AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-3617-2015Mapping spectroscopic uncertainties into prospective methane retrieval errors from Sentinel-5 and its precursorCheca-GarciaR.ramiro.garcia@kit.edur.checagarcia@gmail.comhttps://orcid.org/0000-0001-7653-3653LandgrafJ.GalliA.https://orcid.org/0000-0003-2425-3793HaseF.VelazcoV. A.https://orcid.org/0000-0002-1376-438XTranH.BoudonV.AlkemadeF.ButzA.https://orcid.org/0000-0003-0593-1608IMK-ASF, Karlsruhe Institute of Technology (KIT), Karlsruhe,
GermanyNetherlands Institute for Space Research (SRON), Utrecht,
the NetherlandsLaboratoire Interuniversitaire des Systèmes
Atmosphériques, CNRS-UMR 7583, Université Paris Est Créteil,
Université Paris Diderot, Institut Pierre-Simon Laplace,
FranceLaboratoire Interdisciplinaire Carnot de Bourgogne, UMR6303
CNRS-Univ. Bourgogne Franche-Comté, 9 Av. A. Savary, BP 47870, Dijon,
FranceSpace Research and Planetary Sciences, Physics Institute,
University of Bern, Bern, SwitzerlandCenter for Atmospheric
Chemistry, Faculty of Science, Medicine & Health, University of
Wollongong, Wollongong, AustraliaR. Checa-Garcia (ramiro.garcia@kit.edu, r.checagarcia@gmail.com)8September201589361736292December201429January201510July20156August2015This 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://amt.copernicus.org/articles/8/3617/2015/amt-8-3617-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/3617/2015/amt-8-3617-2015.pdf
Sentinel-5 (S5) and its precursor (S5P) are future European
satellite missions aiming at global monitoring of methane
(CH4) column-average dry air mole fractions
(XCH4). The spectrometers to be deployed onboard the
satellites record spectra of sunlight backscattered from the Earth's
surface and atmosphere. In particular, they exploit CH4
absorption in the shortwave infrared spectral range around
1.65 µm (S5 only) and 2.35 µm (both S5 and
S5P) wavelength. Given an accuracy goal of better than 2 % for
XCH4 to be delivered on regional scales, assessment and
reduction of potential sources of systematic error such as
spectroscopic uncertainties is crucial. Here, we investigate how
spectroscopic errors propagate into retrieval errors on the global
scale. To this end, absorption spectra of a ground-based Fourier
transform spectrometer (FTS) operating at very high spectral
resolution serve as estimate for the quality of the spectroscopic
parameters. Feeding the FTS fitting residuals as a perturbation into
a global ensemble of simulated S5- and S5P-like spectra at relatively
low spectral resolution, XCH4 retrieval errors exceed
0.6 % in large parts of the world and show systematic correlations
on regional scales, calling for improved spectroscopic parameters.
Introduction
The greenhouse gas methane (CH4) plays a key role in
anthropogenically driven climate change
. Therefore, monitoring of atmospheric
CH4 abundances is one of the crucial elements of future Earth
observing satellite missions e.g.,. The
European Space Agency (ESA) and its national partners have scheduled
the Sentinel-5 Precursor (S5P), also known as TROPOMI
, and the Sentinel-5 (S5)
for launch in 2016 and around 2021,
respectively. Both satellites carry spectrometers sensitive to the
shortwave infrared (SWIR) spectral range. CH4 absorption in
sunlight backscattered from the Earth's surface and atmosphere allows
for the retrieval of column-average dry air mole fractions of methane
(XCH4). Thereby, the S5P and S5 strategy builds on the
pioneering heritage of the SCanning Imaging Absorption spectroMeter
for Atmospheric CHartographY (SCIAMACHY)
and the Greenhouse Gases Observing Satellite (GOSAT)
demonstrating that highly accurate satellite
remote sensing of XCH4e.g.,
can be a valuable tool to gain insight into CH4 emissions at
the Earth's surface e.g.,.
Estimating such surface–atmosphere fluxes through inverse modeling,
however, poses stringent accuracy requirements on the retrieved
XCH4. Regionally or temporally correlated biases as low as
1 % can jeopardize the usefulness of the XCH4 satellite
records for inverse modeling of surface fluxes
. An analogue,
potentially even more stringent requirement applies to remote sensing
of column-average dry air mole fractions of carbon dioxide
(XCO2)
e.g.,. Therefore,
considerable effort is dedicated to estimating and reducing sources of
error for XCH4 (and XCO2) retrievals from solar
backscatter measurements. Most studies focus on how to avoid or
evaluate errors due to light-path uncertainties in light-scattering
atmospheres e.g.,. In particular, assess
the residual aerosol- and cirrus-induced XCH4 retrieval
errors for an S5P-like observer using a global and seasonal ensemble
of simulated S5P measurements.
demonstrate the detrimental impact of
spectroscopic uncertainties on XCH4 retrievals and on the
respective surface flux estimates from SCIAMACHY. They find about
20 % overestimation of the tropical CH4 source (up to 60 ppb) due to
a spurious spectroscopic interference between CH4 and water
vapor (H2O) absorption in the 1.65 µmCH4
band. In a previous support study for the S5P mission,
degrade high-resolution spectra around
2.35 µm wavelength recorded by ground-based Fourier
transform spectrometers (FTS) at a midlatitude and a tropical site to
the spectral resolution of the S5P instrument. They conclude on a weak
dependence of the retrieved XCH4 on spectral resolution and
H2O content of the atmosphere pointing at relatively little
impact of erroneous spectroscopy on XCH4 retrievals. The
spectral fitting residuals in the 2.35 µm band, however,
reveal a clearly systematic pattern, which is in particular correlated
with H2O absorption lines.
Here, we aim at mapping spectroscopic errors into XCH4
retrieval errors for an S5- and S5P-like observer on the global scale
in order to assess whether error patterns are significant in magnitude
and whether they are correlated among regional spatial and seasonal
temporal scales. Such correlations are particularly detrimental for
surface flux inversions since they can be readily mistaken for
a regional or seasonal flux pattern, unlike random noise errors that
cancel themselves out on the aggregated scales. To this end, the global ensemble of
simulated measurements used previously by is
revisited by replacing the light-path perturbation through
a perturbation due to imperfect spectroscopy. Thereby the
spectroscopic perturbation is estimated from fitting residuals to
observations of a direct-sun viewing, ground-based Fourier transform
spectrometer (FTS) operating at very high spectral
resolution. Submitting the perturbed satellite spectra to the
retrieval algorithm (which is not aware of the perturbation) allows
for assessing the residual XCH4 forward model error due to
imperfect spectroscopy.
This paper is organized as follows. Section
describes the retrieval algorithm and the general properties of the
S5P and S5 trial ensemble. Section describes the
ground-based FTS measurements and introduces the method – and its
assumptions – to generate a spectroscopic perturbation among the
satellite trial ensemble. Section discusses the
spectroscopy-induced XCH4 retrieval errors, and
Sect. concludes the study.
Satellite retrieval and trial ensemble
Characteristics of simulated measurements and retrieval simulations.
We investigate three retrieval configurations (SW1, SW3, and SW1+3) that take
into account the possible combinations of band SWIR1 and SWIR3. For each
channel, the signal to noise ratio (SNR) is modeled according to SNR =aR/aR+b with R the backscattered radiance in units
[photons ⋅ s-1 cm-2 sr-1 nm-1] and empirical
parameters a and b (included on the Table as SNR-a and SNR-b). The
full width at half maximum (FWHM)
defines the width of the Gaussian instrument response function which is
sampled by 2.65 pixels for each band.
NameUsed spectralUsed spectralTargetSW1SW3SW-1+3SNR-aSNR-bFWHMrange [nm]range [cm-1]absorbersSWIR11610–16755970–6300CH4, CO2, H2O✓✓2.132×10-7414 5780.24 nm(divided in two windows) SWIR32305–23854200–4325CH4, CO✓✓2.141×10-7248 8360.24 nm
Remote sensing of atmospheric parameters in general requires a forward
model F that relates the retrieval parameters included in the
state x (with xj the jth retrieval parameter) with the
measurements y (with yi the ith spectral element):
y=F(x)+ϵy+ϵF,
with ϵy the noise error due to detector noise (for
example) and ϵF the forward model error due to
approximate description of the relevant physics or due to errors of
parameters feeding F (for example). Here, we intentionally introduce
a well-defined spectroscopy-related forward model error
ϵF as described in Sect. .
The simulated measurements y are spectra of backscattered
sunlight in the SWIR spectral range. Thereby, instrument properties
are implemented according to the S5 instrument characteristics
summarized in Table . S5 covers spectral bands
from the UV to the SWIR but here, we focus
on the SWIR bands around 1.6 µm (named henceforth SWIR1)
and 2.3 µm (named henceforward SWIR3; in the early phase
of the mission, SWIR2 had been assigned to a channel around
2.0 µm, which was dropped later). The finite spectral
resolution of the spectrometers is modeled by a Gaussian instrument
response function (ISRF) with 0.24 nm width (full width at
half maximum (FWHM)). Measurement noise is calculated from
a parametric model that considers both signal-dependent contributions
such as photoelectron shot-noise and signal-independent contributions
such as dark-current noise. The typical signal to noise ratio (SNR) is
on the order of several hundreds for the SWIR bands. Being S5's
precursor, S5P features similar instrument characteristics but does
not include of the SWIR1 channel around 1.6 µm.
The forward model F(x) employed here is a variant
of the “RemoTeC” algorithm similar to the method used in
. RemoTeC is designed to retrieve XCH4
(and XCO2) for solar backscatter spectra in the SWIR
spectral range such as collected by GOSAT, the Orbiting Carbon
Observatory (OCO-2), S5P and S5. In its standard setup, the algorithm
is able to simulate backscattered radiances in particle-loaded
atmospheres taking into account light-path modification by
scattering. Here, we focus on the evaluation of spectroscopic
errors. Therefore, our study uses a variant of RemoTeC that neglects
scattering by aerosols and particles, and the measured spectrum
depends only on the absorption properties of the target and
interfering absorbers described in Table . The
estimation of those absorption properties relies on HITRAN-2008
spectroscopic parameters assuming a Voigt
line shape. For the water vapor on the SWIR3, the line list described on
the reference is used. It should be noticed,
however, that for line-shape parameters of CH4 and H2O in
the SWIR1 and SWIR3 regions, data in HITRAN-2008 and HITRAN-2012
have significant uncertainty because only a subset of the absorption lines was
accurately measured or calculated. We refer to for a detailed
description. Neglecting refined line-shape effects
(line mixing, speed dependence and Dicke narrowing) could also lead to gas
retrieval errors
.
Furthermore, the SWIR1 region in HITRAN-2008 and HITRAN-2012 is still not
fully characterized, for both line positions and line intensities, compared
to other longer wavelength regions
; detailed assignment and lower
state energy are not known in many cases affecting line intensity calculations
at temperatures other than 296 K. Further experimental and theoretical
investigations of this spectral region are presently underway
.
The spectra modeled by RemoTeC are convolved by the satellite's ISRF,
and noise is added as described above to simulate S5- and S5P-like
measurements. Section explains how an extra error due to
spectroscopic deficiencies is generated and added to the
measurements.
The ensemble of scenes for which we perform retrieval simulations is
the same as the one described in detail by Butz
et al. (2010, 2012). While our former studies focus on errors
induced by aerosol and cirrus scattering, we neglect such effects here; therefore we assume all scenes to be free of scattering particles. The ensemble
covers 1 day in each of the following months: January, April, July, and October, respectively, for which we collect
atmospheric absorption and surface reflection properties on
an ∼3∘×3∘ latitude × longitude
grid. Surface albedo in SWIR1 and SWIR3 is assembled from the MODIS
land albedo product and a database generated from SCIAMACHY's
2350 nm channel . Meteorological
parameters and the abundances of the relevant atmospheric absorbers
listed in Table are taken from models
(CarbonTracker for CO2, TM4 for
CH4 and CO , ECHAM5-HAM for
H2O, temperature and pressure, ).
Given the simulated measurements y, RemoTeC uses an inverse
method based on Philipps–Tikhonov regularization
e.g., to estimate the state vector x
from Eq. (). The state vector elements are the
12-layer vertical profiles of CH4 (and CO2 partial
column concentrations when SWIR1 band is covered), the total column
concentrations of the interfering absorbers H2O, and CO, and
surface reflection parameters (per channel). To find x, the
inverse method minimizes the cost-function J given by
J(x)=Sy-1/2(F(x)-y)2+γ‖W(x-xa)‖2,
where xa is the a priori state vector, Sy is
the diagonal error covariance matrix, W is the
regularization matrix, and γ is the regularization parameter
chosen such that it allows for about 1 degree of freedom for the
CH4 (and CO2) vertical profiles. The regularization
matrix W=LTL is assembled from the
discrete first-order difference operator L for the
CH4 (and CO2) vertical profiles and vanishes for all
other state vector elements.
Once the state vector solution x^ is found it may be
written in linear approximation as a combination of the true state
xtrue, the a priori, and the error contributions,
x^=Axtrue+(I-A)xa+Gϵy+GϵF,
where A is the averaging kernel and G is the
contribution or gain matrix . For our
simulations the true state is identical to the a priori
(xtrue=xa) and Eq. () reduces to
x^=xtrue+Gϵy+GϵF.
Defining an operator hT that selects the CH4
partial
columns from the state vector, adds them up and divides by the total
dry air column yields the retrieved dry air mole fraction
XCH4=hTx^=hTxtrue+hTGϵy+hTGϵF=ctrue+Δcy+ΔcF.
Since the true state (xtrue and ctrue)
and the noise realization (ϵy and Δcy) are
known, we can evaluate the targeted XCH4 forward model error
ΔcF by retrieving XCH4 from the simulated
measurements and subtracting ctrue and Δcy.
Generating forward model errors
The first step in generating the spectroscopic forward model error for
the satellite retrieval simulations is selecting a set of spectra
recorded by the ground-based, direct-sun viewing FTS located at the Darwin
(Australia) TCCON station and operated by University of Wollongong.
The instrument, Bruker 125HR, provides spectral coverage in all
absorption bands relevant here (see Table ).
Such ground-based FTS measurements have been used in previous studies
for validating other ground-based instruments and
for comparisons to satellite retrievals of XCH4 and XCO2e.g.,. The FTS-measured atmospheric
transmittance spectra are iteratively fitted by a variant of the RemoTeC
algorithm. Essentially, our approach follows the methods and analyses in
. Details can be found there. The approach is conceptually analogous to
regularly operated TCCON stations and verified by a comparison between the
GFIT algorithm and RemoTeC. The adjusted parameters
include the vertical profiles of CH4 and the relevant interfering
species such as H2O, CO2, CO, and a background baseline
transmittance. Assuming that the residual spectra (difference between
the measured and the iteratively adjusted modeled spectrum) are dominated
by spectroscopic errors, the residual spectra serve as forward model
error perturbation ϵF for the satellite retrieval
simulations.
FTS transmittance spectrum in SWIR1 (upper panel), residual
transmittance at FTS spectral resolution (first middle panel) and residual
transmittance at S5/S5P spectral resolution (second lower panel). The last two panels show the average offour
illustrative humid spectra (reddish lines) and four illustrative dry spectra
(bluish lines) at FTS and S5/S5P spectral resolutions. The water vapor
absorption lines (with line intensity ≥10-26 [molec cm-2]) are
shown with blue vertical stacks. The methane absorption lines (with line
intensity ≥10-23 [molec cm-2]) are shown with magenta vertical
stacks.
FTS transmittance spectrum in SWIR3 (upper panel), residual
transmittance at FTS spectral resolution (first middle panel) and residual
transmittance at S5/S5P spectral resolution (second lower panel). The last two panels show the average of four
illustrative humid spectra (reddish lines) and four illustrative dry spectra
(bluish lines) at FTS and S5/S5P spectral resolutions. The water vapor
absorption lines (with line intensity ≥10-26 [molec cm-2]) are
shown with blue vertical stacks. The methane absorption lines (with line
intensity ≥10-23 [molec cm-2]) are shown with magenta vertical
stacks.
The methodology we introduce here assumes that the perturbation
Δτ derived from the FTS residuals is dominated by
deficiencies of the employed spectroscopic parameters and models. This
assumption appears justified by the use of state-of-the-art
instrumentation and retrieval techniques with a proven performance
record. Further, the FTS residuals represent only a fraction of the
actual spectroscopic errors, i.e., those which cannot be compensated by the
free parameters of the FTS fitting routine such as CH4 and
H2O abundances. In that sense, the estimated perturbation is
an optimistic interpretation of spectroscopic errors.
For a ground-based, direct-sun viewing observer in a plane-parallel
atmosphere, the monochromatic atmospheric transmittance
Tgb recorded can be written,
Tgb(τ)=Igb(τ)ES=exp-τcosαgb,
where Igb is the observed radiance, ES is the solar
irradiance at top-of-the-atmosphere, αgb is the solar
zenith angle of the ground-based sounding, and τ is the molecular
absorption optical thickness integrated along the zenith direction
(i.e., along the vertical). For simplicity, we neglect scattering
processes due to molecules and particles. The processing chain of the
ground-based FTS measurements provides a best fit Tgb, mod
to the observed transmittance spectra Tgb, true. The
corresponding mismatch
ΔT=Tgb, true-Tgb, mod
is termed the FTS fitting residual to be used for perturbing our
simulated satellite retrievals. Figures and show the FTS measured transmittance T and
the fitting residual ΔT. Our study uses 50 different FTS
spectra recorded at different humidity conditions .
The FTS operates at very high spectral resolution such that the measured
residual ΔT is approximately equal to the monochromatic residual.
Further assuming that the FTS fitting residual is caused by errors
in spectroscopic parameters, we can evaluate Eq. (),
ΔT=exp-τtruecosαgb-exp-τmodcosαgb=Tgb, modexp-Δτcosαgb-1,
with Δτ=τtrue-τmod. Thus, given
the FTS residual ΔT, the FTS transmittance Tgb,
mod, and the FTS solar zenith angle αgb, we
can calculate a perturbation Δτ of the vertical absorption
optical thickness
Δτ=-cosαgblnΔTTgb, mod+1.
In the next step, the perturbation derived from the ground-based
spectra needs translation into a perturbation of the satellite
observations. In a non-scattering atmosphere, the reflectance
Rsat observed from a downward-looking space-borne observer
is given by
Rsat(τ)=Isat(τ)ES=Acosαsatπexpτcosαsat+τcosθsat,
where Isat is the reflected radiance, A is the ground
albedo, αsat is the solar zenith angle and
θsat is the satellite viewing zenith angle (assumed
θsat=0∘, nadir-viewing in our simulation
exercise). Replacing the absorption optical thickness τ in
Eq. () by a perturbed optical thickness
τper=τmod+Δτ yields the perturbed
satellite measurement.
Up to here we assume monochromatic light, but in order to introduce
the perturbed satellite measurement in the retrieval algorithm we have
to take in account the satellite spectral resolution. Therefore, if
the satellite retrieval is not aware of this perturbation, the
spectroscopic forward model error ϵF amounts
to
ϵF=(R⋅Fsat)(τper)-(R⋅Fsat)(τ),
where (R⋅Fsat) represents the convolution of
the reflectance by the satellite's ISRF
(Fsat). The forward model error
ϵF results in the XCH4 retrieval
error ΔcF to be evaluated.
Seasonal XH2O concentrations (molecules cm-2). Latitudes
with solar zenith angles larger than 70∘ were
filtered.
Air mass factor (AMF) for the four months considered. Latitudes with
solar zenith angles larger than 70∘ were filtered.
Figures and reveal
variability in Δτ derived from the two different FTS
measurements. Typically, the fitting residuals are larger for wetter than for dryer days. To take into account the dependence on water
vapor within the ensemble, the perturbation Δτ for each
simulated scene is estimated by interpolating linearly between the
perturbations derived from the 50 FTS measurements Δτ(XH2O), where the interpolation variable is the total column
water vapor concentration XH2O. The effect of the different
viewing geometries is implicitly taken into account by attributing the
spectroscopic perturbation to the vertical absorption optical
thickness. Figures and show how
XH2O and the air mass factor (AMF) vary among our trial
ensemble. AMF for the satellite geometry is defined as
AMFsat=1cosαsat+1cosθsat=1cosαsat+1,
while the AMF for the ground-based FTS measurements is
defined as
AMFgb=1cosαgb.
The satellite soundings are assumed nadir-viewing
(θsat=0∘) with solar zenith angles up to
αsat=70∘, i.e., AMFsat ranges
between 2 and 3.9. The XH2O range covered by the FTS
measurements is reasonably large (factor 14 between the low and the
high value) that we are confident extrapolating to the actual
XH2O value of the simulated scene. Dependencies of
Δτ on other geophysical variables such as the CH4
and CO2 concentrations are neglected, in particular since
these concentrations show comparatively little variability in the
atmosphere.
Additionally, three processing steps are carried out. First we
determine a small spectral shift between the ground-based and the
satellite spectra by comparing the FTS transmittance Tgb
to simulated satellite soundings at very high instrument
resolution. Second, all the FTS measurements are interpolated to the
same spectral grid with a resolution of 0.007 cm-1. Third,
to avoid spurious large values of Δτ in the vicinity of
optically thick absorption lines (Tgb→0 in
Eq. ), we adopt a minimum for Tgb equal
to the 1-σ noise level of the FTS spectra.
XCH4 retrieval error ΔcF/ctrue[%] for
retrieval concept SW1+3 (both SWIR1 and SWIR3
bands).
Bi-dimensional histograms of methane retrieval
error (%) with respect to XH2O total concentration values.
This section discusses the spectroscopic XCH4 retrieval
errors (ΔcF) for the three retrieval configurations
(SW1, SW3, SW1+3) introduced in
Table . Thereby, SW3 (covering SWIR3 only) can
be considered representative for the S5P setup, SW1+3 (covering SWIR1
and SWIR3), and SW1 (covering SWIR1 only) are possible strategies for
S5. Figures through
show the residual XCH4
retrieval errors when introducing the spectroscopic perturbation in
our global trial ensemble covering 1 day in each of the following months: January, April, July,
and October, respectively. Overall the induced retrieval errors are in the range of
a few tens ppb, which is relevant in the view of S5's and S5P's error
budget.
The SW1 configuration (Fig. ) yields an
overall overestimation of the true XCH4 over the tropics, while
in midlatitudes it yields slight underestimation. The retrieval errors
are consistently around 0.7 % larger in the tropics than in
mid-to-high latitudes, and the latitudinal pattern of the bias persists
over all seasons but is less pronounced for July when the sun is high
in the sky. The observed latitudinal correlation appears driven by
the dependence of the AMF on latitude and season. Similar patterns have
been detected in real XCH4 retrievals from SCIAMACHY's SWIR1 band
though SCIAMACHY exhibited much coarser spectral resolution than the
soundings simulated here. , for example,
assume a latitudinal and monthly bias correction for SCIAMACHY XCH4
to reconcile their source estimates driven by the satellite retrievals and
by in situ flask samples. The SW3 configuration
(Fig. ) yields XCH4 errors that are
spatially and temporally variable between roughly -0.3 and 1.2 %.
The error patterns are less correlated with the variation in AMF but
tentatively correlate with the variation of total column water vapor
XH2O. Persistently dry scenes such as the desert areas show very
small XCH4 errors while the seasonally humid midlatitudes reveal
regionally and seasonally variable errors. The tropics, however, show
overall small variability of spectroscopy-induced XCH4 errors.
The combined configuration SW1+3 (Fig. )
yields XCH4 error patterns that combine the characteristics
observed for SW1 and SW3. The latitudinal dependence of residual errors
shows up through a general overestimation of XCH4 in the tropics.
In the midlatitudes, a pronounced dependence on the water vapor column
overwrites the latitudinal signal.
To illustrate the dependence of the XCH4 errors on
XH2O, Fig. shows the
correlation between the simulated errors and the water vapor content
of the scene. The correlation confirms the above observation that SW1
yields XCH4 that is less affected by interference from
XH2O than SW3 but still dry scenes over Siberia and humid
ones over the tropics correlate with XCH4 errors. SW3
retrievals, however, suffer from a strong interference from water
vapor, which results in underestimation of XCH4 for very dry
scenes, an increasing overestimation for increasingly humid case and
then, a decreasing interference from very humid cases. The complicated
structure of overlapping CH4 and H2O absorption lines
in SWIR3 (Fig. ) renders such interferences
likely. Their detailed mapping on XCH4 retrieval errors,
however, largely depends on the choice of the spectral windows and the
spectral resolution of the instrument. The SW1+3 retrievals correlate
with water vapor abundances for dry and moderately humid cases but
show less dependence on very humid conditions.
These results are consistent with the current status of CH4
and H2O spectroscopy in HITRAN-2008/2012. For both SWIR3 and SWIR1,
the situation is very challenging for line-shape parameters, namely
line broadening. The SWIR3 region being more intense, and given the
large number of CH4 and H2O lines in this region,
satellite retrievals from SWIR3 are more affected by air-broadening
errors than retrievals from SWIR1. A second reason that may explain
the differences between SWIR1 and SWIR3 is that, for SWIR1, there are
dedicated studies providing effective Voigt line-shape parameters
which lead to the
smaller transmittance residuals shown in Fig.
compared to Fig. .
Discussion and conclusion
The goals of Sentinel 5 and the Sentinel 5 Precursor concerning
XCH4 retrievals demand a total accuracy better than 2 %
(around 30 ppb) in order to allow for successful source and
sink estimates on regional and seasonal scales
. Uncertainties due to noise are expected
to be in the range of 0.1 % (around 2–3 ppb). Forward
model errors are present due to imperfect correction of light-path
modification driven by particle scattering . The
direct consequence is that additional forward model errors (e.g., due to
spectroscopic deficiencies) can jeopardize the desired performance. Our
assessment estimates such spectroscopy-induced XCH4
retrieval errors for a global and seasonal ensemble of simulated S5-
and S5P-like satellite soundings.
The key assumption of our approach is that a realistic spectroscopic
perturbation can be derived from spectral fitting residuals of
a ground-based, direct-sun viewing FTS. This assumption can be
criticized in two ways: (1) the FTS fitting residual contains only
that part of the spectroscopic errors that cannot be accounted for
through the free parameters of the FTS fit, i.e., only the part of the
spectroscopic errors that are in the null-space
of the FTS retrieval; (2) the fitting
residual contains errors due to other sources than spectroscopy. While
flaw (1) would generate overly optimistic XCH4 errors, flaw
(2) would generate overly pessimistic error patterns or an attribution
to the wrong error sources. Since the FTS operates at a spectral
resolution that allows for fully resolving the atmospheric absorption
lines, we expect flaw (1) to be small. Flaw (2) is battled by using an
FTS instrument and data reduction methods with demonstrated
state-of-the-art performance. Ground-based FTS records such as those exploited here, have been used in the past to evaluate spectroscopic
parameters e.g.,.
Translating the ground-based FTS fitting residuals into our satellite
sounding ensemble, we consider dependencies on the air mass factor and
atmospheric water vapor content but neglect dependencies on other
variables such meteorological variables or the CH4 abundance
itself. This choice renders parameter space treatable and largely
follows previous studies that found water vapor interferences
and latitudinal biases
(potentially driven by viewing geometry dependencies)
to be the dominating error patterns in
XCH4 from space-borne sensors.
However, our study only examines the standard configurations currently
foreseen for CH4 retrievals from S5 and S5P. The residual
spectroscopic errors found here might be mitigated by selecting narrower
spectral windows to avoid spectroscopic interferences. For example, we
conducted a sensitivity study that omits the CH4 Q-branch in SWIR-1
from the retrievals. The Q-branch (at about 6005 cm-1) consists of a
manifold of densely spaced absorption lines that are hard to separate in order to determine spectroscopic parameters and line shapes. Cutting the Q-branch,
however, shifts the residual XCH4 errors in the SW1 configuration to
negative values (underestimation), but the range of errors is not reduced
substantially. A further strategy to avoid H2O absorption interfering
with the targeted CH4 lines could be to retrieve the vertical profile
of H2O instead of the total column. The retrieved H2O profile
would be unrealistic, but the retrieval would gain freedom to compensate
wrong H2O spectroscopy by vertical oscillations. Since H2O is
not the target parameter a wrong H2O profile shape would do no harm
to S5 and S5P's goal to accurately estimate CH4 concentrations. Since
such an assessment would imply major changes to our inverse method, we defer
it to future studies.
Our retrieval simulations indicate that the spectroscopy-induced XCH4
retrieval errors are significant, both in magnitude and in their
spatiotemporal correlation structure. While retrievals from the SWIR1 band
(SW1) show a moderate correlation with latitude and water vapor, XCH4
retrievals from SWIR3 suffer from interferences with water vapor absorption.
The observed correlated error patterns generally amount to a few tens ppb,
which would jeopardize the usefulness of the XCH4 retrievals for
inverse modeling of sources/sinks at the surface.
Acknowledgements
This research was funded by the European Space Agency (ESA) through
the Consolidation of S5-SWIR requirements project: RfQ
3-13741/12/NL/CT/lf and by Deutsche Forschungsgemeinschaft (DFG)
through the Emmy Noether Programme, grant BU2599/1-1 (RemoteC). The
authors would like to thank Manfred Birk and Georg Wagner from DLR
for helpful discussions concerning the application of
high-resolution atmospheric transmission residuals recorded with
ground-based FTIR spectrometers for the estimation of resulting
retrieval biases of satellite sensors. The authors thank to I. Aben,
Christian Frankenberg
and one anonymous reviewer for their comments and suggestions.
We also acknowledge the
support by Deutsche Forschungsgemeinschaft and the Open Access
Publishing Fund of Karlsruhe Institute of
Technology.
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Edited by: H. Worden
ReferencesBasu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I.,
Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B.,
Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT
retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717,
10.5194/acp-13-8695-2013, 2013.Bergamaschi, P., Frankenberg, C., Meirink, J.
F., Krol, M., Dentener, F., Wagner, T., Platt, U., Kaplan, J. O., Körner,
S., Heimann, M., Dlugokencky, E. J., and Goede, A.: Satellite cartography of
atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on
inverse model simulations, J. Geophys. Res-Atmos., 112, D02304,
10.1029/2006JD007268, 2007.Bergamaschi, P.,
Frankenberg, C., Meirink, J. F., Krol, M., Villani, M. G.,
Houweling, S., Dentener, F., Dlugokencky, E. J.,
Miller, J. B., Gatti, L. V., Engel, A., and Levin, I.:
Inverse modeling of global and regional CH4 emissions using
SCIAMACHY satellite retrievals, J. Geophys. Res., 114, 22301,
10.1029/2009JD012287, 2009. Bovensmann, H., Burrows, J.,
Buchwitz, M., Frerick, J., Noël, S., Rozanov, V., Chance, K.,
and Goede, A.: SCIAMACHY: mission objectives and measurement
modes, J. Atmos. Sci., 56, 127–150, 1999.Brown, L., Sung, K., Benner, D.,
Devi, V., Boudon, V., Gabard, T., Wenger, C., Campargue, A.,
Leshchishina, O., Kassi, S., Mondelain, D., Wang, L., Daumont, L.,
Régalia, L., Rey, M., Thomas, X., Tyuterev, V. G., Lyulin, O.,
Nikitin, A., Niederer, H., Albert, S., Bauerecker, S., Quack, M.,
O'Brien, J., Gordon, I., Rothman, L., Sasada, H., Coustenis, A.,
Smith Jr., M. T. C., Wang, X.-G., Mantz, A., and Spickler, P.:
Methane line parameters in the HITRAN2012
database, J. Quant. Spectrosc. Ra., 130, 201–219,
10.1016/j.jqsrt.2013.06.020, 2013.Buchwitz, M., Reuter, M., Bovensmann, H., Pillai, D., Heymann, J.,
Schneising, O., Rozanov, V., Krings, T., Burrows, J. P., Boesch, H., Gerbig,
C., Meijer, Y., and Löscher, A.: Carbon Monitoring Satellite (CarbonSat):
assessment of atmospheric CO2 and CH4 retrieval errors by error
parameterization, Atmos. Meas. Tech., 6, 3477–3500,
10.5194/amt-6-3477-2013, 2013.Butz, A., Bösch, H., Camy-Peyret, C., Chipperfield, M. P., Dorf, M.,
Kreycy, S., Kritten, L., Prados-Román, C., Schwärzle, J., and
Pfeilsticker, K.: Constraints on inorganic gaseous iodine in the tropical
upper troposphere and stratosphere inferred from balloon-borne solar
occultation observations, Atmos. Chem. Phys., 9, 7229–7242,
10.5194/acp-9-7229-2009, 2009.Butz, A.,
Hasekamp, O. P., Frankenberg, C., Vidot, J., and Aben, I.:
CH4 retrievals from space-based solar backscatter
measurements: performance evaluation against simulated aerosol and
cirrus loaded scenes, J. Geophys. Res., 115, 24302,
10.1029/2010JD014514, 2010.Butz, A., Guerlet, S.,
Hasekamp, O., Schepers, D., Galli, A., Aben, I.,
Frankenberg, C., Hartmann, J.-M., Tran, H., Kuze, A.,
Keppel-Aleks, G., Toon, G., Wunch, D., Wennberg, P.,
Deutscher, N., Griffith, D., Macatangay, R.,
Messerschmidt, J., Notholt, J., and Warneke, T.: Toward
accurate CO2 and CH4 observations from GOSAT,
Geophys. Res. Lett., 38, L14812,
10.1029/2011GL047888, 2011.Butz, A., Galli, A., Hasekamp, O.,
Landgraf, J., Tol, P., and Aben, I.: TROPOMI aboard Sentinel-5
Precursor: prospective performance of CH4 retrievals for
aerosol and cirrus loaded atmospheres, Remote Sens. Environ.,
120, 267–276,
10.1016/j.rse.2011.05.030, 2012.Chevallier, F.,
Bréon, F.-M., and Rayner, P. J.: Contribution of the
Orbiting Carbon Observatory to the estimation of CO2
sources and sinks: theoretical study in a variational data
assimilation framework, J. Geophys. Res., 112, 9307,
10.1029/2006JD007375, 2007.Frankenberg, C., Meirink, J. F., van Weele, M., Platt, U.,
and Wagner, T.: Assessing methane emissions from global
space-borne observations, Science, 308, 1010–1014,
10.1126/science.1106644, 2005.Frankenberg, C., Bergamaschi, P., Butz, A., Houweling, S.,
Meirink, J. F., Notholt, J., Petersen, A. K., Schrijver, H.,
Warneke, T., and Aben, I.: Tropical methane emissions: a revised
view from SCIAMACHY onboard ENVISAT, Geophys. Res. Lett.,
35, L15811,
10.1029/2008GL034300, 2008a.Frankenberg, C., Warneke, T., Butz, A., Aben, I., Hase, F., Spietz, P., and
Brown, L. R.: Pressure broadening in the 2v3 band of methane and its
implication on atmospheric retrievals, Atmos. Chem. Phys., 8, 5061–5075,
10.5194/acp-8-5061-2008, 2008b.Galli, A., Butz, A., Scheepmaker, R. A., Hasekamp, O., Landgraf, J., Tol, P.,
Wunch, D., Deutscher, N. M., Toon, G. C., Wennberg, P. O., Griffith, D. W.
T., and Aben, I.: CH4, CO, and H2O spectroscopy for the Sentinel-5
Precursor mission: an assessment with the Total Carbon Column Observing
Network measurements, Atmos. Meas. Tech., 5, 1387–1398,
10.5194/amt-5-1387-2012, 2012.Ghysels, M.,
Gomez, L., Cousin, J., Tran, H., Amarouche, N., Engel, A.,
Levin, I., and Durry, G.: Temperature dependences of
air-broadening, air-narrowing and line-mixing coefficients of the
methane ν3 R6 manifold lines – application to in-situ
measurements of atmospheric
methane, J. Quant. Spectrosc. Ra., 133, 206–216,
10.1016/j.jqsrt.2013.08.003, 2014.Gisi, M., Hase, F., Dohe, S.,
Blumenstock, T., Simon, A., and Keens, A.:
Gisi, M., Hase, F., Dohe, S., Blumenstock, T., Simon, A., and Keens, A.:
XCO2-measurements with a tabletop FTS using solar absorption spectroscopy,
Atmos. Meas. Tech., 5, 2969–2980, 10.5194/amt-5-2969-2012, 2012.Guerlet, S., Butz, A., Schepers, D., Basu, S.,
Hasekamp, O. P., Kuze, A., Yokota, T., Blavier, J.-F., Deutscher, N. M.,
Griffith, D. W.-T., Hase, F., Kyro, E., Morino, I., Sherlock, V., Sussmann,
R., Galli, A., and Aben, I.: Impact of aerosol and thin cirrus on retrieving
and validating XCO2 from GOSAT shortwave infrared measurements, J.
Geophys. Res.-Atmos., 118, 4887–4905, 10.1002/jgrd.50332, 2013. Hansen, P. C.: Rank-Deficient
and Discrete Ill-Posed Problems: Numerical Aspects of Linear
Inversion, SIAM – Monographs on Mathematical Modeling and
Computation 4, Chapter 5, Philadelphia, USA, 1998.Ingmann, P.,
Veihelmann, B., Langen, J., Lamarre, D., Stark, H., and
Courrèges-Lacoste, G. B.: Requirements for the GMES atmosphere
service and ESA's implementation concept: Sentinels-4/-5 and-5p,
Remote Sens. Environ., 120, 58–69,
10.1016/j.rse.2012.01.023,
2012.Kirschke,
S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G., Dlugokencky, E.
J., Bergam- aschi, P., Bergmann, D., Blake, D. R., Bruhwiler, L.,
Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E., L., Houweling, S., Josse, B., Fraser, P. J.,
Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quere, C., Naik, V.,
O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R.
G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, 10.1038/ngeo1955, 2013.Kuze, A., Suto, H., Nakajima, M., and
Hamazaki, T.: Thermal and near infrared sensor for carbon
observation Fourier-transform spectrometer on the greenhouse gases
observing satellite for greenhouse gases monitoring, Appl. Optics,
48, 6716–6733,
10.1364/AO.48.006716, 2009.Meirink, J. F., Bergamaschi, P., and Krol, M. C.: Four-dimensional
variational data assimilation for inverse modelling of atmospheric methane
emissions: method and comparison with synthesis inversion, Atmos. Chem.
Phys., 8, 6341–6353, 10.5194/acp-8-6341-2008, 2008.Miller, C. E., Crisp, D.,
DeCola, P. L., Olsen, S. C., Randerson, J. T.,
Michalak, A. M., Alkhaled, A., Rayner, P., Jacob, D. J.,
Suntharalingam, P., Jones, D. B. A., Denning, A. S.,
Nicholls, M. E., Doney, S. C., Pawson, S., Bösch, H.,
Connor, B. J., Fung, I. Y., O'Brien, D., Salawitch, R. J.,
Sander, S. P., Sen, B., Tans, P., Toon, G. C.,
Wennberg, P. O., Wofsy, S. C., Yung, Y. L., and Law, R. M.:
Precision requirements for space-based XCO2
data, J. Geophys. Res., 112, 10314,
10.1029/2006JD007659,
2007.Nikitin, A., Lyulin, O.,
Mikhailenko, S., Perevalov, V., Filippov, N., Grigoriev, I.,
Morino, I., Yokota, T., Kumazawa, R., and Watanabe, T.: GOSAT-2009
methane spectral line list in the
5550–6236 cm-1
range, J. Quant. Spectrosc. Ra., 111, 2211–2224,
10.1016/j.jqsrt.2010.05.010, 2010.Nikitin, A.,
Boudon, V., Wenger, C., Albert, S., Brown, L., Bauerecker, S., and
Quack, M.: High resolution spectroscopy and first global analysis
of the Tetradecad region of methane 12CH4,
Phys. Chem. Chem. Phys., 15, 10071–10093,
10.1039/C3CP50799H, 2013.O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C.,
Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J.,
Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T.,
Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval
algorithm – Part 1: Description and validation against synthetic
observations, Atmos. Meas. Tech., 5, 99–121, 10.5194/amt-5-99-2012,
2012.Oshchepkov, S., Bril, A., and
Yokota, T.: PPDF-based method to account for atmospheric light
scattering in observations of carbon dioxide from
space, J. Geophys. Res., 113, 23210,
10.1029/2008JD010061,
2008.Palmer, P. I., Feng, L., and Bösch, H.: Spatial resolution of tropical
terrestrial CO2 fluxes inferred using space-borne column CO2 sampled in
different earth orbits: the role of spatial error correlations, Atmos. Meas.
Tech., 4, 1995–2006, 10.5194/amt-4-1995-2011, 2011.Peters, W.,
Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G.,
Hirsch, A. I., Worthy, D. E. J., van der Werf, G. R.,
Randerson, J. T., Wennberg, P. O., Krol, M. C., and
Tans, P. P.: An atmospheric perspective on North American Carbon
Dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104,
18925–18930,
10.1073/pnas.0708986104, 2007.Reuter, M., Buchwitz, M., Schneising, O., Heymann, J., Bovensmann, H., and
Burrows, J. P.: A method for improved SCIAMACHY CO2 retrieval in the
presence of optically thin clouds, Atmos. Meas. Tech., 3, 209–232,
10.5194/amt-3-209-2010, 2010. Rodgers, C.: Inverse
Methods for Atmospheric Sounding: Theory and Practice, Series on
Atmospheric Oceanic and Planetary Physics, vol. 2, World Scientific
Publishing Company, Chapters 3 and 5, River Edge, NJ, USA, 2000.
Rothman, L. S., Gordon, I. E., Barbe, A., Benner, D. C., Bernath, P.
F., Birk, M., Boudon, V., Brown, L. R., Campargue, A., Champion, J.,
Chance, K., Coudert, L. H., Dana, V., Devi, V. M., Fally,
S., Flaud, J., Gamache, R. R., Goldman, A., Jacquemart, D.,
Kleiner, I., Lacome, N., Lafferty, W. J., Mandin, J., Massie, S.
T., Mikhailenko, S. N., Miller, C. E., Moazzen-Ahmadi, N., Naumenko, O. V.,
Nikitin, A. V., Orphal, J., Perevalov, V. I., Perrin, A., Predoi-Cross, A.,
Rinsland, C. P., Rotger, M., Rotger, M., Šimecková, M., Smith, M. A. H.,
Sung, K., Tashkun, S. A., Tennyson, J., Toth, R. A., Vandaele, A. C., and
Vander Auwera, J.: The HITRAN 2008 molecular spectroscopic
database, J. Quant. Spectrosc. Radiat. Transfer, 110, 533–572,
2009.Rothman, L. S., Gordon,
I. E., Babikov, Y., Barbe, A., Chris Benner, D., Bernath, P. F., Birk, M.,
Bizzocchi, L., Boudon, V., Brown, L. R., Campargue, A., Chance, K., Cohen, E.
A., Coudert, L. H., Devi, V. M., Drouin, B. J., Fayt, A., Flaud, J.-M.,
Gamache, R. R., Harrison, J. J., Hartmann, J.-M., Hill, C., Hodges, J. T.,
Jacquemart, D., Jolly, A., Lamouroux, J., Le Roy, R. J., Li, G., Long, D. A.,
Lyulin, O. M., Mackie, C. J., Massie, S. T., Mikhailenko, S., Müller, H.
S. P., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V., Perrin,
A., Polovtseva, E. R., Richard, C., Smith, M. A. H., Starikova, E., Sung, K.,
Tashkun, S., Tennyson, J., Toon, G. C., Tyuterev, Vl. G., and Wagner, G.: The
HITRAN 2012 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 130,
4–50, 10.1016/j.jqsrt.2013.07.002, 2013.Scheepmaker, R. A., Frankenberg, C., Galli, A., Butz, A., Schrijver, H.,
Deutscher, N. M., Wunch, D., Warneke, T., Fally, S., and Aben, I.: Improved
water vapour spectroscopy in the 4174–4300 cm-1 region and its impact
on SCIAMACHY HDO/H2O measurements, Atmos. Meas. Tech., 6, 879–894,
10.5194/amt-6-879-2013, 2013.Schneising, O., Buchwitz, M., Burrows, J. P., Bovensmann, H., Bergamaschi,
P., and Peters, W.: Three years of greenhouse gas column-averaged dry air
mole fractions retrieved from satellite – Part 2: Methane, Atmos. Chem.
Phys., 9, 443–465, 10.5194/acp-9-443-2009, 2009.Schrijver, H., Gloudemans, A. M. S., Frankenberg, C., and Aben, I.: Water
vapour total columns from SCIAMACHY spectra in the 2.36 µm window,
Atmos. Meas. Tech., 2, 561–571, 10.5194/amt-2-561-2009, 2009.Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E., Wilson, J.,
Ganzeveld, L., Tegen, I., Werner, M., Balkanski, Y., Schulz, M., Boucher, O.,
Minikin, A., and Petzold, A.: The aerosol-climate model ECHAM5-HAM, Atmos.
Chem. Phys., 5, 1125–1156, 10.5194/acp-5-1125-2005, 2005.
Streets, D. G., Canty,
t., Carmichael, G. R., de Foy, B., Dickerson, R. R., Duncan, B. R., Ed-
wards, D. P., Haynes, J. A., Henze, D. A., Houyoux, M. R., Jacob, D. J.,
Krotkov, N. A., Lamsal, L. N., Liu, Y., Lu, Z., Martin, R. V., Pfister, G.
G., Pinder, R. W., Salawitch, R. J., and Wecht, K. J.: Emissions estimation
from satellite retrievals: a review of current capability, Atmos. Environ.,
77, 1011–1042, 10.1016/j.atmosenv.2013.05.051, 2013.Thompson, D. R., Benner, D. C.,
Brown, L. R., Crisp, D., Devi, V. M., Jiang, Y., Natraj, V.,
Oyafuso, F., Sung, K., Wunch, D., Castano, R., and Miller, C. E.:
Atmospheric validation of high accuracy CO2 absorption
coefficients for the OCO-2 mission, J. Quant. Spectrosc. Ra., 113,
2265–2276,
10.1016/j.jqsrt.2012.05.021, 2012.Tran, H.,
Hartmann, J., Toon, G., Brown, L., Frankenberg, C., Warneke, T.,
Spietz, P., and Hase, F.: The 2ν3 band of CH4
revisited with line mixing: consequences for spectroscopy and
atmospheric retrievals at
1.67 µm, J. Quant. Spectrosc. Ra., 111,
1344–1356,
10.1016/j.jqsrt.2010.02.015, 2010.Tyuterev, V.,
Tashkun, S., Rey, M., Kochanov, R., Nikitin, A., and Delahaye, T.:
Accurate spectroscopic models for methane polyads derived from
a potential energy surface using high-order contact
transformations, J. Phys. Chem. A, 117, 13779–13805,
10.1021/jp408116j, 2013.Veefkind, J. P.,
Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J.,
Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O.,
Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R.,
Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA
Sentinel-5 Precursor: a GMES mission for global observations of the
atmospheric composition for climate, air quality and ozone layer
applications, Remote Sens. Environ., 120, 70–83,
10.1016/j.rse.2011.09.027, 2012.