Monitoring CO2 from space is essential to characterize the
spatiotemporal distribution of this major greenhouse gas and quantify its
sources and sinks. The mixing ratio of CO2 to dry air can be derived
from the CO2/O2 column ratio. The O2 column is usually
derived from its absorption signature on the solar reflected spectra over
the O2 A band (e.g. Orbiting Carbon Observatory-2 (OCO-2), Thermal
And Near infrared Sensor for carbon Observation (TANSO)/Greenhouse
Gases Observing Satellite (GOSAT), TanSat). As a result of
atmospheric scattering, the atmospheric path length varies with the aerosols'
load, their vertical distribution, and their optical properties. The
spectral distance between the O2 A band (0.76 µm) and the
CO2 absorption band (1.6 µm) results in significant
uncertainties due to the varying spectral properties of the aerosols over
the globe.
There is another O2 absorption band at 1.27 µm with weaker lines
than in the A band. As the wavelength is much closer to the CO2 and
CH4 bands, there is less uncertainty when using it as a proxy of the
atmospheric path length to the CO2 and CH4 bands. This O2
band is used by the Total Carbon Column Observing Network (TCCON) implemented for the validation of
space-based greenhouse gas (GHG) observations. However, this absorption
band is contaminated by the spontaneous emission of the excited molecule
O2*, which is produced by the photo-dissociation of O3 molecules
in the stratosphere and mesosphere. From a satellite looking nadir, this
emission has a similar shape to the absorption signal that is used.
In the frame of the CNES (Centre National d'Études Spatiales – the French
National Centre for Space Studies) MicroCarb project, scientific studies have been
performed in 2016–2018 to explore the problems associated with this O2*
airglow contamination and methods to correct it. A theoretical synthetic
spectrum of the emission was derived from an approach based on A21
Einstein coefficient information contained in the line-by-line high-resolution
transmission molecular absorption (HITRAN) 2016
database. The shape of our synthetic spectrum is validated when compared to
O2* airglow spectra observed by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography
(SCIAMACHY)/Envisat in limb viewing.
We have designed an inversion scheme of SCIAMACHY limb-viewing spectra,
allowing to determine the vertical distribution of the volume emission rate
(VER) of the O2* airglow. The VER profiles and corresponding integrated
nadir intensities were both compared to a model of the emission based on the
Reactive Processes Ruling the Ozone Budget in the Stratosphere
(REPROBUS) chemical transport model. The airglow intensities depend mostly on
the solar zenith angle (both in model and data), and the model underestimates
the observed emission by ∼15 %. This is confirmed with SCIAMACHY
nadir-viewing measurements over the oceans: in such conditions, we have
disentangled and retrieved the nadir O2* emission in spite of the
moderate spectral resolving power (∼860) and found that the nadir
SCIAMACHY intensities are mostly dictated by solar zenith angle (SZA) and are larger than the model
intensities by a factor of ∼1.13. At a fixed SZA, the model airglow
intensities show very little horizontal structure, in spite of ozone
variations.
It is shown that with the MicroCarb spectral resolution power (25 000) and
signal-to-noise ratio (SNR), the contribution of the O2* emission at 1.27 µm to the
observed spectral radiance in nadir viewing may be disentangled from the
lower atmosphere/ground absorption signature with a great accuracy. Indeed,
simulations with 4ARCTIC radiative transfer inversion tool have shown that
the CO2 mixing ratio may be retrieved with the accuracy required for
quantifying the CO2 natural sources and sinks (pressure-level error
≤1 hPa; XCO2 accuracy better than 0.4 ppmv) with the
O2 1.27 µm band only as the air proxy (without the A band). As a result of
these studies (at an intermediate phase), it was decided to include this
band (B4) in the MicroCarb design, while keeping the O2 A band for
reference (B1). Our approach is consistent with the approach of Sun et al. (2018),
who also analysed the potential of the O2 1.27 µm band
and concluded favourably for GHG monitoring from space. We advocate for the
inclusion of this O2 band on other GHG monitoring future space
missions, such as GOSAT-3 and EU/European Space Agency (ESA) CO2-M missions, for a better GHG
retrieval.
Introduction
Carbon dioxide (CO2) is recognized as the main driver of human-induced
climate change. Its evolution in time is therefore scrutinized with
attention. We know how much CO2 is produced each year by human
activity, but it does not correspond to the measured yearly increase of
CO2 in the atmosphere. The atmospheric fraction is the ratio
of the atmospheric increase of CO2 mass to the mass of CO2 anthropogenic emission. On decadal
timescales, this ratio has been close to 0.5 since the beginning of
continuous measurements of atmospheric concentration in the late 1950s,
despite an increase of the anthropogenic emissions by a factor of 5 (Le
Quéré et al., 2018). An atmospheric fraction lower than 1 is
explained by the existence of natural sinks that are fuelled by the
increasing amount of CO2 in the atmosphere. The current global carbon
budget indicates that the ocean and land surface contribute roughly equally
to the sink. There is little doubt that the oceanic sink will continue in
the future despite a solubility decrease induced by raising temperature,
while the fate of the land sink is more uncertain (Ciais et al., 2013).
There is a lack of understanding of the vegetation dynamic, and its response
to increasing CO2 and changing climate. In fact, there is no consensus on
whether the land sink is mostly in the tropics, midlatitudes, or boreal
regions. This lack of understanding of the vegetation processes limits our
ability to anticipate the carbon budget and thus the rate of climate change.
There is therefore a strong need for a better understanding of the carbon
cycle and the processes that control the exchanges of carbon between the
atmosphere, the vegetation, and the soil. This understanding can be obtained
through a continuous monitoring of the CO2 fluxes at the
land–atmosphere interface and the analysis of its response to interannual
climate anomalies. This objective suggests the development of a satellite
monitoring system as recognized by the scientific community and several
space agencies (CEOS, 2018).
The first satellites to be launched with the aim of monitoring the CO2
cycle were Envisat (European Space Agency; ESA) with the Scanning
Imaging Absorption Spectrometer for Atmospheric Chartography
(SCIAMACHY) instrument, Greenhouse Gases Observing Satellite (GOSAT) (Japan
Aerospace Exploration Agency; JAXA) and Orbiting Carbon Observatory (OCO)
(NASA). The latter was unfortunately lost at launch, and a very similar
satellite, OCO-2, was built and launched. These have been followed by TanSat
(Chinese Academy of Sciences), GOSAT-2, and OCO-3 on the International Space
Station. All instruments rely on a similar method to estimate the CO2
concentration from space: high-resolution spectra of the reflected sunlight
are acquired over several bands centred on clusters of CO2 and O2
absorption lines. The depths of the lines are sensitive to the number of
molecules along the sunlight atmospheric path. The so-called differential
absorption method makes it possible to infer the amount of absorbing gas
along the line of sight, using some ancillary information on the atmospheric
profile. CO2 is the target component of the atmosphere and O2 is
used as a normalization component to link the CO2 estimated number of
molecules to a mixing ratio. Note that the sunlight atmospheric path length
is linked to the surface pressure but also to the presence of
light-scattering particles (aerosols and clouds) in the atmosphere. Because
oxygen is well mixed in the atmosphere, it is adequate for the normalization
of the measurement to estimate a mixing ratio.
The instruments currently in orbit focus on the CO2 absorption bands at
1.6 and 2.0 µm, and the O2 absorption band at 0.76 µm.
The use of the oxygen band poses several challenges: (i) there is still
significant uncertainty on the radiative transfer modelling within this
band; and (ii) its central wavelength is notably different from that of the
CO2 bands so that the spectral variations of the atmospheric scatterer
optical properties may lead to different optical paths for photons at
different wavelengths.
An alternative could be the use of the O2 absorption band around
1.27 µm. It is much closer in wavelength to the CO2 absorption bands,
which reduces the uncertainties linked to the spectral variations of the
atmospheric path. In addition, the absorption lines are weaker than those in
the 0.76 µm band, so the radiative transfer modelling is more
accurate. In fact, the 1.27 µm band is the one used for the
processing of TCCON (Total
Carbon Column Observing Network, a ground-based network of high-resolution
spectrometers observing the Sun to determine column densities) spectra
for the estimation of the column mixing ratio. This band
was not selected for current flying CO2 monitoring missions because it
is affected by airglow, a light emitted by oxygen molecules in the high
atmosphere. Oxygen airglow at 1.27 µm has a spectrum that is very
similar to the oxygen absorption spectrum used to estimate the sunlight
atmospheric path.
Sketch of a space instrument and platform to monitor greenhouse gases (GHGs),
including CO2. The O2 concentration (black curve) and the O2* volume
emission rate of the 1.27 µm airglow (blue curve) are plotted as a
function of altitude. The optical path of nadir-viewing observations is
inevitably crossing the airglow layer, whose emission is superimposed on the
spectrum of solar radiation scattered by the surface–aerosols–atmosphere system. The O2 absorption at
1.27 µm is mainly produced in the lower atmosphere, while the airglow
is in the range of ∼30–70 km altitude. Ozone photolysis
indicated in the figure is the main source of O2 airglow at 1.27 µm but not the only one.
Previous studies (Kuang et al., 2002) conducted during the preparation phase
of the OCO mission (Crisp et al., 2004) indicated that the contribution of
airglow could not be corrected with the desired accuracy. Conversely,
similar studies performed during the design phase of the CNES (Centre National d'Études Spatiales – the French
National Centre for Space Studies) MicroCarb
mission indicated that the airglow could be distinguished from the oxygen
absorption spectrum, provided that the instrument achieve a high spectral
resolution. These unpublished studies led to the addition of a fourth band,
centred at 1.27 µm, in the MicroCarb optical concept. The MicroCarb
mission shall then be the first CO2 monitoring mission to test the
potential of the 1.27 µm band, rather than the 0.76 µm band,
for the estimate of CO2 column concentrations from space. Note that the
instrument does record the classical O2 band at 0.76 µm for
reference and comparison with previous space missions. Recently, an
independent study (Sun et al., 2018) confirmed the MicroCarb analysis. The
authors show that, indeed, airglow has a spectral signature that is
different from that of the oxygen absorption and can therefore be
distinguished from the signature of oxygen absorption. It argues for the
inclusion of the 1.27 µm band in the design of future CO2
monitoring missions. In the present paper, we describe the analysis of the
airglow signature that has been conducted in the context of the MicroCarb
preparation.
When describing the choices made to define the OCO investigation to
determine CO2 vertical columns and mixing ratios from nadir-viewing
observations (which needs associated O2 columns), Kuang et al. (2002)
recognized the virtues of the O2 band at 1.27 µm (closest to the
CO2 bands) but discarded its use because it is contaminated by the
intense O2 airglow dayside emission when looking nadir from an orbiter
(Fig. 1). They quoted Noxon (1982) as having shown that the emission is
not only intense but variable. In fact, Noxon (1982) analysed spectra of
this emission collected from 60 flights of a KC-135 aircraft over 10 years
and a variety (latitude and seasons) of observing conditions, including two
solar eclipses. He reported that there were no secular variations (within
30 %), and also that the variations with latitude (obtained along a single
flight) were very smooth. This smoothness is confirmed by the present study
of both the SCIAMACHY dataset and the airglow model that we made, combined
with a chemistry transport model (CTM) model of ozone (not a climatology).
We have mentioned before that the TCCON ground-based spectrometer array,
observing the Sun, uses this 1.27 µm band to derive the CO2/ dry air mixing ratio (because the O2/ dry air mixing ratio is fixed equal to 0.2095)
rather than the A band, which can also be measured by some TCCON
spectrometers. Why? The argument is that the depth of the O2 lines at
1.27 µm has the same order of magnitude as those of CO2, while
the A-band (760 nm) absorption lines are much stronger. The use of spectral
bands with similar absorption depth may reduce small systematic errors (e.g. detector
linearity failure) for atmospheric quantities that are based on
measurement ratios. We argue that the same argument can be used for
observations from space, although other problems are added (Ring effect of
filling the line bottoms, polarization, etc.).
Given the level of accuracy which is needed for a useful retrieval of
CO2, it may not be possible to rely only on an a priori model of the O2
airglow to subtract it from a nadir-viewing spectrum which contains both the
absorption spectrum of O2 and the emission spectrum at 1.27 µm,
closely blended. An exception may be for high-surface-reflectance scenes,
such as glint viewing, when the transmitted reflected signal gets much
larger than the airglow emission. Indeed, for typical scenes, the amplitude
of the reflected and airglow spectra are similar, with nearly identical
spectral variations. There are nonetheless some differences that make it
possible to disentangle one from the other. First, there is the collision-induced
absorption (CIA) which is present in absorption but not in
emission, since it is proportional to the square of the O2 density and
therefore confined to lowest altitudes. Second, the emission at 1.27 µm
increases linearly with the column of O2* at all wavelengths
(re-absorption by O2 is negligible at emission altitude), resulting in
a constant relative shape of the emission spectrum, while the absorption
spectrum is not linear: the transmittance Tr=exp(-τ) saturates at
high optical thicknesses of O2τ>1, and the absorption
spectral shape is not constant but depends on the air-mass factor. Third,
individual rotational lines are subjected to pressure broadening, also
proportional to the air density. Therefore, the emission lines occurring at
high altitudes are much thinner than the same absorption lines built in the
lower atmosphere. These effects are illustrated in Fig. 2. O'Brien and
Rayner (2002) have proposed to discriminate the emission from the absorption
by recording a single line at very high spectral resolution (resolving
power of 400 000), with an imager and three very narrow filters, whose
positions are indicated in Fig. 2. One difficulty with this scheme is that
the photon flux collected in those three narrow bands is very small and the
corresponding signal-to-noise ratio (SNR) strongly reduced, rendering this proposal unpractical. By
contrast, the size of a pixel element of MicroCarb (corresponding to a
resolving power of 25 000) is also indicated for comparison. The whole spectrum
is recorded, and the shoulders of the absorption line contribute to the
disentangling of emission and absorption in a retrieval exercise, with 1024 pixels distributed along the O2 band.
Comparison at high spectral resolution of spectral shape
of atmospheric O2 transmission (transmittance) and
spectral shape of O2* emission. The full width at half maximum (FWHM) of an
individual O2 line (red) is much wider than the FWHM
of its counterpart in emission (black line), allowing in principle to
disentangle absorption from emission at selected wavelengths. The channels
recommended by O'Brien and Rayner (2002), of width 0.02 cm-1, are
represented: one outside an O2 line for the continuum; the other two on the side
of an absorption line but still outside the airglow emission line. The
transmittance was calculated with HITRAN at nadir at highest spectral
resolution. The black line represents the MicroCarb pixel size, giving a
resolving power of 25 000.
This paper is organized as follows. In Sect. 2, a brief review of previous
observations of the O2 (0, 0) airglow emission at 1.27 µm is
presented first and a formula for computing a theoretical airglow spectrum
is given. In Sect. 3, we describe how the SCIAMACHY observations of this
airglow at the limb have been processed in order to derive volume
emission rate (VER) vertical profiles (vertical inversion), and how a synthetic
airglow spectrum may be derived from combining the VER profile and our
spectroscopic studies. Our model spectral shapes are validated by a
comparison with SCIAMACHY observed shapes. In Sect. 4, we compare the
airglow total nadir intensities and VER profiles derived from SCIAMACHY limb
observations with our Reactive Processes Ruling the Ozone Budget in the Stratosphere
(REPROBUS) airglow model. A deficit of airglow from the
model is found. The MicroCarb space mission with its instrument is briefly
described in Sect. 5. It is shown in Sect. 6 that the O2 airglow
emission may be extracted from nadir-viewing SCIAMACHY observations over the
oceans, where the reflectance is minimal, in spite of its moderate spectral
resolution. Section 7 covers the overall conclusions with a prospective on
future greenhouse gas (GHG) monitoring space missions.
We put some additional details in several appendices, in order to ease the
reading of the most important results in the main text. Appendix A contains
details of the theoretical derivation of the synthetic spectrum of the
O2* airglow, together with a method to accurately compute the shape of
the airglow spectrum. The method of vertical inversion of the limb
observations to retrieve a vertical profile of the airglow emissivity is
described in Appendix B (onion peeling accounting for O2 absorption). A
comparison (Appendix C) of the ozone predicted by REPROBUS with
Global Ozone Monitoring by Occultation of Stars (GOMOS)/Envisat observations indicates a model deficit in ozone which, when
accounted for, would narrow the discrepancy between SCIAMACHY and the
airglow model. In Appendix D, the accuracy and bias results of the O2
column retrievals (or surface pressure) and O2 airglow intensity
disentangling from nadir MicroCarb simulated spectra are detailed in some
typical situations. In Appendix E, some other cases where absorption
measurements could be contaminated by airglow emission are examined in nadir
viewing. In Appendix F, some corrections made to SCIAMACHY
level-1c spectra to extract the absolute spectral radiance are illustrated.
Observations and spectroscopy of the O2 airglow band at 1.27 µmObservations of the airglow of O2* emission at 1.27 µm
The aeronomical emission at 1.27 µm was first observed in 1956, in
the “dayglow” (daytime aeronomical emissions) from instruments aboard
Soviet stratospheric balloons (up to 30 km altitude) (Gopshtein and Kushpil,
1964), but its origin was not understood at that time. Noxon and Vallance
Jones (1962) recorded spectra from a KC-135 plane flying at 13 km
altitude and described the origin of the emission in the form of the
electronic transition of the oxygen molecule from an excited state to the
fundamental, with the emission of a photon in one of the rotating branches
of the (0, 0) transition that form the entire “1.27 µm atmospheric IR
band”:
O2a1Δg⟶O2X3∑g-+hν1.27µm.
The recorded intensity was very large (more than 10 megarayleigh; 1
rayleigh is 106/4π photons cm-2 s-1 sr-1), but
atmospheric absorption in the very same band absorbs most of it before
reaching the ground. The much fainter emission from the (0, 1) transition of
the same electronic state at 1.58 µm had been observed earlier from
the ground, because it is not attenuated by O2 absorption (most O2
molecules are in the V′′=0 vibration level at atmospheric temperatures).
Throughout this paper, we use for convenience indifferently O2* or
O2(1Δ) or O2 (a1Δg) to designate
the molecule in its excited electronic state (a1Δg). There
are various ways to produce an O2 molecule in its excited state
(a1Δg), which are schematized in Fig. 9. The most important
mechanism of production of these excited molecules is the photolysis of
ozone by solar UV:
O3+hνλ≤310nm⟶O1D+O2a1Δg,
which therefore occurs during the day but can be observed more easily from
the ground at dusk with a high intensity of 30 megarayleigh. Once it is produced, it
remains there with a long lifetime, about 75 min, and is spontaneously
de-excited by emitting a photon or by a collision without a photon
(“quenching”).
At night, the emission falls to 100 kilorayleigh, but this time the origin of the
molecules a1Δg is mainly due to the recombination of
oxygen atoms O in their electronic ground state O(3P):
O+O+M⟶O2a1Δg+M.
Space observations of 1.27 µm
With a sounding rocket, Evans et al. (1968) were able to reconstruct for the
first time the vertical distribution of the emission at 1.27 µm, by
inverting the brightness integral. Their VER profile
showed that emissivity is highest at about 50 km (∼107 photons cm-3 s-1)
and zero or low below 30 km (due to quenching and
screening of solar UV by ozone). A secondary maximum at about 85 km is due
to the presence of a layer of mesospheric ozone well documented by
GOMOS/Envisat in star occultation mode of observation (Kyrölä et
al., 2018).
Several satellite instruments have been used in the past for the study of
the O2(1Δ) emission, mainly to retrieve the O3 or
the O concentration in the upper atmosphere:
the Solar Mesosphere Explorer (SME) satellite (Thomas et al. 1984);
the Optical Spectrograph and InfraRed Imager System (OSIRIS) spectrometer
on the Odin satellite (Llewellyn et al., 2004);
one infrared radiometer aboard the OHZORA satellite (Yamamoto et al.,
1988);
the SABER broadband IR photometer aboard the NASA Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) aeronomy
mission (Russell et al., 1999; Mlynczak et al., 2007; Gao et al., 2011); and
the SCIAMACHY spectrometer experiment aboard the Envisat ESA mission
(2002–2012) (Burrows et al., 1995; Bovensmann et al., 1999), which we
analyse in Sects. 3 and 4.
Spectroscopy and modelling of a synthetic spectrum of
O2* airglow
The spectroscopy of the O2 molecule and the modelling of a synthetic
spectrum of the O2* airglow are described with some details in
Appendix A. We relied on the high-resolution
transmission molecular absorption (HITRAN) spectroscopic database, both to illustrate the
structure of P, Q, and R branches of the 1.27 µm electronic transition
and to verify a theoretical relationship between the absorption and the
emission in this band. By using some equations from the paper of Simeckova
et al. (2006), which describes how were obtained the parameters contained in
HITRAN database, we obtained a very simple result on the ratio of emission
ε(k) to absorption line strength Sν(k,T) for each
spectral line (transition) k:
εkSν(k,T)=8πcν02Qtot(T)QtotupT1expc2ν0T-1.
This equation is the same as Eq. (A13) of Appendix A. T is the
temperature of the atmosphere in which is produced the airglow, ν0
is the wavenumber of the transition, and c2 is the second radiation
constant, c2=hc/kB, where c is the speed of light, h is the Planck
constant, and kB=1.38065×10-23 J K-1 is the Boltzmann
constant. Qtot(T) is the total internal partition sum of the absorbing
gas at the temperature T, and Qtotup(T) is the internal partition
sum of the upper level (here, a1Δg).
We have used this formulation to transform an absorption spectrum by O2
that can be easily computed with Line-By-Line Radiative Transfer Model (LBLRTM) software (see details in
Appendix A) into a synthetic emission spectrum. This method of construction
of a synthetic emission spectrum was the basis of our work on three topics:
a satisfactory comparison with the observed spectra of SCIAMACHY (see
below); retrieving the airglow intensity from SCIAMACHY nadir data over
low-albedo regions; retrieving the surface pressure from simulations at high
spectral resolution.
We should mention that one reviewer was able to show with some manipulations
of equations that the same relationship (Eq. 4) could be obtained from the
equations contained in Sun et al. (2018). It clearly stands as a validation
of our present work and shows that the two approaches are consistent. We
should also mention that, in an early phase of our studies, we used what we
call later below (Sect. 3.3) our “crude model”, in which the airglow
emission spectrum would have the same spectral shape as the local emission
spectrum. In Sect. 3.3, we show that SCIAMACHY data are in agreement
with our “new” model, based on Eq. (4), rather than with the crude
model.
The use of SCIAMACHY data for the study of the
O2 (1Δ) emission
We have used the SCIAMACHY data because of the spectral capability (with a
resolving power λ/dλ∼850) and extensive dataset
produced during the ESA/Envisat mission.
Description of SCIAMACHY investigation of O2 (1Δ) emission
SCIAMACHY is a multi-channel spectrometer dedicated to the study of Earth's
atmosphere aboard the ESA Envisat satellite
(Burrows et al., 1995, Bovensmann et al., 1999). It is an
eight-channel grating spectrometer that measures scattered sunlight in limb
and nadir geometries from 240 to 2380 nm. In addition, it was operated also
in solar and lunar occultation. In this study, we have used both limb and
nadir measurements covering the O2(1Δ) band
(1230–1320 nm) in spectral channel 6 (1050–1700 nm).
In a recent study to retrieve the volume emission rates of O2(1Δ) and O2(1Σ) in the
mesosphere and lower thermosphere, Zarboo et al. (2018) have used a special
mode of SCIAMACHY: the mesosphere and
lower thermosphere (MLT) limb scan mode, dedicated to the study of the
mesosphere and lower thermosphere in the region of 50–150 km. This mode was
used only twice a month from July 2008 until April 2012. In contrast, we
have used the normal limb-mode viewing geometry, where SCIAMACHY
tangentially observes the atmosphere from the surface up to about 100 km
with a vertical step of 3.3 km. At each tangent point, the full width at half maximum (FWHM) of the field of view (FOV)
is 2.6 km (with a somewhat coarser vertical resolution), the horizontal
along-track resolution is about 400 km, and the horizontal cross-track
resolution is 240 km. To improve the SNR, the four
cross-track spectra at the same elevation step are co-added, reducing the
cross-track resolution to 960 km (the swath width).
To generate data for our study, we used the SCIAMACHY level-1b
version 8.02 dataset that we converted into level-1c radiometrically calibrated
radiances (in physical unit) by using the SCIAMACHY command line tool
SciaL1c version 3.2. Before deriving the O2(1Δ) VER
profiles, we had to perform a few corrections on the level-1c radiance
spectra, as illustrated in Appendix F. First we subtracted the average of
the 4 spectra measured above 105 km tangent height (generally around 150 or 250 km)
as a dark spectrum from the measured spectra at all of the other
tangent heights. This high-altitude spectrum contains some residual spectral
(readout) patterns left from the calibration step. All spectra contain two
bad pixels at wavelengths of 1262.267 and 1282.128 nm. In order to correct
these two pixels, we replaced their value by the average of their two
surrounding pixels. When the tangent altitude of the line of sight (LOS) decreases, there is
an increasing background signal due to the Rayleigh and/or aerosol
scattering outside the O2 band. We corrected the spectra from this
signal by removing a straight line computed as a linear interpolation
between the two “surrounding” average backgrounds (estimated from the
median value of all points to avoid outliers) in the 1235–1245 nm domain and
in the 1295–1305 nm domain. The spectra after correction are ready to be
used for the retrieval of the SCIAMACHY O2(1Δ) VER, as described in Appendix B. An onion-peeling
method, modified to account for the re-absorption of O2, allows to
retrieve the VER vertical distribution from any limb scan. Then the VER is
integrated vertically, yielding the O2(1Δ) intensity that
would be observed at nadir for an observer located at the tangent point of
the limb scan.
In Fig. 3, the nadir radiances (equivalent to intensities or brightness)
derived from a series of SCIAMACHY limb scans along one particular orbit are
plotted as a function of solar zenith angle (SZA), when different
atmospheric models are used (the atmospheric density profile modifies the
re-absorption by O2). For each model, there are two branches,
corresponding to north and south along the dayside polar orbit of Envisat
(the north branch is in winter, while the south branch is in summer for this
orbit). We see that the choice of the atmospheric model in the computation
of the O2 absorption has a small (∼3 %) but noticeable impact
(on the brightness seen at nadir). We have also plotted the prediction of
the REPROBUS model, as described in Sect. 4 and 4.2.1. It
should be noted that the choice of the “adapted climatology” (for which we
take for each measurement the most appropriate in latitude and season of the
three considered atmospheric models), makes it possible to reduce the
separation between the two branches and thus to be closer to the separation
between the two branches obtained with the REPROBUS model.
Computed O2* radiances in
nadir-viewing geometry, derived from SCIAMACHY limb radiances, as a function of
SZA for orbit 20070101_1256 when the
O2 absorption is computed with various choices of
atmospheric models: climatology US_STANDARD (black),
SUBARTIC_WINTER (blue), SUBARCTIC_SUMMER
(green) and ADAPTED_CLIMATO (red) (see Appendix B for
details). There is a slight dependence of the nadir intensity on the choice
of atmospheric model. The dashed purple curve (with filled circles)
corresponds to the REPROBUS v02 model.
Computation of synthetic spectra and comparison with SCIAMACHY
observed spectra
Once we have the vertical profile of VER corresponding to a given SCIAMACHY
limb scan, we can compute the spectrum of the local emissivity (in absolute
units of photons cm-3 s-1 sr-1 nm-1) with the theoretical approach
developed in Appendix A. Then, we may integrate the spectra with Abel's
integral along horizontal LOS tangent at the limb, for a direct comparison
with the actually observed SCIAMACHY spectra. In this particular exercise,
we did not account for the O2 absorption for simplicity, and for this
reason we restricted our comparison to altitudes >60 km. The
spectral resolution of SCIAMACHY was used to smooth the high-resolution
spectra (line by line) obtained from the approach described in Appendix A.
In Fig. 4a, the observed spectra, binned by altitudes
(60–70, 70–80, and 80–90 km), are represented along with our model spectra computed for the
same scans and binned in the same way, for a particular limb scan (points in
green in Fig. F5 in Appendix F representing the locations of the tangent
points of SCIAMACHY limb scans). The agreement is basically very good, both
in shape and intensity. We note that the model is slightly brighter than the
data, and the relative difference is larger for the bin 60–70 km than for
the other bins. We tentatively assign this behaviour to the fact that we
have not accounted for the O2 absorption along the LOS in the model,
more important at 60–70 km than higher.
Figure 4b is the same as Fig. 4a, with the crude model in which the spectral
shape of the O2* emission is identical to the O2 absorption. In
this case, the R branch is systematically overestimated by this crude model.
(a) SCIAMACHY limb spectra (solid lines; absolute units are
photons cm-2 s-1 sr-1 nm-1), binned by altitudes (60–70, 70–80, and 80–90 km), along with our model spectra computed for the
same scans and binned in the same way. Panel (b) is the same as (a)
but with the crude model in which the shape of the emission of O2* is identical to the
absorption by O2. This crude model shows an excess of
emission in the R branch (left) and a deficit in the P branch.
The ratio of measured spectra to model spectra (Sobs/ Smod) were averaged
together for all scans of that particular orbit within the same three
altitude bins. They are represented in Fig. 5, both for the crude model
(absorption equal to emission; Fig. 5a) and for our “true” model of emission
based on Eq. (4) (Fig. 5b). It is clear that the crude model does not
represent well the observed spectra, while the model with the true emission
agrees quite well with the data. This comparison validates the approach that
we developed in Sect. 2 and Appendix A, except that the overall level of
the ratio is slightly below 1 (Fig. 5b). Again, we assign this behaviour
to the fact that we have not accounted for the O2 absorption along the
LOS in the model, and indeed it can be seen that the ratios are closer to 1
for higher altitudes. Below 1255 nm and above 1285 nm, the intensity of the
spectra is very small, and thus we attribute the noisy shape of the ratio
spectra to low SNR.
Ratios of measured spectra / model spectra of limb spectra,
averaged over a whole Envisat orbit, and binned by altitudes (60–70,
70–80, and 80–90 km). (a) Crude model in which the shape of the
emission of O2* is identical to the absorption by
O2. (b) Same ratios with our new model described
in Appendix A. The ratios are closer to 1 for larger altitudes because
absorption by O2 is neglected in this particular
exercise.
Climatology of O2* VER derived from
SCIAMACHY limb radiances
To build up a climatology of the O2* emission at 1.27 µm, we
have applied our inversion scheme to get VER vertical distributions
(Appendix B) to all SCIAMACHY limb data collected during the first 3 d of
each month of 2007. Note that in the normal mode, the limb scans extend
down to 0 km (our inversion is made >30 km), while in the
special SCIAMACHY MLT mode, only altitudes >50 km are observed.
Our database contains the analysis of 448 orbits, containing 12 400 limb
scans in the normal mode which go down sufficiently for our purpose (some
limb scans do not reach low enough altitudes to allow retrieval of the full
VER profile above 30 km).
The vertical inversion of SCIAMACHY limb radiances to get a VER vertical
profile is done below 90 km down to 0 km; but only results >30 km
are significant, because at the limb and low altitudes, there is Rayleigh
and aerosol solar radiation scattering (the useful signal for SCIAMACHY limb
mode ozone retrieval) which dominates over the O2* radiance. Once a VER
profile is obtained, it can be integrated vertically, taking into account
the absorption by O2. Therefore, a “SCIAMACHY” nadir radiance is
obtained, which corresponds to the O2* radiance that would be observed
by SCIAMACHY if it were observing nadir at the position of tangent points
where the limb radiances were obtained. In fact, the nominal operation mode
of SCIAMACHY does indeed alternate limb-viewing and nadir-viewing
observations to discriminate tropospheric ozone from stratospheric ozone
(Ebojie et al., 2014).
When this VER is integrated vertically to get a nadir radiance, the
integration stops at the upper limit of 80 km in order to have a better
comparison with the REPROBUS model which stops also at 80 km. The air
atmospheric model selected to compute the re-absorption by O2 is our
so-called “adapted climatology” (see Appendix B). In Fig. 6, about
one-third of all VER profiles collected for the first 3 d of January 2007
are displayed (other months are quite similar). The colour code corresponds
to the SZA of the limb scan. We kept also scans near the terminator, where
the VER is significant only above 80 km. Clearly, the SZA is the factor
dominating the shape, the peak altitude and the intensity of the airglow VER
profiles between 30 and 80 km. This is due to UV photo-dissociation of ozone
(the main process of O2* production) penetrating more deeply when the
SZA is small (because of ozone UV screening). The lower the SZA, the
brighter the airglow emissivity. Above 80 km, other processes come into
play and a second airglow peak is observed which seems less correlated with
the SZA than is the main peak at 45–50 km. The altitude of the main airglow
emissivity peak varies between 43 and 45 km for values of SZA below
50∘ and increases for higher values of SZA up to about 60 km.
VER profiles of airglow at 1.27 µm
retrieved from SCIAMACHY limb data for 1 January 2007
(80 profiles). The colour scale represents the SZA. For
the lowest SZA values probed by SCIAMACHY (33∘), the peak VER is
2×107 photons cm-3 s-1 around 45 km. At large SZA values, the emission
is present only at high altitudes (>80 km). Above 90∘, there is almost no signal for inversion.
In Fig. 7, all the “SCIAMACHY” nadir
radiances (longitude–latitude) obtained by inversion of limb radiances and vertical integration
of VER (first 3 d of each month of the year 2007) are mapped for 4 typical months,
(January, April, July, and October). The airglow brightness is almost
independent of longitude. At high latitudes (north and south), the
brightness is lower: this is the effect of larger SZA. The region of
maximum brightness is displaced with season, following the latitude of the
sub-solar point, again an effect of the SZA dependence of the O2*
radiance. This is illustrated in Fig. 8, where all the nadir radiances are
plotted as a function of SZA, with a colour code on latitude. The lower SZA,
the brighter is the nadir emission. This SZA dependence is well reproduced
by the model (penetration of solar UV deeper in the ozone layer for small
SZA; see Sect. 4). Still, there is a separation of the curves in two
branches that are relevant to the Northern Hemisphere and Southern Hemisphere. The
separation between the two branches depends on the season. This observed
overall pattern of the O2* radiance is directly linked to the
climatology of upper stratosphere/lower mesosphere ozone and also
reproduced by the model (Sect. 4).
Airglow brightness maps as seen from space in nadir view,
retrieved from SCIAMACHY limb-viewing data for the months of January, April,
July, and October 2007 (first 3 d of each month only). The colour scale
represents brightness. Zones without data (holes), particularly numerous in
April, are corrupted products that have been eliminated. SZA points >90∘ have been eliminated.
O2* airglow intensities that would be seen at nadir as a
function of SZA for the months of January, April, July, and October 2007
(first 3 d of each month only), retrieved from the processing of
SCIAMACHY limb data. The colour scale represents the latitude. There is a
geometrical correlation between the latitude and SZA imposed by the polar
orbit of Envisat. The airglow brightness is mostly correlated with SZA. The
comparison of these intensities with those obtained by the REPROBUS airglow
model is presented in Sect. 4.2.1.
Comparison between an airglow model based on REPROBUS and
SCIAMACHY observations
In this section, we compare the predictions of a dedicated 3-D model of the
airglow emission of O2(a1Δg) at 1.27 µm to the
airglow observations of SCIAMACHY. The comparison makes use exclusively of
the SCIAMACHY limb observations but is made in two different ways. One way
is to compare the SCIAMACHY VER vertical profile retrieved from limb
measurements through vertical inversion as described in Appendix B. The
second way is to compare the nadir integrated emission Iag (brightness)
of the airglow. Both model and data nadir emissions are obtained by vertical
integration of the VER, respectively, in the airglow model and in the
SCIAMACHY-derived VER vertical profile. This nadir emission is directly
relevant to the GHG observations since, from an orbiter and nadir viewing,
this signal is superimposed on the solar back-scattered emission from which
the columns of GHG gases and O2 must be retrieved. This is why it is
not practical to use the nadir observations of SCIAMACHY to study the
O2* airglow, since the nadir signal is dominated by surface
back-scattered solar radiation (except over the oceans, as we shall see in Sect. 6.2.2).
Since the photolysis of ozone is the major source of the O2* airglow,
it was also felt necessary to compare the ozone density predicted by our
airglow model and GOMOS
ozone measurements also on Envisat, simultaneous with SCIAMACHY observations
(but not with the same geometry), as described in Appendix C.
3-D simulation of the airglow emission of
O2(a1Δg) at 1.27 µm
The airglow model is composed of two separated elements. The first element
is the REPROBUS chemistry transport model (CTM) computing the 3-D
distribution of ozone and other chemical species as a function of time,
driven by analysed meteorological fields. The second element is an airglow
model operated offline, which extracts from REPROBUS (for one location and
one precise time and date) the information necessary for the computation of
the relevant VER profile.
REPROBUS 3-D simulations
REPROBUS is a global CTM developed for the
stratosphere (Lefèvre et al., 1994). It includes a complete description
of stratospheric chemistry using 58 species and about 100 chemical
reactions. The winds and temperatures used by REPROBUS are forced by the
ECMWF operational analyses, over a domain that extends from the ground to
0.01 hPa (about 80 km) and a horizontal resolution of 2∘× 2∘.
For the present study, we carried out a REPROBUS
simulation covering the whole year 2007 with the results saved every hour.
The choice of 2007 was motivated by the fact that we had already extracted
SCIAMACHY data for this year. From this new simulation, all the GOMOS or
SCIAMACHY data obtained in 2007 can be compared to the CTM with a spatial
difference less than or equal to 1∘ and a time difference less
than or equal to 30 min. It should be noted that for GOMOS the comparison
with the model is limited to ozone profiles, since GOMOS does not have a
channel at 1.27 µm and therefore does not observe airglow at this
wavelength. SCIAMACHY observations of the 1.27 µm airglow were
compared to the combination of REPROBUS and the offline airglow model.
Based on the results of REPROBUS available every hour of 2007 and the
offline airglow model, we have developed a procedure for the automatic
extraction of vertical ozone profiles and O2(1Δ) emission
profiles as well as the integrated O2(1Δ) emission in
coincidence with the GOMOS and SCIAMACHY measurements performed the same
year. This dataset represents 4026 profiles modelled in coincidence with
GOMOS and 12 800 in coincidence with SCIAMACHY. The statistical analysis of
the comparison between the model and observations is presented in
Sect. 4.2 for SCIAMACHY observations and in Appendix C for GOMOS
observations. It should be noted that, as a result of some discrepancies
revealed by this comparison, the REPROBUS model will be modified in the
future for a better representation of mesospheric ozone. Although the
retrieval of O2 column does not need a model, it is likely that the
output of the improved REPROBUS model (O2* intensity) will be used as a
prior information in the retrieval process.
Simulation of airglow emission of O2* at 1.27 µm
Here, we do not care about the details of the spectral shape of the emission,
but rather we compute the local emissivity (VER) and the vertically
integrated emission, in order to compare with SCIAMACHY observations. The
airglow at 1.27 µm is calculated offline from the 3-D outputs of the
REPROBUS model. It takes into account all the mechanisms of production and
loss of O2(a1Δ), as shown in Fig. 9. In practice, the
O2(1Δ) emission model uses as input the ECMWF temperature
and pressure profiles as well as the O3 and O(3P) profiles
calculated by REPROBUS for the selected date and location. From the pressure
and temperature, the total density and density profiles
of N2, O2, and CO2 are also calculated. The airglow model then provides the
vertical profiles of the mixing ratios of O(1D), O2(b1Σ), O2(1Δ), the vertical profile of VER at 1.27 µm expressed in photons cm-3 s-1, and the
vertically integrated intensity expressed in
photons cm-2 s-1 sr-1 (brightness or intensity, directly
comparable to the radiance signal of the solar radiation back-scattered by
the gaseous atmosphere, aerosols and the surface).
Two versions of the airglow model were used. One early version of the model
(v01) was later modified to a version v02 which yielded better agreement
with SCIAMACHY observations. They differ only by the value of the quenching
rate of the O2(1Δ). The early version v01 contained a
quenching constant k:
kΔ,O2=3.6e-18×exp(-220/T)incm3molecule-1s-1,T=temperature(K),
recommended in the Jet Propulsion Laboratory (JPL) compilation (Burkholder et al., 2015). The version
v02 has a slightly different value of k, recommended by the
International Union of Pure and Applied Chemistry (IUPAC) (Atkinson et
al., 2005):
kΔ,O2=3.0e-18×exp(-200/T)incm3molecule-1s-1,T=temperature(K).
At stratospheric temperatures, the value of kΔ,O2 is
decreased with v02 by about 10 %, enhancing the emission rate of
O2(1Δ). This gives a better fit (but not perfect) between
SCIAMACHY observations and the airglow model. According to Wiensz (2005),
this IUPAC recommended value gives a better agreement between OSIRIS/Odin
direct and indirect measurements of ozone. Unless otherwise specified, we
are presenting in this paper the v02 results.
Energy diagram of the O and O2 molecules
showing both O2 bands at 762 nm (A) and 1.27 µm.
Only the O2 photo-dissociation
(JSRC (Schumann–Runge continuum) and
JLy-α) is not taken into account in our model,
since this represents only about 1 % of the integrated emission
(reproduced from Wiensz, 2005).
Some examples of model results
As an example, Fig. 10 shows the REPROBUS results for 21 June 2007 at the
pressure level of 0.9 hPa (about 50 km), which corresponds to the altitude of
maximum emissivity of O2(1Δ). The figure displays the
O3 volume mixing ratio (Fig. 10a), the corresponding volume
emission rate of O2(1Δ) calculated by the airglow model
(Fig. 10b), as well as the vertically integrated
O2(1Δ) emission (Fig. 10c) (the possible
reabsorption between the emission point and the top of the atmosphere by
O2 is here neglected).
On 21 June 2007, the volume emission rate of O2(1Δ) at
1.27 µm shows a maximum at high southern latitude that is obviously caused
by a maximum of O3 at the same location. However, the
O2(1Δ) emission is very strongly modulated by the solar
zenith angle. This effect is further exacerbated when the emission is
vertically integrated: the intensity of the O2(1Δ)
emission is then systematically maximum at local noon and its variations are
entirely controlled by the solar zenith angle. At a given solar zenith
angle, the intensity shows very little spatial variability and the effects
of the heterogeneity of the ozone field are almost completely erased.
Simulations of the REPROBUS model for 21 June 2007 at 06:00 UT. This is
the geographical distribution of three relevant quantities with their colour code.
(a) Ozone mixing ratio (ppmv) at 0.9 hPa (about 50 km). (b)
Volume emission rate of O2(1Δ) at 0.9 hPa in units of
107 photons cm-3 s-1. (c) Integrated vertical
intensity of O2(1Δ) in units of 1012 photons cm-2 s-1 sr-1.
Because of the particular time and date, the sub-solar point is at a latitude of 23.5∘ N
and on the meridian of 90∘ E, which is the red spot plotted in the centre of panel (c).
Comparison of SCIAMACHY data with REPROBUS-derived airglow model
For each observation of our SCIAMACHY 2007 dataset in limb viewing, we have a co-located
VER profile of O2(1Δ) calculated by the REPROBUS-based airglow model.
We were therefore able to make comparisons between the airglow of SCIAMACHY and that of
REPROBUS in two different ways: (i) the brightness of the airglow as it would be seen by a
TOA (top-of-atmosphere) observer in nadir viewing; (ii) the VER airglow vertical profiles.
Comparison of O2(1Δ) airglow brightness as seen by a TOA observer in nadir view
The airglow model brightness is obtained by a vertical integration of the VER
produced by the airglow model. Re-absorption by O2 in nadir geometry
is small and has been neglected. However, this nadir model intensity cannot be
directly compared with a nadir observation of SCIAMACHY, as it is most of the
time completely dominated by terrestrial albedo. Therefore, to evaluate the
nadir intensity corresponding to the SCIAMACHY data in limb viewing, we proceeded
as follows:
For each vertical scan at the limb with SCIAMACHY, the total brightness of
the airglow was first estimated by integrating spectrally the SCIAMACHY
spectra at each altitude, and then the VER profile was determined with an
onion-peeling method (taking into account horizontal re-absorption), as described in Appendix B.
Then, the VER was vertically integrated to yield the intensity (or brightness)
that an observer placed above the tangent points of the SCIAMACHY scan would see looking to nadir.
Figure 11 compares the nadir intensities as a function of SZA co-located for
SCIAMACHY and REPROBUS for the first 3 d of January, April, July, and
October 2007. This represents the data collected over ∼50 orbits of
Envisat for each considered month. The Envisat orbit is almost polar and
descending in latitude on the day side (Equator crossing around 10:30 LT,
descending node). In both data and model, the SZA is the dominating
factor for the intensity. The latitude (which is colour coded in the plot)
plays also a small role (through the ozone field), more important in July.
The repeatability of the SCIAMACHY-derived nadir intensities is obvious,
with very little dispersion. The main difference between data and model is
that at SZA ≲70∘ the REPROBUS/airglow
model systematically underestimates the airglow intensity by 10 %–20 %
compared to that seen in the SCIAMACHY data.
Regarding the SZA of the SCIAMACHY data, we noted
that the SZA value provided in the SCIAMACHY ESA products in limb viewing,
as defined in the data product, is the SZA value of one of the two points
(the nearest to Envisat) corresponding to the intersection between the LOS
and TOA (defined at 100 km altitude). But what we need is
the SZA of the tangent point of the LOS, which is different.
Therefore, we systematically calculated the SZA at the tangent point of the
SCIAMACHY LOS using an external tool (IDL routine). All results presented in
this report are obtained using this recalculated SZA.
Airglow intensity seen at nadir as a function of SZA for
the months of January, April, July, and October 2007 (first 3 d of each
month only). Data (star symbol) come from the SCIAMACHY limb data (retrieval
of VER with onion-peel inversion and subsequent vertical integration); other data
(Δ symbol) are from the REPROBUS/airglow model. The colour
scale for symbols represents latitude. The SZA for the SCIAMACHY curve was
recalculated with an external tool. The data are systematically larger than
the model but overall behaviour is similar.
Note that in Fig. 11 there is a remaining difference between the minimum SZA
of the SCIAMACHY data and the minimum SZA of the model of up to about
8∘. This difference may be explained due to the UT time
difference between the model and the data since, unlike the data, the model
was calculated on a fixed UT time grid with a round hour (10:00, 11:00 UT,
etc.). There is therefore a time difference between model and data of up to
30 min, which can be both ways in difference of SZA:
SZA(model) – SZA(data). The true SZA is the data one; and the model SZA may be
the same as the data ±8∘, but only the negative
differences of SZA(model) – SZA(data) are obviously visible on the plot, when
SZA(data) is at its minimum value, and SZA(model) is below SZA(data). Points
with positive differences are just mixed with all other points.
Comparison of VER vertical profiles
We have seen systematic differences between the nadir intensities
(vertically integrated VER) of SCIAMACHY and REPROBUS. It is therefore
interesting to pinpoint at which altitude the differences are essentially
located, by directly comparing the vertical VER profiles produced by the
REPROBUS model and those that could be derived from the SCIAMACHY limbs by
onion-peel inversion. The comparison of some typical VER profiles of
SCIAMACHY and REPROBUS is illustrated in Figs. 12 and 13. It shows that in
the lower part of the profiles, say up to 40 km altitude, there is a good
match of VER values between SCIAMACHY and REPROBUS. At higher altitudes >40 km, REPROBUS/airglow model predicts less
O2(1Δ) airglow than observed by SCIAMACHY. One obvious
possibility is that there would be actually more ozone in the upper
stratosphere than predicted by the REPROBUS model, since the O2*
emissivity is proportional to the ozone concentration at high altitudes
(optically thin medium).
Comparison of SCIAMACHY and REPROBUS airglow VER profiles
for 3 January 2007. Three profiles were drawn at SZAs of about
30, 60, and 80∘, indicated in the legend.
The SCIAMACHY profiles are plotted as solid lines and the geolocated
REPROBUS profiles are plotted as dashed lines.
The same type of comparison is presented in Fig. 13a for a full set of
SCIAMACHY limb observations obtained during the first 3 d of January
2007 but still selecting the SZA of tangent points in slices around
30, 60, and 80∘, with a different colour
for each SZA slice. The number of profiles were 66, 28, and 29, respectively,
for slices around 30, 60, and 80∘. Solid
lines are the VER retrieved from SCIAMACHY, while dashed lines are
calculated by the REPROBUS/airglow model. Only the data collected in the
Southern Hemisphere are presented here. Figure 13b
represents the relative difference (SCIAMACHY-Model)/Model. Focusing our
attention to the altitude range of 40–70 km where most of the emission occurs,
it seems that the relative difference behaviour with altitude is identical
for SZA of 80 and 60∘ (green and blue curves) with a
peak of discrepancy (about 30 % more observed emissivity) at 67 km, while
for SZA around 30∘ the peak of discrepancy is even larger (about
45 % more observed emissivity) but at a lower altitude ∼58 km. In
Appendix C, we compare the ozone profile calculated by REPROBUS with ozone
measurements taken by GOMOS on Envisat during the same period of time (year
2007). In short, this comparison suggests that at least one part of the
airglow discrepancy is due to a deficit in the ozone predicted by REPROBUS
(up to 15 %) in the range of 40–70 km (Fig. D3).
(a) Comparisons of all SCIAMACHY and REPROBUS airglow
VER profiles for the first 3 d of January 2007 in three SZA domains:
SZAs of 32–34∘ (red curves), SZAs of 58–62∘ (green curves),
and SZAs of 78–82∘ (blue curves). Only profiles from the Southern
Hemisphere were selected. (b) For each profile, relative difference
(SCIAMACHY-REPROBUS)/REPROBUS is shown.
Another possible reason for the discrepancy in airglow vertical profiles
between our model and SCIAMACHY observations would be the radiometric
calibration of SCIAMACHY in this airglow band. For the time being, we reject
this hypothesis for two reasons. The first is that the radiometry of
SCIAMACHY has been compared to other instruments with observation of deep
convective clouds (DCCs) found in the tropics. A good agreement is found
around 1300 nm when compared to MODIS and Hyperion hyperspectral sensor
(Morstad et al., 2012). The second reason is that the onion-peel inversion
scheme that we have designed to derive VER vertical profiles is a linear
one. Therefore, changing the calibration of SCIAMACHY by a scaling factor
would also change the VER profile by the same factor, while we see that the
VER discrepancies are changing with altitude.
The MicroCarb mission
In the domain of Earth observations and GHG monitoring, CNES (Centre
National d'Etudes Spatiales) has developed the MicroCarb mission, a space
observatory dedicated to CO2 monitoring. As a result of the studies
conducted since 2016 by CNES concerning the use of the 1.27 µm
O2 band reported in the present paper, it was decided to incorporate
in the instrument the 1.27 µm O2 band as band B4.
The MicroCarb mission builds on a high spectral resolution infrared grating
spectrometer aboard a microsatellite. The satellite platform is an
enhanced version of the Myriade family. The total mass of the satellite
including payload is 170 kg for a power of 100 W. MicroCarb will be launched
in 2021 on an 11:30 h ascending node or alternately 13:30 h descending node
heliosynchronous orbit (to be decided later).
The MicroCarb data consist of the measurement in four spectral bands of the
solar irradiance reflected by the surface and partially absorbed by
atmospheric gases. Two bands are dedicated to the measurement of CO2
around 1.60 µm (weak CO2 B2) and 2.04 µm (strong CO2
B3). Two spectral bands are dedicated to O2 around 0.76 µm
(strong O2 B1) and 1.27 µm (weak O2 B4). Figure 14
illustrates typical radiances in the four MicroCarb bands and Table 1 gives
the main properties of the MicroCarb bands. The mechanical implementation on
a microsatellite is enabled by a very compact design of the instrument,
having a unique telescope, one slit per band, a unique grating and a unique
Sofradir Next Generation Panchromatic 1024×1024 pixel detector for the
four bands (Pasternak et al., 2016).
Example of MicroCarb simulated spectra of the strong
O2 band (B1) (a), the weak
O2 band (B2) (b), the strong
O2 band (B3) (c), and the weak
O2 band (B4) (d).
Spectral parameters of the four MicroCarb spectral bands
MicroCarb provides spectra for individual footprints of 4.5 km across track
(ACT) per 8.9 km along track (ALT). Three contiguous ACT footprints are
acquired at once during the integration time of ∼1.3 s. An
embedded imager, using the same telescope as the spectrometer, provides a 27 km ACT per 17.8 km ALT image centred on the three footprints for each
integration time. Each image is made of 121 m × 153 m individual pixels.
MicroCarb will look at nadir over land or use a scanning mirror to get a
swath up to +/-200 km. MicroCarb will look at sunglint over seas and
lakes. Specific observations will be dedicated to calibration (target, Sun,
internal lamp, internal shutter, cold space, Moon) or probatory experiments
(local mapping). Observations at the limb are foreseen, dedicated to record
the pure airglow emission for spectral characterization and better
simulation in the forward model that will be used for the retrieval of
O2 column in nadir viewing.
The MicroCarb ground segment will produce five levels of products: level 0
(L0) corresponding to raw telemetry, L1 to spectra calibrated for
radiometry, spectrometry and geometry, L2 to dry air column-averaged
CO2 volume mixing ratios, L3 to space and time averages of the L2, and L4 to surface carbon fluxes.
The computation of L2 products from L1 data is a very active research field
(see, e.g. Boesch et al., 2011; Crisp et al., 2017b; Hasekamp et al., 2015;
Heymann et al., 2015; Yoshida et al., 2011). The MicroCarb is developing
its own inversion tool named 4ARTIC (4AOP Radiative Transfer Inversion
Tool). This tool is based on the optimal estimation described in Rodgers
(2000). 4ARTIC retrieves CO2 and H2O on 19 vertical layers, mean
and wavelength slope of albedo for each band, surface pressure, aerosol
properties, 0.76 µm fluorescence, potential instrumental parameters,
and the 1.27 µm airglow emission as described hereafter.
The prior information will be provided by the ECMWF analysis for pressure,
temperature and humidity, CAMS (Copernicus Atmosphere Monitoring Service)
for CO2 and aerosols, PlanetObserver for the digital elevation model
(https://www.planetobserver.com/products/planetdem/planetdem-30/,
last access: 11 June 2020)
and Sentinel-2 images for albedo (from the MultiSpectral Instrument (MSI),
the unique instrument aboard Sentinel-2). The Jacobians (partial
derivatives of the spectrum with respect to geophysical variables) will be
computed by the 4AOP radiative transfer code (Scott and Chedin, 1981). The
scattering by molecules and aerosols will be computed by a discrete
ordinated scheme using LIDORT (Spurr, 2012).
Main characteristics of the four MicroCarb bands (B1, B2, B3, B4).
A major difficulty for passive spectrometry space missions dedicated to
trace gases is to handle the perturbation of the light path by the aerosol
scattering. Aerosols may increase or decrease the optical length, depending
on conditions. The available prior information about aerosols (type,
density, vertical distribution, optical properties) is poor, as well as the
aerosol information content in the spectrum. A specific retrieval scheme was
therefore developed for 4ARTIC to handle aerosols as an equivalent
distribution with a limited number of free parameters. The vertical
distribution of aerosols is described as a Gaussian:
hz=A′exp-4ln2z-zaer2waerzaer2,
where A′ is a normalization coefficient, and the width of the Gaussian
waer is linked to the height zaer of peak aerosol concentration
by
waerzaer=w0exp-4ln2zaer-w022w02,
where w0 is equal to 4 km. This scheme is inspired from Butz et al. (2009).
The spectral dependence of aerosol optical depth (AOD) is described
by the Angström coefficient (ka):
AODσ=AODσ0σσ0ka,
where σ is the wavenumber and σ0 a wavenumber
reference. 4ARTIC then retrieves three aerosol parameters at the same time
as CO2: the AOD at σ0=0.76µm, the altitude of
the maximum of the Gaussian (zaer) and the Angström coefficient
(ka). The single scattering albedo (SSA) of one aerosol particle is
currently fixed to 1 and the phase function is described by the
Henyey–Greenstein function with g currently fixed to 0.8, a value used frequently
in the literature to describe preferential forward scattering, but could be
adapted if necessary.
As already mentioned, the main purposes of the O2 bands are to provide
information about aerosols, which modify the optical path of solar photons
scattered back to the spacecraft. Most of the current CO2 missions,
e.g. GOSAT (Yokota et al., 2009) or OCO-2 (Crisp et al., 2017a) acquire
only one O2 band, at 0.76 µm. As this band is spectrally far
from the CO2 bands, any spectral dependence of the aerosols might
disturb the evaluation of aerosol impact in the CO2 bands. As an
example, the OCO-2 products are known to be sensitive to aerosols, making a
bias correction post-processing mandatory (O'Dell, 2018). In this context,
and since the instrumental concept of MicroCarb offered the possibility to
carry four spectral bands, the MicroCarb Mission Group chose the same 0.76,
1.60 and 2.04 µm bands as OCO-2 and GOSAT, and chose the additional
1.27 µm O2 band in order to get aerosol information spectrally
closer to the CO2 bands and therefore to better constrain the ka
Angström coefficient.
Disentangling O2* airglow from
O2 absorption in nadir-viewing spectraOverview
With the theoretical shape of the O2* dayglow emission spectrum
(Appendix A) and a selected VER vertical profile (e.g. Fig. D1), it is
possible to construct a synthetic spectrum of the dayglow in absolute
radiance units at the native spectral resolution of LBLRTM (several
105), which may be degraded to any spectral resolution to simulate
various instruments. For a given surface albedo and SZA, the radiance of
solar radiation scattered by the surface modified by O2 absorption may
also be computed, using LBLRTM. Adding both spectra is a simulation of what
will be seen by a nadir-viewing instrument (in the case of no aerosols) in
the region around 1.27 µm, as shown in Fig. 15 for two cases with
different albedos and SZA, where the two spectra are separated. The
contribution of atmospheric Rayleigh scattering is small at this wavelength
and ignored in this exercise.
Airglow (blue) and scattered sunlight (red) absolute
intensities and radiances (in W m-2 sr-1µm-1)
for two cases: (a) high albedo (A=0.55) and almost sub-solar
SZA of 1∘ (referred to as the Lmax case (maximum luminance
for instrument SNR estimates) and (b) low albedo (A=0.1) and
high SZA of 65∘ (referred as the Lmin case for SNR estimates
for minimum luminance). The depression on the continuum of reflected
sunlight is due to the O2 CIA (now included in HITRAN 2016; Gordon et al., 2017). Water vapour
absorption lines, which would be on the right part
of the spectra, are not represented here. The white area corresponds to the MicroCarb wavelength
coverage.
While the O2* emission spectrum (blue) is quite similar to the O2
absorption spectrum imprinted on the albedo (red in Fig. 15b), there
are five factors which make them different (as noted before), allowing their
disentangling by spectral profile fitting:
The transmission Tatm(τ)=exp(-τ) is not linear when
τ>1, while the emission stays linear.
Airglow lines are narrower than absorption lines (no pressure broadening
at high altitude).
The CIA affects only the O2 absorption spectrum.
As shown in Sect. 2 and Appendix A, the ratio of O2* emission to O2
absorption is not a constant, but a continuous function of the wavenumber ν (in cm-1).
The temperatures near the surface and in the mesosphere are different,
dictating different populations of rotational levels.
Basically a retrieval software tool allowing to determine from an observed
spectrum simultaneously the albedo, the O2 vertical column (or surface
pressure Psurf, when the water vapour is ignored), and the O2*
airglow intensity consists of two parts: a forward model to simulate what
would be observed (depending on the parameters to be retrieved) and a
scheme to minimize the χ2 of the fit of the observed spectrum by the
simulated spectrum (Levenberg–Marquardt).
For the present study, we have developed what we call the LATMOS breadboard,
a software (in Igor Pro language version 8.0 from WaveMetrics) dedicated
first to proof of concept for the use of the O2 band at 1.27 µm
in presence of O2* airglow contamination, and then we applied it to
SCIAMACHY nadir observations, showing that when the albedo is weak, the
O2* airglow intensity may be identified and its intensity actually
measured, as a scaling factor of a synthetic airglow spectrum (see below in
Sect. 6.2). We have also used the 4ARCTIC software to evaluate the
performance of the particular MicroCarb instrument configuration
(wavelength coverage and spectral resolution) and specified SNR as a
function of spectral radiance of the nadir-viewing scenery (Sect. 6.3
and Appendix D).
Airglow inversion in nadir SCIAMACHY spectra
The possibility to extract 1.27 µm airglow information from nadir
SCIAMACHY spectra is limited by the relatively low resolving power of this
instrument (about 860). The intensity of the sunlight reflected by the
surface and the atmosphere is in general much larger than the airglow in
this spectral band. This is true above continents but above ocean, where the
near infrared albedo is very low, it should be possible to extract the
airglow when the sky is clear. We tested this possibility using the spectral
inversion LATMOS breadboard, originally developed to test the possibility to
determine the airglow intensity in the 1.27 µm O2 band in the MicroCarb CO2 mission.
Algorithm used in the LATMOS inversion breadboard
As said above, the spectrum observed by SCIAMACHY in the 1.27 µm band
at nadir is the sum of the nadir solar flux reflected by the ground and the
atmosphere, partly re-absorbed by atmospheric O2, and the airglow
O2* emission spectrum. For clear-sky conditions, if we neglect the
reflection by the atmosphere, the reflected solar spectrum may be expressed
as
INadirλ,sza=AFλTatm1+1cosszacosszaπ,
where
A is albedo (assuming a Lambert law (isotropic) reflectance);
λ is wavelength;
F(λ) is solar spectrum outside atmosphere;
sza is solar zenith angle; and
Tatm is one-way vertical atmospheric transmission.
The airglow spectrum is assumed to be proportional to the logarithm of the
atmospheric O2 transmission at high altitude multiplied by the emission
to absorption ratio ε(λ)/SS(λ) (Eq. 4) as
explained in Appendix A. The relative intensity of the lines depends mainly
on temperature. To take into account this dependency, we represent the
airglow spectrum Ag(λ) as a linear combination of a warm spectrum
Agwarm(λ) and a cold spectrum Agcold(λ). These warm and
cold spectra are computed using US standard atmosphere transmission tables
from LBLRTM for nadir viewing around 50 km where the temperature is higher
(270 K) and 70 km where the temperature is lower (217 K):
9Agwarm(λ)≈Cnorm⋅[Ln(T51km(λ))-Ln(T49km(λ)]⋅[ε(λ)/SS(λ))]10Agcold(λ)≈Cnorm⋅[Ln(T71km(λ))-Ln(T69km(λ)]⋅[ε(λ)SS(λ)],
where Tz(λ) is the transmission at wavelength λ from
altitude z to the top of the atmosphere and Cnorm a normalization constant
determined in order that the integral of the spectrum is equal to the
integral of a reference spectrum, and using Eqs. (A15) and (4).
A Levenberg–Marquardt (L–M) method is used to determine the parameters
giving the best fit to SCIAMACHY spectra. The total column of O2,
assimilated to surface pressure, the airglow, the H2O column and the
albedo are inverted using the L–M converging scheme. As atmospheric
transmission depends non-linearly on the O2 column, its Jacobian is
calculated at ground level from the difference in transmission between the
ground and 1 km altitude.
The measured spectrum will therefore be expressed as
Fλ=K1⋅1-K2⋅INadir0kmλ+K2⋅INadir1kmλ⋅TH2OK5+K3⋅Agwarm(λ)+K4⋅Agcold(λ),
where K1 is the intensity of the reflected spectrum (proportional to the
albedo); K2 is the sensitivity of the reflected spectrum to
surface pressure; K3 is the warm component of the airglow spectrum; K4 is
the cold component of the airglow spectrum. TH2O is the atmospheric
transmission of water vapour for a reference column of H2O, and is
elevated to the power K5 to compute the transmission of another column
of water vapour, where K5 is the ratio of H2O column / reference column.
There are some weak lines of H2O in the area around
the 1.27 µm band that must be accounted for when comparing forward
simulations with real data. The coefficients K1, K2, K3, K4, K5
can be imposed or left free in the L–M inversion. The measurement
uncertainty in each spectel (spectral element) is assumed to be proportional
to the square root of the signal.
Application to SCIAMACHY nadir-viewing observations: retrieval
of O2* airglow intensity at 1.27 µm
The inversion scheme was applied to 3 d of SCIAMACHY nadir data above the
ocean (1–3 April 2007). For the L–M inversion, SCIAMACHY nadir
characteristics are taken from OSCAR
https://www.wmo-sat.info/oscar/satellites (last access: 11 June 2020)
Band 971–1773 nm
Spectral resolution 1.48 nm (resolving power 858 at 1.27 µm)
SNR 1500 at 25 W m-2 sr-1µm-1
The SNR is probably optimistic but it does not matter for the present study.
The uncertainty is not calculated from the data SNR but estimated roughly
from the dispersion of the airglow intensity results. The dispersion depends
very much on the intensity of the backscattered solar flux as shown in
Fig. 17, being high for large intensities and much smaller at lower
levels.
Figure 16 shows two examples of similar SZA spectra (35–36∘):
one with a high radiance over a thick cloud cover and one
with a low radiance on a clear day. In the case of high-reflected radiance
(Fig. 16a), the 1.27 µm band is dominated by O2 absorption,
with the airglow filling only slightly the bottom of the lines. In the case
of low reflected flux (Fig. 16b), the band is dominated by the
airglow.
Two examples of SCIAMACHY spectra at nadir: (a) above
thick clouds and (b) above clear ocean. The SCIAMACHY spectrum is in blue,
fitted spectrum in thick black, determined airglow spectrum in red, fitted
reflected spectrum in light black, and reflected spectrum without absorption in
dotted black. The word “radiance” in the label axis indicates the spectral
radiance or intensities.
In Fig. 17, it can be seen that when the reflected solar radiance is small,
the values of the inverted airglow intensity are little dispersed. On the
contrary, a very high dispersion is observed with a high-reflected radiance.
It is concluded that airglow inversion is only possible at low reflected
solar radiance, corresponding to situations above the ocean on a clear day
or at a high SZA. For the rest of this study of SCIAMACHY nadir
observations, we will limit the analysis to spectra with a reflected
radiance lower than 5 W m-2 sr-1µm-1.
Intensity of the inverted airglow as a function of the
reflected solar spectral radiance (in units of W m-2 sr-1µm-1). The points are coloured according to
the SZA from blue to red from 27 to 60∘.
In order to validate the airglow values inverted using nadir observations,
we compare them to the values inverted using limb observations (Fig. 18).
The latter are obtained by vertical inversion of the airglow profile
observed at limb and integrated over the vertical column as described in
Appendix B. The good agreement observed between the two methods gives us
confidence in the results obtained with nadir observations. On the other
hand, when the data are compared to our model, there is an underestimation of
the nadir intensity of the airglow simulated by REPROBUS v02 of about
10 %–15 %. This underestimation had already been found for limb comparisons.
The inverted airglow follows the same SZA dependence as that simulated by
REPROBUS. It can be noticed that for SZA >90∘, the
airglow values at nadir are divided into two families of different
intensity. Figure 19 shows these values with distinction between morning and
evening data. The higher values in the evening than in the morning are due
to the long lifetime of O2(a1Δg), greater than 1 h
in the absence of quenching in the high mesosphere. REPROBUS cannot
reproduce this morning–evening difference, the concentration of
O2(a1Δg) being calculated by assuming the photochemical
equilibrium.
Airglow intensity at nadir according to the SZA for the
month of April 2007: in red are SCIAMACHY measurements at nadir with a
reflected solar spectral radiance <5 W m-2 sr-1µm-1;
in blue are nadir intensities retrieved from SCIAMACHY measurements at the limb;
in black are simulations with REPROBUS v02. The airglow intensity at
its maximum reaches 3.2×1012 photons cm-2 s-1 sr-1,
which corresponds to 5 mW m-2 sr-1 (spread over a few
nanometres' wavelength), to be compared to a solar backscattered luminance
(radiance) of ∼5 to 70 W m-2 sr-1µm-1 (see Fig. 16).
Airglow nadir intensity versus SZA for
SZA >90∘; SCIAMACHY measurements are in red during the evening
and in blue during the morning; REPROBUS v02 simulations are in black. The observed
difference between morning and evening values is due to the long lifetime
of O2(a1Δg).
In order to evaluate the underestimation of the airglow by REPROBUS, a
linear regression is performed between the SCIAMACHY measurements with nadir
and the nearest REPROBUS values in time and position (Fig. 20). The slope of
the regression is 1.13. SCIAMACHY therefore sees on average 13 % more
airglow than estimated with REPROBUS. The regression line does not go
through the origin but to 2×1011 photons cm-2 s-1 sr-1.
This can be attributed to not taking into account in REPROBUS the airglow
above 80 km that is present both day and night.
Nadir airglow SCIAMACHY observed intensities versus
REPROBUS v02 simulation (same units on both axes, photons cm-2 s-1 sr-1). The dots are coloured according to
the reflected solar radiance from 0 in blue to 5 W m-2 sr-1 in red. Linear
regression of the correlation is in black. Airglow intensity values are
expressed in ph cm-2 s-1 sr-1.
We have demonstrated that, despite the moderate resolving power of SCIAMACHY
(∼860 against 25 000 for the future MicroCarb mission), it is possible
to extract the O2 airglow at 1.27 µm in nadir spectra provided
that the spectra are selected above the sea with a low reflected solar flux
(clear-sky conditions). The inverted airglow is on average 13 % higher
than that simulated by REPROBUS v02, in agreement with REPROBUS – SCIAMACHY
comparisons at limb (Sect. 4.2.1). The inverted airglow follows the
same SZA dependency as that simulated by REPROBUS except at twilight where
the morning–evening difference is not reproduced by REPROBUS which assumes
photochemical equilibrium. Sun et al. (2018) had concluded that it is not
possible to extract nadir airglow from SCIAMACHY measurements. We have shown
on the contrary that this is possible if we select the low flux spectra
reflected over the ocean in clear weather.
With MicroCarb data and its higher resolving power, one will be able around
coastal zones to compare the O2* airglow intensity measured above the
sea and above the ground, just nearby. They should be very similar, if the
spatial characteristic lengths of intensity variations are as large as found
by REPROBUS model, larger than the 2×2∘ REPROBUS resolution. This
comparison would provide an important “sanity check” of the retrieval of
O2 column, from which is derived Psurf assuming that the column
of dry air is strictly proportional to the O2 column, and adding the
pressure due to the column of H2O. Psurf is explicitly an element of
the state vector to be retrieved in GHG retrievals.
Following an interesting suggestion of one of the anonymous referees, we have tried
to estimate the small-scale horizontal
variations of O2* airglow from nadir-viewing SCIAMACHY data that could be due to gravity waves and are
not represented in REPROBUS CTM. This is not an easy task using the
relatively low spectral resolution of SCIAMACHY data. At this resolution,
spectral features in airglow and O2 absorption spectra are highly
correlated and the estimation of airglow is accurate only for very low
values of reflected solar flux as illustrated in Fig. 17, where a large
dispersion of airglow is observed for high values of reflected solar
radiance. There are not enough observations reaching a low level of solar
flux to plot maps of airglow. In spite of these limitations, we made an
attempt to estimate at least an upper limit for the small-scale variations
of airglow. We selected all pairs of nadir observations with reflected solar
flux <2 mW m-2 nm-1 sr-1, solar zenith angle <60∘ and distance <110 km. With these strong criteria, only
1 % of the observations were selected. The average relative difference in
airglow intensity between the pairs of observations was equal to 1.0 %. We
consider this value as an upper limit of the impact of gravity wave
perturbation in airglow intensity. At this level, the impact on the retrieval
of O2 column (and Psurf) and XCO2 will be very limited.
Surface pressure retrieval on simulated nadir spectra with
MicroCarb parameters
With the instrumental characteristics of MicroCarb (Sect. 5), we have
explored the accuracy of surface pressure retrieval in presence of the
contaminating O2* airglow, using the dedicated 4ARTIC v4.2 software.
The details of the exercise, done with several variants but using only the
band B4 (1.27 µm) are described in Appendix D, with a brief summary
below.
The requirements specified for the MicroCarb mission for Psurf accuracy
calls for a bias <0.1 hPa, and a random error <1 hPa. We
found out that, if the O2* airglow is ignored in the forward model used
for the retrieval, the bias on Psurf depends on the back scattered solar
light, but may amount to about 60 hPa (for a mean radiance with albedo of 0.2).
Putting the O2* airglow in the forward model, we found out that,
if the shape of the O2* airglow spectrum is perfectly known, a very
small bias is achieved (-0.002 hPa), with a random error of 0.88 hPa. In
reality, the shape in nadir viewing may depend on the temperature of the
atmosphere below, at the altitude of emission. We found out that we could
find a good fit to the simulated data by a linear combination of two airglow
models, at two different temperatures (similarly as in Sect. 6.2.1). In
this case, the random error was 0.88 hPa (compliant to requirements), and a
bias of -0.11 hPa, marginally non-compliant with the <0.1 hPa bias
requirement (Table 2 in Appendix D).
Therefore, we have demonstrated with these simulations that with the
spectral resolving power of MicroCarb (25 000), and only using the O2
IR band B4 at 1.27 µm, we may retrieve Psurf (or similarly the
O2 vertical column of dry air) with an accuracy almost compliant with
the MicroCarb requirements. Other tests were also made with 4ARTIC, by
using simultaneously both O2 bands B1 (0.76 µm) and B4
(1.27 µm) of MicroCarb. Of course, it improves very much the retrieval
accuracy, but since we are investigating the use of the B4 band because band
B1 is suspected to present some problems, we do not show the results here.
It will be particularly interesting to investigate the improvement of using
the B4 band, in addition to the B1, in the presence of aerosols. This is way
beyond the scope of the present study. However, the capability to
disentangle the spectrum of the O2* emission from the O2 IR
absorption with their fine structure should not depend very much on the
presence of aerosols, in view of their slowly varying spectral signature
with wavelength.
Conclusions
In this paper, we have reported the results of a 3 years (2016–2018)
scientific research effort to revisit the use of the O2 absorption band
at 1.27 µm in the problem of GHG retrieval from space observations of
the detailed spectrum of the solar radiation scattered by aerosols,
atmosphere and surface. It is widely recognized that this 1.27 µm
band being nearer in wavelength to the CO2 bands at 1.6 and
2.0 µm, it should be in principle less uncertain to “transport”
aerosols optical properties (wavelength dependent) from this O2 band to
the CO2 bands, than from the O2 A band at 0.76 µm. However,
the O2 absorption band at 1.27 µm is contaminated by a strong
airglow emission, presenting a similar spectral structure. This airglow is
mainly due to the spontaneous relaxation of excited oxygen O2(a1Δg) that is formed in the photolysis of ozone.
Nadir O2* airglow intensity
As a first approach, we needed to have an idea of the absolute amount of
O2* airglow radiance, in order to compare to the expected solar
scattered radiances in nadir viewing which are well documented. We could not
use the SCIAMACHY nadir observations around 1.27 µm for this purpose,
since they are mixed with solar scattered radiation from the surface.
Therefore, we used the limb-viewing observations of SCIAMACHY in this band,
which are not contaminated by atmosphere/aerosols scattering of solar
radiation when looking above ∼30 km altitude. We have
designed a method to retrieve the vertical profile of the O2* VER from
a limb scan of SCIAMACHY, taking into account the re-absorption of O2
along each LOS. A “fictitious” nadir-viewing intensity from SCIAMACHY
could therefore be derived by vertical integration of the VER. When
extracting the data for various periods of the year, it was found that the
major factor governing this O2* airglow intensity was the Solar Zenith
Angle, with little variability otherwise.
In parallel, we have conducted a major effort to model the intensity of the
O2* airglow emission, as an offline extension of the CTM REPROBUS
model providing the ozone field within the ECMWF meteorological field. We
found the same overall behaviour with SZA and some weak seasonal dependence.
A systematic comparison of 12 833 limb-scans acquired by SCIAMACHY
in 2007 and corresponding fictitious nadir-viewing intensities with the
prediction of our model (and also VER vertical profiles) indicates an
overall good agreement, although with a deficit of about 15 % in the
modelled intensity with regard to the SCIAMACHY intensity. For the time being, we
assign this deficit to be due at least partially (but possibly not totally)
to an ozone deficit in the REPROBUS model, as suggested by comparisons with
dayside GOMOS ozone vertical profiles obtained by the technique of star
occultation (not sensitive to an absolute calibration).
In summary, we have found that the intensity of the O2* airglow is well
behaved (with a weak horizontal variability), and quite predictable, with a
dispersion of probably only a few per cent around a climatological average.
Also, it should be almost as good as the ozone field in CTM models which are
run with the actual meteorological fields like ECMWF. Therefore, one could
imagine that a GHG nadir-viewing observation in the O2 band could be
corrected by subtraction of a model of the O2* airglow to get the pure
nadir solar scattered intensity (on which is imprinted the O2
absorption that we are analysing to get the O2 column). However, the
degree of accuracy that is needed for the determination of Psurf for useful
measurements of GHG gases is very large, about ∼0.1 hPa for
the bias and 1 hPa for random error. According to our simulations, and if
the airglow is ignored in the inversion but subtracted from a model, this
airglow intensity model would have to be accurate to ∼1.5 %
(for a mean radiance with albedo of 0.2) to achieve the 1 hPa random error.
Therefore, in most cases, it is insufficient to rely entirely on a model to
predict the actual airglow intensity to be subtracted from an observation.
We need to disentangle in the observed spectrum itself the contribution of
the airglow and the contribution of the solar scattered radiation. For this,
we will rely on the fact that the spectrum shape of the O2* airglow is
different from the O2 absorption spectrum. Still, if the high
horizontal smoothness of the intensity predicted by the model is confirmed
by the MicroCarb observations, it will help the retrieval. With the limited
disentangling capabilities of SCIAMACHY, we have shown that the data
indicate fluctuations (at constant SZA) smaller than 1 %, with a
negligible impact on the XCO2 retrieval accuracy.
Spectral shape of the O2* airglow
It is clear that if the dayglow spectrum of O2* were strictly identical
to the O2 absorption spectrum, it would be impossible to disentangle
one from the other, and one would have to subtract blindly a model of the
airglow emission from the observed spectrum, with an associated uncertainty.
While all the transitions of the O2 1.27 µm do exist in both the
absorption spectrum and in the O2* airglow emission spectrum, the
resulting spectra in a nadir-viewing geometry are different for two reasons
and under three aspects.
First, the emission happens at high altitude and low air densities, while
the absorption happens in the dense, lower atmosphere. Therefore, each
absorption line is broadened by collisions, as shown in Fig. 2. Also, while
for the shape of the airglow emission, all the spectral lines are
proportional to each other, on the contrary the radiance factor (=πB/solar
flux cos(SZA), B brightness) is modulated by the O2
transmittance spectrum (Tr(τ) =exp(-τ)) which is not linear for
the strong lines with large τ. Finally, there is the CIA effect
producing a broadband absorption (Fig. 15), which is totally negligible in
the upper atmosphere where emission takes place.
Second, for the same line of the electronic transition described by the rotational states J′ (upper level), J′′ (lower level), the relative populations are dictated by the numbers J′ (relevant for emission) and J′′ (relevant for absorption). For the P and R branches (ΔJ=±1) with J′≠J′′, the relative populations for emission and absorption are therefore different.
We have computed the theoretical shape of the dayglow spectrum from the
Einstein coefficients A21 of spontaneous emission from data contained
in the HITRAN database. It depends on the temperature of the atmosphere at
the place of emission (rotational relative populations). We have compared
our theoretical spectra degraded to the resolving power of SCIAMACHY (∼860) with limb observations of SCIAMACHY and found an overall excellent
agreement, validating our theoretical approach of the airglow spectrum. This
allowed performing some simulation exercises (see below in Sect. 7.3)
with good confidence about their ability to represent reality, showing that
with the resolving power of 25 000 of the MicroCarb instrument, it is indeed
possible to disentangle the airglow emission from the O2 absorption in
the O2 band at 1.27 µm. We note that the broad CIA band would
not require such a high spectral resolution to be useful for the
disentangling.
Simulation of Psurf retrievals
Simulation exercises performed with the 4ARCTIC software (Sect. 6.3
and Appendix D) have demonstrated that with the resolving power of MicroCarb
(25 000), and only using the O2 IR band B4 at 1.27 µm, we may
retrieve Psurf (or similarly the O2 vertical column of dry air) with an
accuracy (bias of -0.11 hPa) almost compliant with the strong MicroCarb
requirements: bias <0.1 hPa. The use of the B1 band (0.76 µm),
in addition to the B4 band, will most likely yield a better understanding of
the behaviour of this band B1 which at present is not fully understood. Once
better understood, it will certainly improve the accuracy of the Psurf
MicroCarb retrieval by constraining further the aerosols optical properties.
One may even hope that such a new knowledge could be used for an improved
retrieval of other GHG missions using only the A band.
One may wonder if the 25 000 resolving power is absolutely necessary to
disentangle the O2* emission from the O2 absorption at
1.27 µm with sufficient accuracy for GHG retrievals. Sun et al. (2018)
have explored spectral resolving power down to ∼4200, and found
(their Fig. 4) an accuracy on the O2 column of ∼0.35 % for
an SNR of ∼500 and a spectral resolving power of 5700, which would
result on an error of 1.4 ppm for XCO2 (0.35%×410 ppm), marginally
insufficient. Their whole analysis was done with already accounting for the
CIA O2 absorption, whose broad size and smooth pattern is insensitive
to spectral resolving power (Fig. 15). On the other hand, as can be seen in
Fig. 15 with the same number of spectels as MicroCarb and a coarser spectral
resolution (and sampling), the whole O2 band would be measured and
would possibly allow to better constrain the CIA absorption and O2
column retrieval. The larger spectral sampling gives additional photons per
spectel, which may be traded off for an increased spatial resolution.
However, the high resolving power of MicroCarb is an asset for the exact
knowledge of the instrumental spectral function which is also important for
the retrieval accuracy.
On the other hand, with our Igor–software breadboard tool, we have shown
that with the SCIAMACHY spectral resolving power (∼860), it was
possible to determine the intensity of the O2* airglow over oceans (low
albedo) but not to disentangle it and retrieve the CO2 column,
whatever the albedo, with a useful enough accuracy. We suggest though, on
the basis of our analysis and the results of Sun et al. (2018), that when
CIA is taken into account, a spectral resolving power of about 5000 and a
high SNR could possibly yield a sufficiently good accuracy on the Psurf
retrieval in the O2 band at 1.27 µm, and could improve the
treatment of aerosols and their wavelength dependent optical properties,
being nearer the CO2 bands, for a better XCO2 retrieval. The
CO2 Mission (space mission CO2-M) is sponsored by European
Community, and developed and operated by ESA, as a space segment for the
monitoring of CO2 anthropogenic emissions, with potentially a series of
three operational spacecraft on Sun-synchronous orbits. The present typical
baseline optical design of the CO2-M has a spectral resolving power of
about 6300 at 0.76 µm, and 5400 for the weak CO2 channel at
1.60 µm. There is no channel for the O2 band at 1.27 µm
but it can be imagined by interpolation that a new channel for this band
derived from the baseline design would have a resolving power of about
5750. Based on the discussion in the previous paragraph and on the whole
present study, we advocate for the inclusion in the design of CO2-M
instrument of a channel at 1.27 µm.
TANSO-GOSAT is a Fourier transform spectrometer dedicated to GHG
monitoring on JAXA GOSAT space platform. TANSO-GOSAT2 was launched in
October 2018 and use bands at 0.76 (for O2), 1.60 µm weak
CO2 band and 2.0 µm (strong CO2 band). We advocate for
the inclusion of an additional channel dedicated to the measurement of the
O2 band at 1.27 µm in the design of future
Thermal And Near infrared Sensor for carbon Observation (TANSO) instruments, as
well as for the Chinese family of TanSat following the TanSat-1 already
collecting data.
Finally, we have mentioned in Appendix E other cases where an airglow
emission could contaminate nadir-viewing observations from space analysing
scattered sunlight, since the LOS goes across all the airglow layers (Fig. 1).
We listed a few commonly used absorption bands and their potential
contamination by airglow: the O2 molecule and the band A emission,
CO2, H2O, CH4 and CO molecules where fluorescence induced by
solar light could be a source of contamination. These situations would
deserve more detailed calculations to estimate the impact on their column
abundance retrievals.
Data availability
Primary data from GOMOS/Envisat and SCIAMACHY/Envisat are available at the corresponding ESA websites. There were many studies in the present paper, which generated many different datasets. The data processed in the frame of this project, sponsored by CNES within a contract to ACRI-ST, are in principle not available. This is true also for REPROBUS models outputs. However, some specific requests for some data will be examined on a case-by-case basis, subject to CNES authorization. The requests should be sent to alain.hauchecorne@latmos.ipsl.fr.
Modelling the airglow emission of O2* excited moleculeSpectroscopy: the various electronic states of the O2 molecule
Figure A1 (from Khomich et al., 2008) presents the various electronic states
of the di-oxygen molecule O2 and the names of the transitions between
them. The O2 molecule, being composed of two identical atoms, is said
to be homopolar. The fundamental state X3Σg- is put at a
reference energy level of 0. The number 3 indicates a triplet state, which
may be decomposed in three sub-states with very nearby energies. The descending
arrows in Fig. A1 indicate transitions to a lower level, corresponding to
the emission of one photon. The reverse process corresponds to the
absorption of one photon. We describe briefly below three of the transitions
indicated in Fig. A1 which are the most relevant to GHGs, since O2
is used as a reference to determine the mixing ratio of CO2. We can
make use of the energy/wavelength conversion:
Eev=hν=hcλ=1242.26λnm.
The Schumann–Runge band is in the far UV. The UV solar flux
dissociates also O2 molecules, producing O atoms, which may recombine
with O2 to form O3, which absorbs in the nearer end of the UV spectrum. All together,
O2 and O3 are protecting life (and DNA molecules) from harmful
solar UV.
The “atmospheric” band is the transition (b1Σg+→X3Σg-), around 760 nm, also
called the A band from Fraunhofer's early nomenclature, or “atmospheric band”,
heavily used in GHG studies.
The “infrared atmospheric” band is the transition (a1Δg→X3Σg-), around 1270 or 1.27 µm in the near
infrared, sometimes called the O2 IR band,
or 1Delta band (according to Gordon et al., 2010). This band is the
subject of the present study.
Because the O2 molecule is homopolar, it has no electric dipolar moment
and in principle electronic transitions are forbidden. The electronic
transitions can only happen thanks to the existence of a magnetic dipole
moment (M1) and/or a quadrupolar electric moment (E2). As a consequence,
spontaneous transitions from a given electronic state down to the
fundamental state X3Σg- are unlikely; therefore, the
lifetime of such a state is rather long: 13 s for the atmospheric A band at
760 nm, transition (b1Σg+→X3Σg-) (Mlynczak and Solomon, 1993) and 75 min for the O2
IR band at 1.27 µm, transition (a1Δg→X3Σg-) (Lafferty et al., 1998). Therefore,
the O2 molecule excited at level a1Δg (often
represented by O2* or O2(1Δ) in this paper) which
results from ozone photo-dissociation has plenty of time to reach thermal
equilibrium with ambient gas, and the various states of vibration rotation
will be populated according to a Boltzmann law (therefore, depending on the
local temperature T) modulated by the rotation quantum number J, with a
statistical weight 2J+1 as described later below.
For the O2 IR band at 1.27 µm, the transition (a1Δg→X3Σg-) is mainly due to
a magnetic dipole (M1). There is, however, also a system of absorption lines
due to the electric quadrupole: (E2), identified for the first time both in
atmospheric absorption spectra (looking at the Sun) and in laboratory
experiments (Gordon et al., 2010). The overall absolute strength of this (E2)
system is about 215 times weaker than the (M1) system (Gordon et al., 2010).
Observations of the 1.27 µm in the atmosphere of the Earth from the ground
One difficulty for ground-based observations of the 1.27 µm emission
is that most of the emission is absorbed by O2 before reaching the
ground, letting 4 %–10 % come down to the ground. A second difficulty is
that there is a strong contribution of scattered solar radiation by the
atmosphere/dust. It is (now) clear what should be the optimal observing
conditions:
looking near the zenith to minimize O2 absorption in the lower
atmosphere (from a ground-based observing station located at high altitude,
the O2 absorption will be a little bit reduced) and
looking just after the sunset, when the Sun is still illuminating the ozone
layer producing this emission, between 30 and 80–90 km, but not illuminating
the lower atmosphere, to avoid Rayleigh/Mie scattering which produces a
strong sky background signal.
The first observation from the ground was reported by Lowe et al. (1969),
followed by Baker et al. (1975) and Pendleton et al. (1996), all fulfilling
the above-mentioned optimal conditions.
Observations on Venus and Mars
The emission of Venus at 1.27 µm was discovered by Pierre Connes
(Connes et al., 1979), then studied in particular by Crisp et al. (1996). It
is due to the recombination in Reaction (3) (O+O+M). On Mars, both types of
emissions (2 and 3) exist. The martian ozone emission was discovered by
Pierre Connes (Noxon et al., 1976), and the martian recombination emission
(Reaction 3) by the OMEGA experiment aboard Mars Express (Bertaux et al., 2012).
Computation of a synthetic spectrum of O2* emission from HITRAN 2016
The aim of this section is to describe a way to compute what could be the
emission spectrum of the emission of O2* in the dayglow, relevant to
nadir GHG daylight observations. In an early phase of the present studies in
2016, we made the following crude approximation. We computed, from HITRAN
2016 (Gordon et al., 2017), the local high-resolution spectrum of absorption
by O2 molecule at a variety of altitudes. Then, we assumed that the
emission spectrum shape (but not magnitude) was identical to the absorption
spectrum. In the following, we detail how we can compute more accurately a
theoretical spectrum of the local emission of the O2* molecule from the
data that are present in HITRAN 2016 line-by-line information and some
additional considerations.
Computation of the total emission rate of
O2* molecule coming from ozone dissociation
As described in Sect. 4.1.1, the REPROBUS CTM model is used to compute
the 3-D distribution of ozone as a function of space and time, based on a set
of chemical reactions, solar photolysis of various species, and the actual
meteorological fields of winds, temperature, and pressure procured by ECMWF.
Then, an additional model computes the photo-dissociation of ozone with the
UV solar spectrum of the day, yielding to a 3-D distribution of the
concentration [O2*] of species O2* (electronic state
a1Δg) in units of cm-3. As said above, the lifetime
of this electronic state is about 75 min, corresponding to a spontaneous
emission rate Aglobal of 1/(75×60)=2.22×10-4 s-1. For low
altitudes, one must take into account the quenching of this excited state by
collisions with all other gases (including O2), mainly at the lowest
altitudes (<50 km). Ignoring the quenching, the rate of emitted
photons, VER in units of photons (cm3 s)-1, may
be computed from
VER=AglobalO2*.
Computation of the airglow detailed line-by-line intensity
The same principle may be applied to the detailed emission spectrum by
computing a VER for each transition line Li of the 1.27 µm band,
line by line. This can be done from the data contained in the HITRAN
database for this O2 electronic transition. All the existing lines
(within a certain wavelength interval) in absorption also exist in
emission, and only those exist. In Eq. (A2), Aglobal must be
replaced by the Einstein coefficient of spontaneous emission A(Li)
which is present in the HITRAN table for each transition (HITRANonline website;
https://hitran.org, last access: 11 June 2020).
In the present study, we have used only
the 16O16O main isotope of O2. For higher degrees of
refinement that may be necessary in view of the high accuracy required on
XCO2, other O2 isotopes should also be considered.
The concentration [O2*(Ei)] of the O2* molecule must be
computed in all possible energy levels Ei. Ei depends on the
rotational number J′ (J′ for upper state, J′′ for lower state which here is
irrelevant) and the vibration number (mainly V′=0 but some with V′=1).
The concentration [O2*(Ei)] will depend on the rotational
statistical weight 2J′+1 and the temperature T, and is also proportional
to [O2*].
VERLi=ALiO2*(Ei)
Computing the distribution of O2*
molecules among the various energy levels
In their 2006 paper, Simeckova et al. (2006) describe “the calculation of
the statistical weights and the Einstein A coefficients for the 39
molecules and their associated isotopologues/isotopomers currently present
in the line-by-line portion of the HITRAN database”. This is all that is
needed to calculate second members of Eq. (A3) for all allowed
transitions Li, giving the rate of emission of the corresponding
spectral line VER(Li).
In an approximation of a two-level system (upper m and lower n levels are
denoted as 2 and 1, respectively) at LTE (local thermodynamic equilibrium),
we have the well-known equations linking the Einstein A coefficients and
B coefficients:
A4g1B12=g2B21A5A21=8πhν3B21,
where A21 (spontaneous emission) is in s-1, B12 (absorption)
and B21 (stimulated emission) are in cm3 (J s2)-1,
and g1 and g2 are the statistical weights of the
levels 1 and 2, respectively.
We start from Eq. (17) of Simeckova et al. (2006) with molecules in the
lower level (level 1) and the upper level (level 2) (much less numerous at atmospheric
temperatures), to describe their relative distribution according to their
energy level E1i or E2i and temperature T, the index i indicating
a particular rovibrational level defined by J′ and V′. If N is the total
number of molecules per unit volume at the temperature T, the population
N2i of one of the energy levels E2i of the upper level (level 2) is equal
to
N2i=g2iNQtot(T)e-c2E2iT,
and a similar equation for N1i and the energies E1i of the lower
level (Eq. 17 of Simeckova et al., 2006). Here, Qtot(T) is the
total internal partition sum of the absorbing gas at the temperature T,
g2i=2J′+1, and E2i is the energy of the upper state in units
of wavenumber (cm-1). c2 is the second radiation constant,
c2=hc/kB, where c is the speed of light, h is the Planck constant,
and kB=1.38065×10-23 J K-1 is the Boltzmann constant.
The total number of molecules per unit volume N=ΣN1i+ΣN2i, and Qtot(T) is the sum of Qtotlo(T) and
Qtotup(T), respectively, the internal partition sum of the lower
level and the upper level. The index i refers to all possible values of J′,
starting at J′=2 (J′=0 and J′=1 do not exist). We have, by definition,
QtotupT=∑ig2ie-c2E2iT.
We may find the value of Qtot(T) in Table 1 of the paper of Simeckova
et al. (2006). For instance, Qtot(T=296 K) =215.77 for the main
oxygen isotopologue 16O16O. The temperature 296 K is a reference
temperature for the HITRAN database. For our purpose, we have to find
Qtotup(T) for the upper level of the transition, from a summation
described in Eq. (A7). The summation must be not over all the
transitions but over all energy levels. Since the HITRAN database consists
in a list of transitions, some caution must be used when using the HITRAN
database, in order to extract a list of energy levels. Once we have
Qtot(T), the total internal partition sum, we may then compute all
values of N2i, for the required temperature, from the distribution of
the excited molecules between the various energy levels from Eq. (A6).
However, Qtotup(T) is very small when referred to all molecules N.
For convenience, we have replaced in Eq. (A9) the values E2i by
E2i-E20, where E20 is the energy of the lowest energy
populated level with J′=2. With this approach, we found new values
Q′totup=Qtotupexp(c2E20/T)
for the upper level: Q′totup(T=296 K) =147.196 for 296 K, and
Q′totup(T=200 K) =100.143. In Fig. A2, both the
exponential term and the relative population are represented as a function of J′, a product of
the exponential term and the statistical weight 2J′+1. Only the V′=0 levels are
kept here, because levels V′=1 levels are weakly populated, though they are
present in the line list that are extracted from HITRAN line-by-line
database in our selected wavelength interval of interest (transition (1, 1).
Partition function of the O2*
molecule as a function of rotation state J′ for T=296 K. Blue
triangles indicate exponential terms for population, with 1 being the lowest.
Black circles indicate the relative population computed from information contained
in the HITRAN database of transitions. It allows to retrieve the allowed
values of J′. The first point is for J′=2 with a value of
5=2J′+1, and exp(0)=1. The sum of all
relative populations is Q′totup by definition.
Figure A3 presents the various energy levels (in cm-1) of the upper state,
the O2* molecule. The energy is here counted above the lowest energy
level of the fundamental state X3Σg-.
Energy levels (in cm-1) of the
excited molecule O2* as a function of rotational and
vibrational quantum numbers J′ and V′.
At LTE and atmospheric temperatures, Qtotup≪Qtotlo≈QtotT with a factor Qtotup(T)QtotT near 10-16, while
Q′totup(T)QtotT is approximately
between 0.1 and 1.
The partition functions described above, as well as the connection between
the A21 and the strength of the transition SHIT, were established
with consideration of a gas at LTE conditions. But then the value of A21
does not depend if there is LTE or not. In particular, when a population of
excited O2* molecules is produced from ozone photolysis, the ratio
ΣN2i/N may be much larger than with LTE conditions. Whatever
rotational level in which they will be produced by photolysis, they will
soon re-equilibrate among the various rotational upper levels because the
radiative lifetime is quite long versus the collision time with ambient
molecules which tend to relax to the collisional equilibrium of the various
rovibrational levels, without changing ΣN2i/N (in the absence
of quenching).
Computing the total decay rate of the excited molecule
O2*
The actual distribution Q(J′,T)/Qtotup(T) of the excited molecule
O2* is a function of the rotational state J′ and of the temperature,
such that the sum over all J′ is equal to 1:
∑QJ′,TQtotupT=1.
Each particular rotational level may decay through several transitions, each
transition with its own decay rate, or Einstein probability of spontaneous
emission, called A21, which is given in HITRAN tables of line-by-line
lists. The total (average) decay rate from the upper level is obtained by
summing all A21 on all transitions weighted by the relative population
of each rotational level:
A21 tot=∑A21J′QJ′,TQtotupT.
Since there is the emission of one photon around 1.27 µm for each
decay of one excited O2* molecule, A21 tot is the weighted sum of
all rates of all transitions going down, which is the total emission rate,
the total number of photons emitted in the whole band per second by a
single molecule of O2*.
Then, we must multiply by the number density of O2* to get the volume
emission rate in photons (cm3 s)-1. We found from the HITRAN data
that the total decay rate is A21 tot=2.29×10-4 s-1,
slightly different from 2.22×10-4 s-1 derived from the rounded
value of 75 min of the lifetime quoted by Lafferty et al. (1998). We may compute
the lifetime 1/A21 tot=4367 s ∼73 min. The excited molecule
O2* will, in average, stay excited for more than 1 h (in the
absence of quenching, de-excitation by collisions without the emission of
one photon; not addressed here).
It must be realized that it is experimentally very difficult to measure
directly such a long lifetime. Instead, because the values of A21 are
connected to the values of absorption coefficients B21, it is easier to
measure the absorption of O2 molecules and then make the appropriate
calculations to derive the A21 values, according to principles
explained in Simeckova et al. (2006), which have been used to fill the
HITRAN line-by-line lists with A21 rates for each transition.
Emission rate per excited molecule
O2*eps=ε(k) from
Eq. (13) in photons s-1 per molecule O2* (left scale)
compared to the absorption line strength SS at 296 K found in HITRAN data
(in units of cm-1 (cm2 per molecule)). See text for more explanations.
Computing the emission spectrum of the excited molecule
O2*
The emission rate per O2* molecule ε(k) of a
transition k is obtained by multiplying the Einstein coefficient A21(k) by the
relative population of the upper level:
εk=A21kQJ′k,TQtotupT.
When plotted as a function of wavelength of transition k, it represents the
emission spectrum for one molecule, per molecule and per second. It is
displayed in Fig. A4 for 296 K (right scale) and compared to the absorption
line intensities SS which are found in the line-by-line HITRAN line list
(left scale). The distribution of the lines in three branches is clearly
observed (Q branch: J′-J′′=0; P branch: J′-J′′=-1; R branch: J′-J′′=+1). The transitions from V′=1 to V′′=1 are very weak and near the
zero line.
Therefore, a spectrum of the local emission in the band could be computed
by describing each emission transition by a Gaussian with an appropriate
width (associated with the temperature), adding all transitions to form a full
spectrum, and multiplying by the actual density of O2* molecules.
However, we have implemented another method to take advantage of LBLRTM
software (Clough and Iacono, 1995)
which computes for the terrestrial atmosphere absorption spectra (either
local or integrated over one LOS) from the HITRAN database.
Indeed, with the adequate scaling of both the right and left scales of Fig. A4,
it is noted that the strength of absorption lines are just above the
emission rates on the left side of the graph (short wavelength), while it is
the reverse on the right side. As we shall see below, there is a theoretical
reason for this progressive change of the emission-to-absorption-strength ratio.
Theoretical computation of the emission / absorption ratio
We first repeat here Eq. (19) from Simeckova et al. (2006), which
links A21 to the line strength SS (below, Sν(k,T)) in which k
designates one transition from energy level E1k with a wavenumber ν0:
Sν(k,T)=g2Qtot(T)A21k8πcν02exp-c2E1kT1-exp-c2ν0T.
We may extract the expression of A21(k) from Eq. (A11) and put it
in Eq. (A10). Taking into account that E2k-E1k=ν0 and that the relative population of energy level E2k is
QJ′k,T=2J′+1exp-c2E2kT=g2exp-c2E2kT,
we could find a very simple result on the ratio of emission ε(k) to absorption line strength Sν(k,T) for each line:
εkSν(k,T)=8πcν02Qtot(T)QtotupT1expc2ν0T-1.
In Fig. A5, the ratio of our calculated emission to the HITRAN line strength
SS for all transitions within our spectral interval is plotted for two
temperatures (black and blue dots), together with the analytical formula (Eq. A13)
(a continuous function of wavenumber ν0) computed on a
regular wavelength grid. Both share the same scale on the left: there is a
perfect coincidence, which validates our derivation of Eq. (A13).
For two different temperatures (296 and 217 K),
the ratios of our calculated emission rate to the HITRAN line strength SS are
plotted (respectively, black and blue dots) for all 375 transitions of
HITRAN table (between 1.2238 and 1.32068 µm). The solid lines
(respectively, red and blue) are computed from the analytical formula (Eq. A13)
on a grid of wavelength–wavenumber using the same scale on the left as the ratio of
emission to line strength in units of photons cm-2 s-1/ cm-1, where cm-1 is a wavenumber unit.
Practical method to produce a synthetic emission spectrum
In order to simulate a local emission spectrum, we could use the method
exposed above in Appendix A4.5, giving the emissivity per molecule O2* for each line,
and then distribute this emissivity over a Gaussian attached to each line
and add all line contributions spectrally. We have developed another method,
capitalizing on the capabilities of LBLRTM software (Clough and Iacono
1995). With LBLRTM, we may compute a local absorption spectrum of O2
(for instance, computing the vertical atmospheric transmission between 67
and 68 km altitude, Tr(λ)). This transmission is linked to the
local absorption a(λ) by
A14Trλ=exp-aλdzA15oraλ=-lnTrλdz.
The shape of the local emission spectrum Em(λ) of O2*
molecules is then obtained by multiplying a(λ) by Eq. (A13)
of the ratio of emission to absorption (ε/SS), which depends only
of the wavenumber ν and temperature T, according to a continuous
function. The wavenumber ν in cm-1 is equal to 10 000 /λ
(in µm). Then, the obtained local emission spectrum Em(λ)
has to be normalized (by integration over wavelength) to the actual VER
for which we wish to compute the local emission rate spectrum, yielding
Emn(λ) where the letter n stands for “normalized”.
The advantage of this approach is that LBLRTM computes the local absorption
spectrum with a number of effects (line broadening, pressure shifts,
Gaussian profiles, etc.) that may become non-negligible at low
altitudes. Applying Eq. (A13) on the continuous spectrum, instead of
applying it to the discrete set of wavelengths of each transition is
justified by the fact that ν and Eq. (A13) vary very little
over the spectral extent of one individual line.
In order to compute the brightness spectrum of the O2* emission along
any LOS, one has to integrate Emn(λ) over length through the
atmosphere. The “self-absorption” by O2 molecules along the LOS must
be accounted for, except in regions where it is negligible (high altitudes,
say >80 km).
Retrieval of
O2(1Δ) VER from SCIAMACHY limb radiances
We have developed a numerical scheme to retrieve the vertical distribution
of the O2(1Δ) VER from a series of
limb radiances obtained during a SCIAMACHY limb scan, taking into account
the absorption along the LOS by background O2 of the
O2(1Δ) emission between the location of the emission and
the spacecraft (sometimes this O2 absorption is called self-absorption,
improperly in our opinion, because the O2(1Δ) molecule is
different from the background O2, as long as it is in the excited state
(1Δ)).
The case with no absorption: the onion-peeling technique
When there is no absorption, the radiance B is related to the integral along
the LOS of the VER (or emissivity) ε(s), s being an abscissa
along the LOS.
B=14π∫εsdsB is the wavelength-integrated spectral radiance, expressed in
photons (cm2 s sr)-1, while ε(s) is here spectrally
integrated and expressed in photons (cm3 s)-1.
A classical way to retrieve a vertical distribution of ε from a
series of radiance measurements at the limb is the onion-peeling method
(Fig. B1), which assumes that the emissivity (or VER) is locally spherically
symmetric, depending only on the altitude. Furthermore, the problem is
discretized by dividing the atmosphere in spherical layers, in which the VER
is constant.
Geometry of the LOS at the limb of an
emission organized in spherical layers. The various LOS are obtained either
by drifting the observer S into an orbit with a fixed inertial direction
(case of GOMOS/Envisat) or by angular scanning (case of
SCIAMACHY/Envisat).
Let p0, …pj be the series of impact parameters of viewing LOS with
p= tangent altitude z+Rearth, p0 the largest. We define a
series of spheres with r0>p0 and decreasing radii. Let
Lj,n= length of one of the two equal segments (j,n) between spheres of
radius rn and rn+1 along the LOS with impact parameter pj.
We have the following expressions (B2):
L0,0=r02-p02L1,1=r12-p12L1,0=r02-p12-L1,1…Lj,n=rn2-pj2-rn+12-pj2.
For the last segments when j=n, the formula is slightly different because
the sphere of radius rj+1 is irrelevant:
Lj,j=rj2-pj2.
The radiances B(p) may then be expressed (B3) as a function of ε(z):
Bp0=14πεz0⋅2⋅L0,0Bp1=14πεz0⋅2⋅L1,0+εz1⋅2⋅L1,1…Bpn=14π2∑kεzkLn,k.
This is a linear system of n equations with the n unknowns ε(zj). This system may be written under a matrix form 4πB=Mε, M being a
triangular matrix of order n, the number of atmospheric layers. Elements of
the matrix are the lengths of LOS segments within a layer between two
spherical shells. Therefore, the matrix may be simply inverted to yield the
vector ε from the vector of measurements
B:
ε=4πM-1B.
The case with absorption: a modified onion-peeling technique
When absorption by O2 is considered, Eq. (18) is modified
into
B=14π∫εsexp(-τ(s))ds,
where τ(s) is the optical thickness between the emission point and the
observer (here assumed to be outside of the atmosphere). The corresponding
attenuation factor is
Trs=exp-τs.
In reality, ε, τ(s), Tr(s), and B all
depend on wavelength λ. However, the quantities τ(s,λ),
Tr(s,λ) may be computed for a given atmospheric model defined
by a vertical profile of temperature T(z) and pressure p(z), with the dry
air density n(z)=p(z)/kT(z) and O2 density 0.2095n(z). We made
use of the LBLRTM code and HITRAN2016 database to conduct such
computations.
The detailed spectral shape of the local emission of the O2* molecule ε(z,λ) may be computed from the analysis of Appendix A. Therefore,
we can compute a wavelength-integrated attenuation factor between the emission location and the
external observer, independent of the actual value of the local VER(z)=∫ε(z,λ)dλ for every cell of the
onion-peeling scheme described above. We keep the matrix approach, but now
each element of the matrix is a length of a segment multiplied by the
pre-calculated attenuation factor. While in the standard onion-peeling
scheme, there are two identical segments, symmetric with regard to the tangent
point, giving equal contributions to the observed intensity; the attenuation
factor is different for the two segments (one in the foreground, the other
in the background).
For a given limb observation, each SCIAMACHY spectrum is integrated in
wavelength to yield the total limb radiance measurements B(z), and
Eq. (B4), where M matrix is modified to include the
attenuation factor, is used to derive the vertical profile of the volume
emission rate ε(z). It may be shown that the
attenuation factor FASj,n affecting both foreground and background
segments on one LOS may be written as
FASj,n=0.5∫τkmzn,λ⋅exp-τj,nfλ+exp-τj,nbλdλ∫τkm(z,λ)dλ,
where τkm(znλ) is the optical thickness per kilometre of O2
absorption at wavelength λ and altitude zn, τj,nfλ and τj,nbλ are, respectively, for
the foreground segment and the background segment the optical thickness of
O2 absorption from the segment to the observer, along the LOS.
Comparison of methods used by others
Zarboo et al. (2018) have also used the SCIAMACHY limb scans in order to
retrieve the vertical profile of the O2* emissivity. However, their
technique is quite different. They find a best fit to the whole series of
observed spectra with a model of the vertical distribution of spectral
emissivity, but they do not account for attenuation by O2. Therefore,
they must underestimate their emissivities more and more with lower
altitudes. Their method does not need to know the state of the atmosphere
nor the theoretical shape of the emissivity; in principle, if their SNR
would be large enough, they could interpret the spectral shape of their retrieved
emissivities in terms of local temperature, by comparing with model
predictions built on our approach developed in Appendix A. Because they have
used a special SCIAMACHY mode of observation dedicated to the MLT, in which the scanning at the limb is in the range
of 50–150 km, the fact that they did not account for O2 re-absorption
along the LOS is probably not very important. They have limited their study
to altitudes >50 km.
Sun et al. (2018) have studied also the SCIAMACHY spectra in the O2*
band for the same purpose as us: for a better retrieval of the XCO2
mixing ratio. In order to retrieve the spectral emissivities at each
altitude, they have developed two methods. The first one (they call it
“linear inversion”) is identical to the method of Zarboo et al. (2018) and
does not account for O2 absorption. In their second method (they call
it “onion-peeling”), they account for O2 absorption, except for the
two tangent heights where absorption is negligible, from which they can
derive the local temperature T by comparison with a model of the spectral
local emissivity quite similar to what we developed in Appendix A. Then,
they use the Mass Spectrometer – Incoherent Scatter (MSIS) model pressure to get the O2 density to compute the
absorption in the second layer from the top and propagate downward the
computations. They can therefore compute the vertical temperature profile at
each altitude.
Sensitivity of VER retrieval to the choice of atmospheric model
and nadir radiance estimate
In our retrieval scheme of VER from observed limb radiances, an atmospheric
model is needed to compute the absorption by background O2. We tested
the sensitivity of the VER retrieval to the choice of the atmospheric model,
as shown in Fig. B2 for a SCIAMACHY limb observation obtained on 1 January 2007
(at latitude of -39.9∘ and SZA of 37.2∘, from
product 20070101_1256). The various VER profiles were
obtained either with US STANDARD (a mean model for all seasons and
latitudes), or with the variants SUBARCTIC_SUMMER,
SUBARCTIC_WINTER, and MIDLAT_SUMMER (this last
one should correspond best to the conditions of this particular
observation). The relative differences may reach ±10 % between 40
and 60 km, with smaller differences above 60 km (less absorption) and larger
differences below 35 km (more absorption, but regardless the VER is small).
Therefore, for our following studies of many SCIAMACHY limb scans, we have
systematically used the most relevant standard profile, according to the
latitude and season: a so-called CLIMATO_ADAPTED atmospheric
profile (“adapted climatology”).
In particular, for each studied limb scan, we have first inverted the
SCIAMACHY limb total radiances to retrieve a VER vertical distribution. Then,
we could compute what would be the nadir radiance in the O2 band that
should have been observed with such a profile, to simulate the MicroCarb
geometry of observation or any other GHG monitoring system. The vertical
integration was done above 30 km up to 80 km to be consistent with REPROBUS
model which stops at 80 km. Absorption by O2 may be computed in the
nadir-viewing geometry, though attenuation in this geometry (altitude
z>30 km) is small (2 % for the Q branch; less outside of the Q
branch).
VER airglow profiles obtained by taking into account
O2 absorption using a climatological atmospheric
profile: SUBARCTIC_SUMMER (green curve),
SUBARCTIC_WINTER (blue curve), US STANDARD (black curve), and
CLIMATO_ADAPTED (red curve). In the case of this profile
measured in January at latitude of -39.9∘, the adapted
climatology corresponds to the MIDLAT-SUMMER climatology.
Comparison of measured (GOMOS) and modelled (REPROBUS)
ozone vertical profiles
The photo-dissociation of ozone is the main mechanism for producing
O2(a1Δ) airglow in the atmosphere. It is therefore
important that the ozone profile calculated by the REPROBUS model is
accurate. In order to verify this condition, comparisons were carried out
with the ozone profiles measured by the GOMOS instrument aboard Envisat.
GOMOS (Bertaux et al., 2010) measured ozone profiles by the stellar
occultation method under night and day illumination conditions. For daytime
occultations, there is a contaminating signal both from the illuminated limb
and from the nadir emission scattered by the GOMOS baffles. Though the
processing pipelines have been designed to correct for these contaminations,
the resulting uncertainties in ozone density retrieval may be larger,
depending on the geometrical conditions of the occultation and the altitude,
lower altitudes having more stray light. Therefore, we are separating night
and day conditions for the comparison.
Nighttime ozone profiles
One example of REPROBUS and GOMOS individual ozone concentration profiles
under night conditions for Envisat orbit no. 25402 (8 January 2007)
is presented in Fig. C1. Figure C1a displays the ozone
concentration measured by GOMOS and predicted by REPROBUS at the same time
and location. As seen on a log scale (in order to accommodate the
5 orders of magnitude of variation of ozone with altitude), the agreement is
remarkable between GOMOS and REPROBUS in the altitude range of 15–65 km. Even
wiggles in the vertical profiles in the range of 15–65 km are present both in
the model and in the data. Figure C1b represents the relative
difference (GOMOS-REPROBUS)/REPROBUS on a linear scale.
(a) One typical example of comparison of a GOMOS vertical
ozone profile (black curve) versus REPROBUS prediction (red curve). The GOMOS
profile was observed during the night on 8 January 2007. (b) Relative
difference (GOMOS-REPROBUS)/REPROBUS.
There is a significant difference above 60 km, where REPROBUS overestimates
the amount of ozone relative to GOMOS. At night, GOMOS ozone profiles show a
strong ozone minimum around 80 km. This is a true feature, which can be seen
directly on the light of the star that increases again when the LOS passes
at this altitude during the occultation of the star. This ozone “hole”,
explained by loss reactions with OH radicals at night, is not reproduced by
REPROBUS. The reason for this discrepancy is the assumption in the model
that ozone concentration is much larger than atomic oxygen at night and thus
can be set as equal to the odd oxygen family (i.e. O3≃O3+O=Ox). This approximation is justified in the stratosphere and the
lower mesosphere but is wrong in the upper mesosphere, where oxygen atoms
have a lifetime of the order of a day and a concentration similar to ozone
during the night (e.g. Brasseur and Solomon, 2005). This shortcoming of
REPROBUS will be corrected in the next version of the model. It must be
noted, however, that this O3 overestimation in the upper mesosphere by
REPROBUS only occurs in nighttime conditions.
Below 20 km, the REPROBUS and GOMOS (night) curves are also diverging.
Occultation measurements are less accurate below 15–20 km, because of the
attenuation of the star signal, so this difference must be considered with
caution. In any case, this bias is not relevant to our study since the
airglow of O2(a1Δ) is negligible below 30 km.
We then compared all GOMOS ozone profiles in night occultation for 2007 with
REPROBUS for six different stars and plotted the relative difference
(GOMOS-REPROBUS)/REPROBUS for the four brightest stars in Fig. C2.
The general trends observed in Fig. C2 are fairly similar for the four stars
and in line with what is observed in the single profile of Fig. C1. The
overestimation of ozone by the REPROBUS model above 60 km is confirmed.
Relative difference between the GOMOS and REPROBUS ozone
nighttime profiles for all 2007 occultations of the four brightest stars (S0001, S0002, S0009, and S0023),
represented here, respectively, in panels (a), (b), (c), and (d). Each dot represents one GOMOS measurement of ozone. The colour of the
dot is related to the latitude of each profile (see the colour scale). The thick
black line represents the median and the thin black lines represent the
16th and 84th percentiles of the distribution of the relative
difference.
(a) One example of comparison of a GOMOS vertical
dayside ozone profile (black curve) versus REPROBUS prediction (red curve). The
GOMOS profile was observed on 5 August 2007, at an SZA of
38∘. (b) Relative difference (GOMOS-REPROBUS)/REPROBUS.
Below 60 km, there is an underestimation of ozone in REPROBUS relative to
GOMOS with a maximum of about -15 % at 55 km, which gradually decreases to
0 % at 20 km. At the location of the maximum airglow (45–50 km), the bias
is about -8 % to -10 %. This lack of ozone in REPROBUS is a major reason to
explain why the airglow emission estimated by model is lower than observed
by SCIAMACHY (Fig. 11). However, some exercises done by arbitrarily multiplying
the REPROBUS O3 profiles by a factor of 1.2 (not shown here)
show a small remaining underestimation of the airglow calculated by the
model. This discrepancy certainly warrants future detailed studies that are
well beyond the scope of the present paper.
Daytime ozone profiles
During the day, one comparison of REPROBUS to GOMOS observations is
displayed in Fig. C3 for an SZA of 38∘. There is a deficit
(∼20 %) of ozone in REPROBUS versus GOMOS around 60 km. However,
high-quality dayside data are scarce with GOMOS, and definitive conclusions
cannot be drawn at this stage.
We may summarize the comparisons GOMOS/REPROBUS with the following points:
In principle, only dayside ozone is relevant for the prediction of
O2* airglow at 1.27 µm.
GOMOS ozone concentration vertical profiles show quite similar values
below 60 km between day and night, and larger values of O3 at night
above 60 km, a feature well understood from mesospheric chemistry.
There is a known shortcoming of the chemistry of REPROBUS model affecting
strongly nightside predictions above 60 km, quite apparent with GOMOS ozone
nightside comparisons (too much ozone in REPROBUS).
Because the O3 diurnal variation is small below 60 km (there, we are
more confident in the model than in GOMOS dayside data to estimate the small
ozone diurnal variation), the comparison GOMOS/REPROBUS on the nightside
showing a deficit (10 %–20 %) of the model versus GOMOS ozone below 60 km
may be applied also to the dayside. Therefore, the comparison of GOMOS ozone
data with REPROBUS ozone suggests that one part of the airglow discrepancy
is due to a deficit in the ozone predicted by REPROBUS in the range of 40–60 km
(Fig. C3).
Surface pressure retrieval on simulated nadir spectra
contaminated by O2* airglow
We performed the inversion of nadir-simulated spectra with the 4ARTIC v4.2
software in order to have an estimation of the performance of the O2
1.27 µm band on the retrieval of the surface pressure (Psurf) when
the spectra are contaminated by the O2* airglow signature.
In order to build the synthetic spectra simulating the data to be fitted in
our inversions, we followed the following three steps:
First, we compute a very-high-resolution “reflected” spectrum in the B4
MicroCarb band (1.27 µm), without noise, by calling the radiative
transfer model 4AOP (sampling 0.001 cm-1). This spectrum is then
degraded at the resolution of the MicroCarb instrument (resolving power of 25 000)
and resampled on the MicroCarb wavelength grid.
We then add an airglow spectrum as seen from TOA with nadir view. This spectrum
is assumed to be proportional to the logarithm of the atmospheric O2
transmission at high altitude multiplied by the emission to absorption ratio
ε(λ)/SS(λ) as explained in Appendix A. In the
frame of this study, we developed a software tool for building
such a spectrum.
Finally, we generate 1000 noisy spectra by adding a randomly generated Gaussian
noise with amplitude based on the MicroCarb SNR.
We tested two inversion methods:
Method no. 1 involves simultaneous inversion of airglow and Psurf.
Method no. 2 involves inversion of only Psurf using a spectel mask, eliminating from
the fit the most contaminated spectels, as recommended by Sioris (2003) for
the O2 A band.
For both methods, the performance of estimation of Psurf is based on a
Monte Carlo approach with the inversion of 1000 noisy spectra. The two
statistical performance estimators are the Psurf random error, which is
calculated as the standard deviation of the 1000 retrieved Psurf values, and
the Psurf bias, which is calculated as the difference between the Psurf true
value (1013 hPa) and the average of the 1000 retrieved Psurf values.
The inversion scheme used by 4ARTIC is based on the optimal estimation
method (OEM) described by Rodgers (2000), which uses a Bayesian approach (use
of a priori information to constrain the inversion). The elements of the
state vector are Psurf, mean albedo, spectral slope of albedo, dry air
mixing ratios XCO2, XH2O, and (only for method no. 1) the airglow
scaling factor(s).
Both methods are described below with their associated results of the Psurf
performance estimators. We remind that the MicroCarb requirements on the
Psurf retrieval for a median intensity luminance Lmoy scenario are 0.1 hPa
in terms of bias and 1 hPa in terms of random error. This reference luminance
value Lmoy corresponds to an observation with SZA of 36∘ and albedo
at 1.27µm=0.2. For both methods, these values have been used for
computing the reflected spectrum with 4AOP (see step 1 above). Only a clean
atmosphere scenario, i.e. without aerosol, was tested for both methods.
Method no. 1
In the first method, we try to invert the O2* airglow at the same time
as Psurf (and the other state vector elements). We tested three different
approaches concerning the inversion of the airglow:
The shape of the airglow spectrum is considered to be perfectly known. In
the state vector, we invert an “airglow scaling factor” whose associated
Jacobian is the airglow spectrum that we put in the simulated data. The
starting value for the scaling factor is equal to 0 and the true value which
is expected to be retrieved is 1.
The shape of the airglow spectrum is not considered to be perfectly known.
We still use a single “airglow scaling factor” but its associated Jacobian
spectrum has a slightly different shape than the spectrum that we put in the
simulated data. We took for the Jacobian spectrum the airglow spectrum
obtained with the REPROBUS VER profile in co-location with the SCIAMACHY
profile used to build the airglow spectrum put in the simulated data. This
is illustrated in Figs. D1 and D2. Thus, the error done on the shape of the
airglow spectrum is representative of the error that the REPROBUS model does
on the computation of an airglow VER profile.
The shape of the airglow spectrum is considered not perfectly known. However,
we try to “approach” it as much as possible by inverting a linear
combination of a cold airglow and a warm airglow (different mesospheric
temperatures), both having slightly different shapes. A cold airglow
spectrum is built with our tool by using a SCIAMACHY VER profile at
SZA of 85∘ (whose peak is around 60 km) and a warm spectrum by
using a SCIAMACHY VER profile at SZA of 36∘ (peak near 45 km).
These two spectra are then normalized to the intensity of the airglow
spectrum that is put inside the simulated spectrum which we wish to invert.
The model spectrum that we wish to best approximate this simulated spectrum
will be a linear combination of these two normalized spectra, with a sum of
coefficients near unity, which is more convenient for the description of the
mesosphere. The cold and warm “normalized” spectra are used in the inverse
model as Jacobians of two elements of the state vector, respectively, a cold
and a warm airglow scaling factor.
The results of the Psurf inversion by method no. 1 for the three approaches,
using only the 1.27 µm band, are presented in Table D1. They show that the MicroCarb B4 band allows
retrieving Psurf with a random error of 0.88 hPa, which is compliant with the
MicroCarb requirement (1 hPa). Adding a second element of airglow does not
seem to increase the Psurf random error. When considering that the shape of
the airglow spectrum is perfectly known (test no. 1), the bias on Psurf is
completely negligible. However, in true conditions, this will not be the case
since the shape of the airglow spectrum is dependant of not perfectly
determined variables like the temperature profile or the airglow VER
profile. When forcing the shape of the spectrum with an error representative
of the error done by REPROBUS model on the determination of the airglow VER
profile (test no. 2), we obtain a significant bias of 0.26 hPa. However, by
letting the inversion process adjusting the shape of the airglow spectrum,
that is to say, using a linear combination of a cold and warm airglow (test no. 3),
the Psurf bias is reduced to -0.11 hPa, which is very close to the
MicroCarb requirement (0.1 hPa).
Method no. 2
In the second method, the airglow is not inverted. Instead, we used a mask of
spectel in order to discard the spectels which are the most contaminated by
the airglow emission. Indeed, since the emission lines are thinner than the
absorption lines and centred on the same wavelength, we can apply a narrow
mask on the central part of each absorption line which will remove most of
the airglow emission lines while keeping the wings of the absorption lines
as well as the CIA continuum, both bringing useful information to retrieve
Psurf. As we consider that the airglow spectrum can be a priori estimated (by a
model) and corrected with an accuracy of about 90 %, the airglow spectrum
which is put in the simulated spectrum to be inverted is only 10 % of the
intensity of the airglow spectrum used in method no. 1.
We tested three pixel masks which remove all spectels whose intensity is
higher than 10 %, 1 %, and 0.1 %, respectively, of the maximum intensity
of the airglow spectrum (located at 7882 cm-1). This is illustrated in
Fig. D3. The results of the Psurf inversion with the three masks, using only the 1.27 µm band, are presented in Table D2.
VER SCIAMACHY profile used to build the
synthetic airglow spectrum put in the simulated spectrum to be
inverted (red); associated (co-located) REPROBUS VER profile,
showing the underestimation of REPROBUS airglow modelling above 50 km (blue). The
airglow synthetic spectra associated with these two VER profiles are
illustrated in Fig. D2. They are very similar.
(a) Synthetic airglow spectra corresponding to
the (red) SCIAMACHY VER profile of Fig. D1, (blue) associated REPROBUS VER
profiles of Fig. D1. For the three tests, the red spectrum is put in the
simulated data to invert. For test no. 1, it is also used as the Jacobian
of the inverse model. For test no. 2, the blue spectrum is used as
Jacobian of the inverse model (blue). The Jacobian spectra of test no. 3 are
not represented here. (b) Zoom on the region of the Q branch.
The spectels contaminated by airglow are a source of bias on the Psurf
retrieval. The more contaminated spectels we discard with a mask, the more
this bias is expected to be reduced. However, at the same time, the more
spectels we discard, the more we increase the random error since we lose some
useful information on Psurf in the absorption bands. Thus, we are looking for
a mask which provides a good compromise between bias and random error. Table D2 shows, however, that the spectel mask method does not allow meeting the
MicroCarb requirement simultaneously in term of bias and random error using
the MicroCarb B4 band only. Values are, however, not that far from MicroCarb
requirements, and mask number no. 3, for example, allows being in spec in terms
of bias (-0.04 hPa) while keeping a reasonable random error of 1.52 hPa.
This method can thus be foreseen as a good alternative to the method no. 1
and is worth being tested on MicroCarb data when available.
Results of the Psurf inversion by method no. 1 (airglow also
inverted). Values printed in bold are non-compliant with MicroCarb
requirement. Non-bold indicates compliant values.
TestDescriptionPsurf BiasRandom error(hPa)(hPa)No. 1Shape perfectly known; one airglow parameter inverted.-0.0020.88No. 2Shape not perfectly known. Error on the shape;0.260.88one airglow parameter inverted.No. 3Shape not perfectly known. Cold and warm airglow parameters.-0.110.88
Airglow spectra at 1.27 µm, at MicroCarb
resolution, used in our tests of inversion of Psurf with method no. 2 for
several values of the spectel mask: no mask (pink), 10 % mask (blue),
1 % mask (red), and 0.1 % mask (green). The three masks remove, respectively,
14 %, 31 %, and 44 % of the spectels in the B4 band.
Results of the inversion of Psurf in MicroCarb band B4 (1.27 µm) with
method no. 2 (spectel mask). Values printed in bold are non-compliant
with MicroCarb requirement. Non-bold indicates compliant values.
The contamination of the absorption O2 band at 1.27 µm by the
corresponding O2* airglow emission is an extreme case, in view of the
strong intensity of the airglow (up to 40 megarayleigh). However, this
particular band is not a unique case. In all nadir-viewing observations from
space analysing scattered sunlight, the LOS goes across all the airglow
layers (Fig. 1), and their emissions are superimposed on the scattered
sunlight spectrum. We list below a few commonly used absorption bands and
their potential contamination by airglow.
For the O2 molecule (band A), Zarboo et al. (2018)
retrieved the VER of the
emission by the O2 molecule excited in the 1Σ state from SCIAMACHY limb observations when
it decays to the fundamental in the A-band O2 at 0.76 µm
(their Fig. 5). One could estimate the nadir-viewing radiance from the total
column. This is a contribution to radiance measurements in the A band, which
(to our knowledge) has been ignored up to now, that should be subtracted
systematically from any nadir A-band measurements, with its detailed
spectral shape, before analysis. Sioris (2003) computed a synthetic spectrum
of the A-band O2 emission, including resonance scattering of solar
photons. Then he performed a column O2 retrieval, with and without the
airglow contribution. In spite of the fact that the airglow may account for
more than 10 % of the total radiance in the core of strong lines, the bias
(negative) when neglecting to subtract the A-band airglow was found to be
only 0.0061 % on the O2 column retrieval. An independent study
performed in the frame of the present work found -0.0073 %, confirming
the Sioris estimate. However, both studies have assumed a total absence of
this A-band emission below 50 km, while the corresponding VER profile
derived from SCIAMACHY observations suggests a fast increase of the emission
with decreasing altitude around 50 km (Fig. 5 of Zarboo et al., 2018).
Not accounting for this A-band emission below 50 km will be more detrimental
(for the accuracy of Psurf retrieval with A band) in low-albedo regions and
low SZA values (resonance scattering penetrates more deeply at low SZA).
The CO2 molecule illuminated by the Sun is
subject to resonance fluorescence, with non-LTE emission in the bands where
the absorption is measured (weak or strong CO2 bands). This is to our
knowledge not yet accounted for in GHG retrievals. In addition, there is a
(0, 1) transition of the O2(1Δ) around 1.58 µm that
interferes with a pair of CO2 weak bands. Though this emission is about
50 times smaller than the 1.27 µm emission, it is preferable to use
the other pair of CO2 bands at 1.60 µm, as done by OCO-2 and
MicroCarb instruments.
For the CH4 molecule, Lopez-Puertas et al. (2005)
reported limb observations of non-LTE emission of CH4 in the
thermal IR at 7.6 µm from Michelson Interferometer for Passive
Atmospheric Sounding (MIPAS)/Envisat Fourier transform spectrometer (FTS) instrument. It began to
be noticeable at 45 km altitude and represented up to 60 % of the
emission at 70 km. Therefore, this non-LTE emission triggered by solar
radiation forms an airglow layer that is intercepted in a nadir-viewing
geometry. Such a known-to-exist contribution should be subtracted
systematically from nadir-viewing measurements (i.e. Infrared Atmospheric Sounding Interferometer (IASI) measurements).
This contribution has been ignored by de Wachter et al. (2017), analysing
nadir IASI methane measurements. A rough estimate from figures of
Lopez-Puertas et al. (2005) indicates a non-LTE horizontal emission of
100 nW cm-2 sr-1, over 4 cm-1 at 50 km altitude. A vertical viewing
would reduce this value by a Chapman factor of several tens, probably below
the noise of IASI measurements (∼10 Nw (cm2 sr cm-1)-1.
However, it should be noted that there is also certainly some non-LTE
emission below 50 km (difficult to see for MIPAS), and that this spectrum
has a very similar shape as the LTE emission. Therefore, the non-LTE
emission should be modelled, scaled to MIPAS determinations at high
altitudes, and subtracted blindly form nadir viewing (IASI-type
measurements). It is possible that other bands of CH4 used for GHG
retrievals may be similarly affected.
The fluorescence of H2O molecule excited by
solar radiation has been observed at 2.67 µm in the coma of several
comets (i.e. Bockelée-Morvan et al., 2015) and should be present in the
atmosphere of the Earth, as well as in the other lines which are present in
GHG bands (i.e. at 2.0 µm). These H2O airglow emissions
superimposed to surface radiation have not been subtracted from nadir
observations or their intensity even estimated, to the best of our
knowledge.
The fluorescence of CO molecule excited by solar radiation has been
observed at 4.53 µm in the upper atmosphere of Venus with high
spectral resolving power (R∼43000) by Marcq et al. (2015).
The same must happen in the atmosphere of the Earth; introducing a bias of
about 1 % on the CO column retrieval (rough estimate) may not be so
important because it is well below the currently achieved accuracy of some tens of percent on CO columns.
Processing of SCIAMACHY level-1c radiance data
Here, we show some figures describing our processing of level-1c SCIAMACHY
radiance data, as explained in Sect. 3.1, in order to get a “pure”
radiance spectrum of the O2* airglow. We also show in Fig. F5 a
typical distribution along one orbit of the SCIAMACHY limb observations used
in our analysis.
This high-altitude spectrum recorded above 105 km
contains some residual spectral (readout) patterns left from the calibration
step and is subtracted from all measurements obtained at lower altitude in
the same scan limb.
Spectra corrected from the high-altitude spectrum
still showing two bad pixels at wavelength 1262.267 and 1282.128 nm.
We replaced their value by the average of their two surrounding pixels
to obtain spectra of Fig. F3. The tangent altitude of the LOS is colour
coding each spectrum.
Same as Fig. F2, after correction of the two bad
pixels. In addition to the O2* airglow, there is the
radiance of solar light scattered by air and aerosols, increasing when
tangent altitude is decreasing. The strong absorption of
O2 in the 1.27 µm band becomes obvious at the
lowest altitudes.
Airglow spectra obtained from Fig. F3 by subtracting a
linear interpolation based on the two constant values of the continuum (one
on each side) estimated from the median value of all points outside the
O2* band. This correction is valid above
∼20 km tangent altitude.
Geographic positions of SCIAMACHY LOS tangent points at
the limb (by groups of four) for Envisat orbit no. 25293, starting on
1 January 2007 at 01:12 UT. The green points are the locations of the limb spectra
which are compared with our theoretical derivation in Sect. 3.
Author contributions
JLB conceptualized revisiting the use of the
1.27 µm O2 band for GHG retrieval and prepared the manuscript
with contributions from all co-authors. JLB and PA developed the theory of the
airglow emission spectrum and the construction of a synthetic spectrum. FMB is
the principal investigator of the MicroCarb mission, in the frame of which
this study was performed. This study was organized by DJ and technically
managed by LB at ACRI and scientifically by JLB at LATMOS. JLB and AH
developed the algorithms for the analysis of SCIAMACHY data. FL developed
the REPROBUS model and the O2* airglow model and participated in
comparisons with GOMOS and SCIAMACHY data. PL maintained the 4ARCTIC
software. LB performed the SCIAMACHY data analysis for VER retrieval from
limb measurements, and comparison with REPROBUS model and ozone GOMOS data.
LB computed also the airglow synthetic spectra and made comparisons with
SCIAMACHY limb spectra. AH developed the LATMOS breadboard inversion tool
and conducted the retrieval of airglow intensity from nadir observations of
SCIAMACHY.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The present study was led by LATMOS and ACRI-ST, and organized by CNES (Centre National d'Etudes Spatiales) in 2016–2018 in the frame of preparation of the MicroCarb mission, a space mission entirely dedicated to the study of CO2. Jean-Loup Bertaux, Alain Hauchecorne, Franck Lefèvre, and François-Marie Breon acknowledge support of Centre National de la Recherche Scientifique (CNRS). Jean-Loup Bertaux and Pavel Akaev acknowledge partial support from the Ministry of Education and Science of the Russian Federation grant no. 14.W 03.31.0017. We wish to acknowledge the useful involvement of other ACRI-ST company members: Nicolas Chapron, Jean-Luc Vergely, Stéphane Ferron, and Meriem Chakroun; and Claude Camy-Peyret for useful discussions. We are grateful to the enormous work performed by the four referees leading to the correction of mistakes (mainly language, but not only) in the original manuscript. We wish to thank ESA for the Envisat programme, allowing the operations, data processing, and archiving of SCIAMACHY and GOMOS data.
Financial support
This research has been supported by CNES in the frame of the MicroCarb mission, with a contract passed to the ACRI-ST company, and the Ministry of Education and Science of the Russian Federation (grant no. 14.W 03.31.0017).
Review statement
This paper was edited by Christof Janssen and reviewed by David G. Johnson and four anonymous referees.
ReferencesAtkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F.,
Hynes, R. G., Jenkin, M. E., Rossi, M. J., and Troe, J.: Summary of
Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry IUPAC
Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry,
available at: http://www.iupac-kinetic.ch.cam.ac.uk/summary/IUPACsumm web latest.pdf (12 June 2020),
2005.Baker, D., Steed, A., Huppi, R., and Baker, K.: Twilight Transition
Spectra Of Atmospheric O2 IR Emissions, Geophys. Res. Lett., 2, 235–238, 10.1029/GL002i006p00235, 1975.Bertaux, J. L., Kyrölä, E., Fussen, D., Hauchecorne, A., Dalaudier, F., Sofieva, V., Tamminen, J., Vanhellemont, F., Fanton d'Andon, O., Barrot, G., Mangin, A., Blanot, L., Lebrun, J. C., Pérot, K., Fehr, T., Saavedra, L., Leppelmeier, G. W., and Fraisse, R.: Global ozone monitoring by occultation of stars: an overview of GOMOS measurements on ENVISAT, Atmos. Chem. Phys., 10, 12091–12148, 10.5194/acp-10-12091-2010, 2010.Bertaux, J. L., Gondet, B., Lefèvre, F., Bibring, J. P., and Montmessin,
F.: First detection of O2 1.27 µm nightglow emission at Mars
with OMEGA/MEX and comparison with GCM model predictions, J. Geophys. Res.,
117, E00J04, 10.1029/2011JE003890, 2012.Bockelée-Morvan, D., Debout, V., Erard, S., Leyrat, C., Capaccioni, F.,
Filacchione, G., Fougere, N., Drossart, P., Arnold, G., Combi, M., Schmitt,
B., Crovisier, J., de Sanctis, M. -C., Encrenaz, T., Kührt, E., Palomba,
E., Taylor, F. W., Tosi, F., Piccioni, G., Fink, U., Tozzi, G., Barucci, A.,
Biver, N., Capria, M. -T., Combes, M., Ip, W., Blecka, M., Henry, F.,
Jacquinod, S., Reess, J.-M., Semery, A., and Tiphene, D.: First observations
of H2O and CO2 vapor in comet 67P/Churyumov-Gerasimenko made by
VIRTIS onboard Rosetta, A and A, 583, A6, 10.1051/0004-6361/201526303, 2015.Boesch, H., Baker, D., Connor, B., Crisp, D., and Miller, C.: Global
characterization of CO2 column retrievals from shortwave-infrared
satellite observations of the Orbiting Carbon Observatory-2 mission, Remote
Sens., 3, 270–304, 2011.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S.,
Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission
Objectives and Measurement Modes, J. Atmos. Sci., 56, 127–150, 1999.
Brasseur, G. P. and Solomon, S.: Aeronomy of the Middle Atmosphere,
Springer, New York, USA, 2005.
Burkholder, J., Sander, S., Abbatt, J., Barker, J., Huie, R., Kolb, C.,
Kurylo, M., Orkin, V.,Wilmouth, D., and Wine, P.: Chemical Kinetics and
Photochemical Data for Use in Atmospheric Studies, Evaluation Number 18,
NASA panel for data evaluation technical report, JPL Publication 15-10, Jet
Propulsion Laboratory, Pasadena, 2015.Burrows, J. P., Hölzle, E., Goede, A. P. H., Visser, H., and Fricke, W.:
SCIAMACHY–scanning imaging absorption spectrometer for atmospheric
chartography, Acta Astronaut., 35, 445–451,
10.1016/0094-5765(94)00278-T, 1995.Butz, A., Hasekamp, O. P., Frankenberg, C., and Aben, I.: Retrievals of
atmospheric CO2 from simulated space-borne measurements of 30
backscattered near-infrared sunlight: accounting for aerosol effects, Appl.
Opt., 48, 3322–3336, 10.1364/AO.48.003322,
2009.CEOS: A constellation architecture for monitoring carbon dioxide and methane
from space, Report prepared by the CEOS Atmospheric Composition Virtual
Constellation Greenhouse Gas Team, available at: http://ceos.org/document_management/Virtual_Constellations/ACC/Documents/CEOS_AC-VC_GHG_White_Paper_Version_1_20181009.pdf (last access: 20 June 2020), 2018.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and Other Biogeochemical
Cycles, in: Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.Clough, S. A. and Iacono, M. J.: J. Geophys. Res., 100, 16519, 10.1029/95JD01386, 1995.Connes, P., Noxon, J. F., Traub, W. A., and Carleton, N. P.: O2 (1) emission in the day and night airglow of Venus, Astrophys. J., 233,
L29–L32, 10.1086/183070, 1979.Crisp, D., Meadows, V. S., Bézard, B., de Bergh, C. Maillard, J.-P., and
Mills, F. P.: Ground-based near-infrared observations of the Venus
nightside: 1.27 µm O2(a1Δg) airglow from the
upper atmosphere, J. Geophys. Res., 101, 4577–4593,
10.1029/95JE03136, 1996.Crisp, D., Atlas, R., Breon, F.-M., Brown, L., Burrows, J., Ciais, P.,
Connor, B., Doney, S., Fung, I., Jacob, D., Miller, C., O'Brien, D., Pawson,
S., Randerson, J., Rayner, P., Salawitch, R., Sander, S., Sen, B., Stephens,
G., Tans, P., Toon, G.,Wennberg, P.,Wofsy, S., Yung, Y., Kuang, Z.,
Chudasama, B., Sprague, G., Weiss, B., Pollock, R., Kenyon, D., and Schroll,
S.: The Orbiting Carbon Observatory (OCO) mission, Adv. Space Res., 34,
700–709, 10.1016/j.asr.2003.08.062, 2004.Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, 10.5194/amt-10-59-2017, 2017a.
Crisp, D., Bösch, H., Brown, L., Castano, R., Christi, M., Connor, B.,
Frankenberg, C., McDuffie, J., Miller, C. E., Natraj, V., O'Dell, C.,
O'Brien, D., Polonsky, I., Oyafuso, F., Thompson, D., Toon, G., and Spurr,
R.: OCO (Orbiting Carbon Observatory)-2 Level 2 Full Physics Retrieval
Algorithm Theoretical Basis, Tech. Rep. OCO D-65488, NASA Jet Propulsion
Laboratory, California Institute of Technology, version 3.0 Rev 0, Pasadena, CA, 2017b.De Wachter, E., Kumps, N., Vandaele, A. C., Langerock, B., and De Mazière, M.: Retrieval and validation of MetOp/IASI methane, Atmos. Meas. Tech., 10, 4623–4638, 10.5194/amt-10-4623-2017, 2017.Ebojie, F., von Savigny, C., Ladstätter-Weißenmayer, A., Rozanov, A., Weber, M., Eichmann, K.-U., Bötel, S., Rahpoe, N., Bovensmann, H., and Burrows, J. P.: Tropospheric column amount of ozone retrieved from SCIAMACHY limb–nadir-matching observations, Atmos. Meas. Tech., 7, 2073–2096, 10.5194/amt-7-2073-2014, 2014.Evans, W. F. J., Hunten, D. M., Llewellyn, E. J., and Vallance Jones, A.:
Altitude Profile of the Infrared Atmospheric System of Oxygen in the
Dayglow, J. Geophys. Res., 73, 10.1029/JA073i009p02885, 1968.Gao, H., Xu, J., Chen, G., Yuan, W., and Beletsky, A. B.: Global
distributions of OH and O2 (1.27 µm) nightglow emissions observed
by TIMED satellite, Sci. China Technol. Sc., 54, 447–456,
10.1007/s11431-010-4236-5, 2011.Gophstein, N. M. and Kushpil, B. I.: Dayglow of the upper atmosphere of the
Earth in the 1.25 µm, Cosmic Res 2:619–622, 1964; Planet. Space Sci., 13, 457–460, 1965.Gordon, I. E., Kassi, S., Campargue, A., and Toon, G. C.: First identification
of the a1Δg-X3Σg- electric
quadrupole transitions of oxygen in solar and laboratory spectra, J. Quant.
Spectrosc. Ra., 111, 1174–1183, 2010.Gordon, I., Rothman, L., Hill, C., Kochanov, R., Tan, Y., Bernath, P., Birk,
M., Boudon, V., Campargue, A., Chance, K., Drouin, B., Flaud, J.-M., Gamache,
R., Hodges, J., Jacquemart, D., Perevalov, V., Perrin, A., Shine, K., Smith,
M.-A., Tennyson, J., Toon, G., Tran, H., Tyuterev, V., Barbe, A.,
Császár, A. A., Devi, V., Furtenbacher, T., Harrison, J., Hartmann,
J.-M., Jolly, A., Johnson, T., Karman, T., Kleiner, I., Kyuberis, A., Loos,
J., Lyulin, O., Massie, S., Mikhailenko, S., Moazzen-Ahmadi, N.,
Müller, H., Naumenko, O., Nikitin, A., Polyansky, O., Rey,
M., Rotger, M., Sharpe, S., Sung, K., Starikova, E., Tashkun, S., Auwera, J.
V., Wagner, G., Wilzewski, J., Wcisło, P., Yu, S., and Zak, E.: The HITRAN
2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203,
3–69, 10.1016/j.jqsrt.2017.06.038, 2017.
Hasekamp, O., Hu, H., Detmers, R., and Butz, A.: Algorithm Theoretical Basis
Document for the RemoTeC XCO2 and XCH4 Full Physics Products, ESA GHG CCI, SRON, ESA, Noordwijk, the Netherlands, 2015.Heymann, J., Reuter, M., Hilker, M., Buchwitz, M., Schneising, O., Bovensmann, H., Burrows, J. P., Kuze, A., Suto, H., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kawakami, S., Kivi, R., Morino, I., Petri, C., Roehl, C., Schneider, M., Sherlock, V., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm, Atmos. Meas. Tech., 8, 2961–2980, 10.5194/amt-8-2961-2015, 2015.Khomich, V. Y., Semenov, A. I., and Shefov, N. N.: Airglow as an indicator
of upper atmospheric structure and dynamics, Springer, Berlin, Heidelberg, 10.1007/978-3-540-75833-4, 2008.Kuang, Z., Margolis, J., Toon, G., Crisp, D., and Yung, Y.: Spaceborne measurements of atmospheric CO2 by high-resolution NIR
spectrometry of reflected sunlight: An introductory study, Geophys. Res. Lett., 29, 11-1–11-4, 2002.Kyrölä, E., Andersson, M. E., Verronen, P. T., Laine, M., Tukiainen, S., and Marsh, D. R.: Middle atmospheric ozone, nitrogen dioxide and nitrogen trioxide in 2002–2011: SD-WACCM simulations compared to GOMOS observations, Atmos. Chem. Phys., 18, 5001–5019, 10.5194/acp-18-5001-2018, 2018.Lafferty, W. J., Solodov, A. M., Lugez, C. L., and Fraser, G. T.: Rotational
line strengths and self-pressure-broadening coefficients for the 1.27 µm a1Δg-X3Σg 0-0 band of
O2, Appl. Opt., 37, 2264–2270, 10.1364/AO.37.002264, 1998.Lefèvre, F., Brasseur, G. P., Folkins, I., Smith, A. K., and Simon, P.:
Chemistry of the 1991–1992 stratospheric winter: Three-dimensional model
simulations, J. Geophys. Res.-Atmos., 99, 8183–8195, 10.1029/93JD03476, 1994.Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz, J., Manning, A. C., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Jackson, R. B., Boden, T. A., Tans, P. P., Andrews, O. D., Arora, V. K., Bakker, D. C. E., Barbero, L., Becker, M., Betts, R. A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Cosca, C. E., Cross, J., Currie, K., Gasser, T., Harris, I., Hauck, J., Haverd, V., Houghton, R. A., Hunt, C. W., Hurtt, G., Ilyina, T., Jain, A. K., Kato, E., Kautz, M., Keeling, R. F., Klein Goldewijk, K., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lima, I., Lombardozzi, D., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Nojiri, Y., Padin, X. A., Peregon, A., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Reimer, J., Rödenbeck, C., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Viovy, N., Vuichard, N., Walker, A. P., Watson, A. J., Wiltshire, A. J., Zaehle, S., and Zhu, D.: Global Carbon Budget 2017, Earth Syst. Sci. Data, 10, 405–448, 10.5194/essd-10-405-2018, 2018.Llewellyn, E. J., Lloyd, N. D., Degenstein, D. A., Gattinger, R. L.,
Petelina, S. V., Bourassa, A. E., Wiensz, J. T., Ivanov, E. V., McDade, I.
C., Solheim, B. H., McConnell, J. C., Haley, C. S., von Savigny, C., Sioris,
C. E., McLinden, C. A., Griffioen, E., Kaminski, J., Evans, W. F., Puckrin,
E., Strong, K., Wehrle, V., Hum, R. H., Kendall, D. J., Matsushita, J.,
Murtagh, D. P., Brohede, S., Stegman, J., Witt, G., Barnes, G., Payne, W.
F., Piché, L., Smith, K., Warshaw, G., Deslauniers, D. L., Marchand, P.,
Richardson, E. H., King, R. A., Wevers, I., McCreath, W., Kyrölä,
E., Oikarinen, L., Leppelmeier, G. W., Auvinen, H., Mégie, G.,
Hauchecorne, A., Lefèvre, F., de La Nöe, J., Ricaud, P., Frisk, U.,
Sjoberg, F., von Schéele, F., and Nordh, L.: The OSIRIS instrument on
the Odin spacecraft, Can. J. Phys., 82, 411–422,
10.1139/p04-005, 2004.Lopez-Puertas, M., Koukouli, M. E., Funke, B., Gil-López,
S., Glatthor, N., Grabowski, U., von Clarman, T., and Stiller, G. P.:
Evidence for CH4 7.6 µm non-local thermodynamic equilibrium
emission in the mesosphere, Geophys. Res. Lett., 32, L04805,
10.1029/2004GL021641, 2005.Lowe, R. P.: Interferometric Spectra of the Earth's Airglow (1.2 to 1.6 µm), A Discussion on Infrared Astronomy, Philos. T. R. Soc. S.-A, 264, 163–169, 1969.Marcq, E., Lellouch, E., Encrenaz, T., Widemann, T., Birlan, M., and Bertaux,
J. L.: Search for horizontal and vertical variations of CO in the day and
night side lower mesosphere of Venus from CSHELL/IRTF 4.53 µm
observations, Planet. Space Sci., 113, 10.1016/j.pss.2014.12.013, 2015.
Mlynczak, M. G. and Solomon, S.: A detailed evaluation of the heating
efficiency in the middle atmosphere, J. Geophys. Res., 98, 10517–10541, 1993.Mlynczak, M. G., Marshall, B. T., Martin-Torres, F. J., Russell III, J. M.
Thompson, R. E., Remsberg, E. E., and Gordley, L. L.: Sounding of the
Atmosphere using Broadband Emission Radiometry observations of daytime
mesospheric O2(1Δ) 1.27 µm emission and derivation
of ozone, atomic oxygen, and solar and chemical energy deposition rates, J.
Geophys. Res., 112, D15306, 10.1029/2006JD008355, 2007.
Morstad, D., Doelling, D., Scarino, D., Bhatt, R., and Gopalan, A.:
Characterization of Deep Convective Clouds as absolute Calibration Targets
for Visible Sensors, 2012 CALCON Technical Conference, 27–30 August 2012,
Logan, Utah, USA, 2012.Noxon, J. F.: A global study of O2 1-delta Airglow – Day and twilight, Planet. Space Sci., 30, 545–557, 1982.Noxon, J. F. and Vallance Jones, A. : Observation of the (0, 0) band of the
(1Δg-3Σg) system of oxygen in the day
and twilight airglow, Nature, 213, 350, 10.1038/196157a0, 1962.Noxon, J. F., Traub, W. A., Carleton, N. P., and Connes, P.: Detection of
O2 dayglow emission from Mars and the Martian ozone abundance,
Astrophy. J., 207, 1025–1035, 1976.O'Brien, D. M. and Rayner, P. J.: Global observations of the carbon budget,
2, CO2 column from differential absorption of reflected sunlight in the
1.61 µm band of CO2, J. Geophys. Res., 107, 4354,
10.1029/2001JD000617, 2002.O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E., Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M., Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer, P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.: Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576, 10.5194/amt-11-6539-2018, 2018.Pasternak, F., Bernard, P., Georges, L., and Pascal, V.: The MicroCarb
instrument, ICSO 2016, Proc. SPIE, 10562, 105621P-13, 10.1117/12.2296225, 2016.Pendleton Jr., W. R., Baker, D. J., Reese, R. J., and O'Neil, R. R.: Decay
of O2(a1Δg) in the evening twilight
airglow: Implications for the radiative lifetime, Geophys. Res. Lett., 23, 1013–1016, 1996.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and
Practice, World Scientific, Singapore, 2000.Russell III, J. M., Mlynczak, M. G., Gordley, L. L., Tansock, J., and
Esplin, R.: An overview of the SABER experiment and preliminary calibration
results, SPIE Proceedings, 3756, 10.1117/12.366382, 1999.
Scott, N. A. and Chedin, A.: A fast line-by-line method for atmospheric
absorptions computations: the automatized atmospheric absorption atlas,
J. Appl. Meteorol., 20, 802–812, 1981.
Simeckova, M., Jacquemart, D., Rothman, L. S., Gamache, R. R., and Goldman, A.: Einstein A-coefficients and statistical weights for
molecular absorption transitions in the HITRAN data base, J. Quant. Spectrosc. Ra., 98, 130–155, 2006.Sioris, C. E.: Impact of the dayglow and the Ring effect on the retrieval of
surface pressure from the A and B bands of O2: application to Orbiting
Carbon Observatory, Internal Report, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA, 2003.
Spurr, R.: LIDORT Version 3.6 User's guide, RT Solutions, Inc., Cambridge, MA 02138, USA, 2012.Sun, K., Gordon, I. E., Sioris, C. E., Liu, X., Chance, K., and Wofsy, S.
C.: Reevaluating the use of O2a1Δg band in spaceborne
remote sensing of greenhouse gases, Geophys. Res. Lett., 45,
5779–5787, 10.1029/2018GL077823, 2018.Thomas, R. J., Barth, C. A., Rusch, D. W., and Sanders, R. W.: Solar
Mesosphere Explorer Near-Infrared Spectrometer: Measurements of 1.27 µm
radiances and the inference of mesospheric ozone, J. Geophys. Res.-Atmos.,
89, 9569–9580, 1984.
Wiensz, J. T.: Ozone Retrievals from the Oxygen Infrared Channels of
the OSIRIS Infrared Imager, Master Thesis, University of Saskatchewan, Saskatoon, Saskatchewan, Canada, 2005.Yamamoto, H., Makino, T., Sekiguchi, H., and Naito, I.: Infrared atmospheric
band airglow radiometer on board the satellite OHZORA, J. Geomagn.
Geoelectr., 40, 321–333, 1988.
Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and
Maksyutov, S.: Global Concentrations of CO2 and CH4 Retrieved
from GOSAT: First Preliminary Results, SOLA, 5, 160–163, 2009.Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., Morino, I., and Yokota, T.: Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite, Atmos. Meas. Tech., 4, 717–734, 10.5194/amt-4-717-2011, 2011.Zarboo, A., Bender, S., Burrows, J. P., Orphal, J., and Sinnhuber, M.: Retrieval of O2(1Σ) and O2(1Δ) volume emission rates in the mesosphere and lower thermosphere using SCIAMACHY MLT limb scans, Atmos. Meas. Tech., 11, 473–487, 10.5194/amt-11-473-2018, 2018.