling the plume signals we use a priori knowledge of CH4
emission locations, i.e., assuming a repartition of the emissions between the
three waste disposal sites according to Table 2 (see
Sect. 3.1). Together with information from the wind, we then model the
CH4 plume's wind-assigned anomaly signal Δyplume:
Δyplume=Δkx.
Here the Jacobian Δk (a column vector)
represents the wind-assigned anomaly model as described in Sect. 2.3. It
describes how an emission at the waste disposal sites according to
Table 2 would be seen in the difference signal. We
are interested in the coefficient x (a scalar describing how the assumed
emissions from Table 2 have to be scaled by a common
factor in order to achieve the best agreement with the observed plume).
Similar to Eqs. (B3) and (B4)
we write
x^=gTΔyplume,
with the row vector
gT=ΔkTΔSy,plume-1Δk-1ΔkTΔSy,plume-1.
This fitting of the emission rate correctly considers the respective
uncertainty of the difference signals at the different locations.
Because of the small plume signals, it is important to estimate the
reliability of the fitted emission rate. The uncertainty of x due to the
background uncertainty and the noise in the satellite data can be estimated
as
ϵBG=gTDKBGSx^BGKBGTDTg
and
ϵn=gTDSy,nDTg
respectively. However, as aforementioned these two error components are not
completely independent.
Data availability
The data are accessible by contacting the corresponding author
(qiansi.tu@kit.edu). The SRON S5P-RemoTeC scientific TROPOMI CH4 data set
from this study is available for download at
10.5281/zenodo.4447228 (Lorente et al., 2021b). The MUSICA IASI data set is available for
download via 10.35097/408 (Schneider et al., 2021c).
Author contributions
QT, FH and OG developed the research
question. QT wrote the manuscript and performed the data analysis
with input from FH, OG, MS and
FK. FH suggested the method of constructing
wind-assigned anomalies for source quantification. MS
suggested the method for calculating the anomalies, for fitting the emission
rates and for estimating the uncertainty. OG provided the COCCON
and meteorological data and helped to interpret them. TB and
AL provided technical support for the TROPOMI data analysis. MS, BE and CJD provided the combined
(MUSICA IASI + TROPOMI) data and provided technical support for the analysis of
these data. All other coauthors participated in the field campaign and
provided the data. All authors discussed the results and contributed to the
final paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank three anonymous reviewers for their constructive comments and suggestions. We acknowledge ESA support through the COCCON-PROCEEDS and COCCON-PROCEEDS
II projects. In addition, this research was funded by the Ministerio de
Economía y Competitividad from Spain through the INMENSE project
(CGL2016-80688-P). This research has largely benefited from funds of the
Deutsche Forschungsgemeinschaft (provided for the two projects MOTIV and
TEDDY with IDs/290612604 and 416767181, respectively). Part of
this work was performed on the supercomputer ForHLR funded by the Ministry
of Science, Research and the Arts Baden-Württemberg and by the German
Federal Ministry of Education and Research.
We acknowledge the support by the Deutsche Forschungsgemeinschaft and the
Open Access Publishing Fund of the Karlsruhe Institute of Technology.
Financial support
This research has been supported by the European Space Agency (COCCON-PROCEEDS and COCCON-PROCEEDS II, grant no. ESA-IPL-POELG-cl-LE-2015-1129), the Ministerio de Economía y Competitividad (INMENSE project, grant no. CGL2016-80688-P), the Deutsche Forschungsgemeinschaft (project MOTIV, ID 290612604; and project TEDDY, ID 416767181), the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (supercomputer ForHLR grant), and the Bundesministerium für Bildung und Forschung (supercomputer ForHLR grant).The article processing charges for this open-access publication were covered by the Karlsruhe Institute of Technology (KIT).
Review statement
This paper was edited by Eduardo Landulfo and reviewed by three anonymous referees.
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