AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-5587-2018Retrievals of tropospheric ozone profiles from the synergism of AIRS and
OMI: methodology and validationRetrievals of tropospheric ozone profiles from the synergism of AIRS and
OMIFuDejiandejian.fu@jpl.nasa.govhttps://orcid.org/0000-0001-5205-0059KulawikSusan S.MiyazakiKazuyukihttps://orcid.org/0000-0002-1466-4655BowmanKevin W.WordenJohn R.ElderingAnnmariehttps://orcid.org/0000-0003-1080-9922LiveseyNathaniel J.TeixeiraJoaoIrionFredrick W.HermanRobert L.https://orcid.org/0000-0001-7063-6424OstermanGregory B.LiuXiongLeveltPieternel F.ThompsonAnne M.https://orcid.org/0000-0002-7829-0920LuoMingNASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, California, USABay Area Environmental Research Institute/NASA Ames Research Center,
Mountain View, California, USAJapan Agency for Marine-Earth Science and Technology, Yokohama, JapanHarvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts,
USARoyal Netherlands Meteorological Institute, De Bilt, 3731 GA, the
NetherlandsFaculty of Civil Engineering and Geosciences, University of Technology
Delft, Delft, 2628 CN, the NetherlandsNASA Goddard Space Flight Center, Greenbelt, Maryland, USADejian Fu (dejian.fu@jpl.nasa.gov)12October201811105587560525April201816May201824August20185September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/5587/2018/amt-11-5587-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/5587/2018/amt-11-5587-2018.pdf
The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite
was designed to profile tropospheric ozone and its precursors, taking
measurements from 2004 to 2018. Starting in 2008, TES global sampling of
tropospheric ozone was gradually reduced in latitude, with global coverage
stopping in 2011. To extend the record of TES, this work presents a
multispectral approach that will provide O3 data products with
vertical resolution and measurement error similar to TES by combining the
single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua
Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV)
channels from the Aura Ozone Monitoring Instrument (OMI). The joint
AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra,
MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of
collocated joint AIRS+OMI and TES to ozonesonde measurements show that both
systems have similar errors, with mean and standard deviation of the
differences well within the estimated measurement error. AIRS+OMI and TES
have slightly different biases (within 5 parts per billion) vs. the sondes.
Both AIRS and OMI have wide swath widths (∼1650 km for AIRS; ∼2600 km for OMI) across satellite ground tracks. Consequently, the joint
AIRS+OMI measurements have the potential to maintain TES vertical
sensitivity while increasing coverage by 2 orders of magnitude, thus
providing an unprecedented new data set with which to quantify the evolution
of tropospheric ozone.
Introduction
Long-term records of the vertical distribution of ozone are essential for
quantifying the impact of changes in tropospheric ozone on air quality and
climate, driven recently by rapid industrialization in Asia concurrent with
reductions in ozone precursor emissions in North America and Europe (Jacob et
al., 1999; Wild and Akimoto, 2001; Akimoto, 2003; Worden et al., 2008, 2011;
Fischer et al., 2011). The A-Train Aura satellite has played an important
role in quantifying the atmospheric ozone and advancing our understanding of
the processes controlling its distribution. The Dutch–Finnish Ozone
Monitoring Instrument (OMI) measures ultraviolet (UV) radiances, which are
used to infer a number of species including ozone profiles and columns
(Levelt et al., 2006a, b, 2018; Liu et al., 2010a, b; Huang et al., 2017).
These measurements have been used in a number of assimilation systems to
constrain both stratospheric and tropospheric ozone distributions (Stajner et
al., 2008; Pierce et al., 2009; Huang et al., 2013; Inness et al., 2013;
Wargan et al., 2015; Olsen et al., 2016). OMI ozone columns have been used to
understand both tropical ozone variability (Chandra et al., 2007; Ziemke et
al., 2007) and high-latitude ozone, including the unprecedented Arctic ozone
loss in 2011 (Manney et al., 2011). The Aura Tropospheric Emission
Spectrometer (TES) has a spectral resolution of 0.1 cm-1, the highest
infrared spectral resolution among any current nadir sounder, which enables
estimation of tropospheric ozone profiles and precursors. TES has advanced a
number of Aura science objectives, including detection of tropospheric ozone
trends over Asia (Lamsal et al., 2011; Verstraeten et al., 2015), the
influences of long-range pollution transport on surface ozone (Parrington et
al., 2008, 2009), and the tropospheric ozone response to stratospheric
circulation (Neu et al., 2014). The TES record has also played an important
role in evaluating chemistry–climate model simulations of present-day ozone
distributions and their ozone radiative forcing as part of the
Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report
(AR5; Bowman et al., 2013; Shindell et al., 2013; Young et al., 2013; IPCC,
2014) and in providing constraints on the tropospheric chemistry through data
assimilation (Miyazaki et al., 2012, 2014, 2015). TES global observations are
limited to a roughly 5-year period (2005–2009) due to instrument aging. TES
global sampling of tropospheric ozone was gradually reduced starting in 2008,
with global observations ceasing altogether in 2011. Consequently, TES's
well-validated global-survey record of tropospheric ozone (H. M. Worden et
al., 2007; Nassar et al., 2008; Boxe et al., 2010; Verstraeten et al., 2013;
Bella et al., 2015) ended in 2011.
The synergy of combining UV and ultra-spectral thermal infrared (TIR)
radiances provides an approach to measuring lower-tropospheric ozone, a key
objective of air quality remote sensing (J. Worden et al., 2007; Landgraf and
Hasekamp, 2007; Costantino et al., 2017). This capability was demonstrated by
Fu et al. (2013) for joint TES+OMI and Cuesta et al. (2013, 2018) for joint
Infrared Atmospheric Sounding Interferometer (IASI) and Global Ozone
Monitoring Experiment 2 (GOME-2). Ozone profiles from joint TES+OMI
retrievals are a part of the standard Earth Observing System (EOS) Aura
products from the time period 2005 to 2008, the time period when neither the
degradation of TES instrument nor the row anomaly of OMI pixels (Huang et
al., 2017; Schenkeveld et al., 2017; Levelt et al., 2018), which provide
measurements collocated to TES measurements, played a role.
In this work, we demonstrate that joint Atmospheric Infrared Sounder (AIRS)
and OMI retrievals can extend the Aura EOS TES standard Level 2 tropospheric
ozone concentration vertical profile products. The retrieved ozone profiles
harnessing the Level 1B radiances from AIRS and OMI measurements have
vertical resolution and error characteristics similar to the TES instrument
on Aura and the prospect of vastly increased spatial coverage.
TES, AIRS, OMI, and ozonesonde measurements
The NASA A-Train satellites (Aqua, Aura, Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation (CALIPSO), CloudSat, Orbiting Carbon
Observatory-2 (OCO-2)) are providing long-term global measurements of the
land surface, biosphere, atmosphere, and oceans of the Earth in a near-polar,
sun-synchronous, ∼700 km altitude orbit whose ascending node has an
Equator-crossing time of around 13:30 local time. The measurements of
three nadir-viewing instruments in the A-Train satellites – including
Aura-TES, Aura-OMI, and Aqua-AIRS – play essential roles in quantifying
atmospheric composition, including O3 and a suite of trace gases, to
advance understanding of air quality and climate science.
TES is a Fourier transform spectrometer (FTS) that measures the double-sided
interferograms of TIR radiances emitted and absorbed by Earth's surface,
gases, and particles in the atmosphere (Beer et al., 2001). Although TES has
both the nadir and limb views, nadir has been the primary scanning geometry
used to obtain full vertical and horizontal coverage of Earth's atmosphere.
In nadir mode, TES measurements cover four optical filter bands (650–900,
950–1150, 1100–1325, and 1900–2250 cm-1) with a constant spectral
resolution of 0.1 cm-1 and a ground pixel size of 5.3×8.5 km2. The 950–1150 cm-1 spectral region includes
high-density absorption features of the ozone υ3 band (the
strongest fundamental band) and minor absorption from interfering species.
The υ3 band has been exploited in the tropospheric O3
soundings by a suite of TIR satellite-borne, nadir-viewing instruments,
including AIRS (Susskind et al., 2003, 2014; Wei et al., 2010), Cross-track
Infrared Sounder (CrIS) (Gambacorta et al., 2013), and IASI (Boynard et al.,
2009, 2016; Dufour et al., 2012; Oetjen et al., 2014, 2016), as well as the
solar occultation satellite-borne (Bernath et al., 2005; Bernath, 2017),
balloon-borne (Toon, 1991; Fu et al., 2007a), and ground-based (Hannigan et
al., 2011) FTSs that quantify the stratospheric ozone layer and the species
playing an essential role in the stratospheric ozone chemistry (Fu et al.,
2007b, 2009, 2011; Sung et al., 2007; Wunch et al., 2007; Allen, 2009; Boone,
2013; Nassar, 2013; Griffin et al., 2017). The spectral resolution of TES
(resolving power (RP): 10 500) is significantly higher than the existing TIR,
including AIRS (RP: 1200), CrIS (RP: 816), and IASI (RP: 5250). Benefiting from
the Aura afternoon orbit, TES takes measurements around local noontime when
the atmosphere–land thermal contrast is typically higher than other times of
the day. Taking the spectral coverage, spectral resolution, and noise
performance into account, the vertical sensitivity of TES and other satellite
sensors (AIRS alone, OMI alone) is quantified in Sect. 3.2. It shows that TES
has the sensitivity to distinguish between the upper- and lower-tropospheric
O3.
AIRS is a grating spectrometer that measures the Earth's TIR emission in the
spectral range of 650–2665 cm-1 (Aumann et al., 2003). It is a
cross-track scanning instrument providing measurements with daily global
coverage. AIRS atmospheric measurements in the ozone υ3 band
provide sensitivity for estimating atmospheric ozone column density. The
currently operational AIRS version 6 retrieval algorithm (Susskind et al.,
2003, 2014) estimates the temperature, humidity, and atmospheric composition
products using the 45 km resolution Level 2 cloud-cleared radiance products
for weather prediction and environmental monitoring. In order to fully
exploit the spatial resolution of AIRS measurements, our joint AIRS+OMI
ozone retrievals use single-footprint (i.e., non-cloud-cleared) Level 1b AIRS
infrared radiances with a spatial resolution of ∼13.5 km nadir
horizontal resolution.
OMI is a nadir-viewing push broom ultraviolet–visible (UV-VIS) imaging
spectrograph that measures backscattered radiances covering the 270–500 nm
wavelength range (Levelt et al., 2006a, b) and captures the absorption
features of the ozone Hartley and Huggins bands that are clearly present in
the 270–310 nm (mainly for stratospheric ozone information) and
310–330 nm (mainly for tropospheric ozone information) spectral regions.
The ground pixel size of OMI measurements at nadir position is about 13 km
(along the ground track of spacecraft) ×24 km (across the track)
when using the spectral radiances 310–330 nm. Since 2009, row anomaly and
stray-light issues have affected the quality of some OMI pixels (Huang et
al., 2017; Schenkeveld et al., 2017; Levelt et al., 2018). Following 2009,
for retrieval, the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES)
algorithm uses the measured radiances from the quality-assured OMI off-nadir
pixels and the corresponding collocated AIRS measurements.
The World Ozone and Ultraviolet radiation Data Centre (WOUDC,
http://www.woudc.org, 4 October 2018) ozonesonde measurements provide
in situ data from the surface to the stratosphere (about 35 km) with
vertical resolution of ∼150 m and accuracy of 5 % (Witte et al.,
2017, 2018; WMO/GAW, 2017). These data fill a critical need for the
validation of ozone profiles measured by spaceborne remote-sensing
instruments (Thompson et al., 2017). The ozonesonde sensor has a dilute
solution of potassium iodide to produce a weak electrical current
proportional to the ozone concentration of the sampled air (Komhyr et al.,
1995). To examine the performances of remote-sensing measurements, we applied
the following coincidence criteria to determine sonde–AIRS+OMI: (1) mean
cloud optical depth < 2.0, (2) cloud fraction within OMI field of
view < 30 %, (3) both satellite ground pixel–sonde distances
< 300 km, (4) solar zenith angle < 80∘, and
(5) daytime measurements with a time difference < 4 h. In order to
determine the sonde–TES pairs, we applied the criteria (1), (3), (4), and
(5), and exclude criterion (2) because the TES retrieval does not use
information from OMI measurements. As a result, for the 2006 time frame, we
obtained 424 sonde–AIRS–OMI triads and 556 sonde–TES measurement pairs.
Retrieval algorithms and retrieval characteristics
The joint AIRS+OMI ozone profile is produced from the MUSES retrieval
algorithm, crafted to accommodate multiple instruments, including joint
TES+OMI O3 retrievals (Fu et al., 2013); joint
CrIS+TROPOMI (TROPOspheric Monitoring Instrument) carbon monoxide (CO)
profiling (Fu et al., 2016); joint TES+Microwave Limb Sounder (MLS) CO
retrievals (Luo et al., 2013); and AIRS CH4, HDO, H2O,
and CO retrievals (Worden et al., 2018; Kulawik et al., 2018). These
atmospheric composition products, with characteristics of vertical resolution
and error similar to TES standard Level 2 data, have the potential to extend
the Aura-TES atmospheric composition Earth science data records (ESDRs),
continuing the climate and air quality science enabled by TES measurements.
The development of the MUSES algorithm leverages a suite of existing
atmospheric composition retrieval algorithms, especially forward radiative
transfer models, including the Earth Limb and Nadir Operational Retrieval
(ELANOR) of the TES operational algorithm (Worden et al., 2004; Clough et
al., 2006; Kulawik et al., 2006a, b; Bowman et al., 2006; Eldering et al.,
2008) for simulation of TIR radiances and Jacobians (Fu et al., 2013, 2016);
the U.S. Smithsonian Astrophysical Observatory (SAO) OMI OZone PROFile
(RROFOZ) algorithm (Liu et al., 2010a, b) for simulation of UV radiances and
Jacobians of Hartley and Huggins bands (Fu et al., 2013; Worden et al.,
2013); and the full-physics OCO-2 algorithm (O'Dell et al., 2012, 2018;
Connor et al., 2016; Crisp et al., 2012, 2017; Eldering et al., 2017) for
simulation of short-wavelength infrared radiances and Jacobians (Fu et al.,
2016).
Joint AIRS+OMI ozone profile retrievals
The retrieval methodology is based on the optimal-estimation (OE) method
(Rodgers, 2000), which minimizes the differences between observed and
measured radiances subject to a priori knowledge, i.e., mean and covariation
of the atmospheric-cloud-surface state, to infer the “optimal” or maximum a
posterior (MAP). Numerically, the MAP state vector x^, which
represents the concentration of atmospheric trace gases and ancillary
parameters, is computed by minimizing the following cost function with
respect to x:
Cx=x-xaSa-12+Lobs-LsimSϵ-12.
Equation (1) is a sum of quadratic functions representing a weighted
Euclidean norm ba2=bTab, with the first term
accounting for the difference between the retrieval vector x and a
priori state xa, inversely weighted by the a priori covariance
matrix Sa, and with the second term representing the
difference between the observed Lobs and simulated
Lsim radiance spectra inversely weighted by the
measurement error covariance matrix Sϵ.
Under the assumption that measurement error between AIRS and OMI is
uncorrelated, Eq. (1) can be written as
Cx=x-xaSa-12+Lobs_AIRS-Lsim_AIRSSϵ_AIRS-12︸AIRS+Lobs_OMI-Lsim_OMISϵ_OMI-12︸OMI.
The joint retrieval algorithm iteratively updates the state vector based upon
a trust-region Levenberg–Marquardt (LM) optimization algorithm (Moré,
1977; Bowman et al., 2006) to minimize the cost function in Eq. (2):
xi+1=xi+γiWTW+Sa-1+KAIRSTSϵ_AIRS-1KAIRS︸AIRS+KOMITSϵ_OMI-1KOMI︸OMI-1×Sa-1xa-xi+KAIRSTSϵ_AIRS-1ΔLAIRS︸AIRS+KOMITSϵ_OMI-1ΔLOMI︸OMI,
where the parameter γi is called the LM parameter, W
is a nonzero scaling matrix, Kinstrument is the
Jacobian matrix representing instrument sensitivity of spectral radiances to
the atmospheric state, and ΔL is the difference between
observed and simulated spectral radiances. The computation of the γi value and W follow Sects. 5.5 and 6.3 of Moré (1977),
utilizing the fitting residuals and K from the space instruments
as input parameters. The γiWTW term,
the core of the trust-region LM optimization algorithm, plays the crucial
role in balancing the convergence speed and robustness. Under large
γi , the step size computation is similar to a steepest-descent
algorithm, which has a lower convergence rate, and under low γi
the step computation is towards a Gauss–Newton approach.
To simulate TIR spectral radiances L and Jacobians K
in TIR and UV spectral regions (Table 1), the joint AIRS+OMI retrieval
adopts the forward models of the joint TES+OMI retrievals (Fu et al., 2013)
with necessary revisions to incorporate the AIRS specifications (spectral
range, signal-to-noise ratios (SNRs), viewing geometry, and spectral response
function) (Pagano et al., 2003; Strow et al., 2003).
a The parameters are included in
the retrievals for different cases (AIRS only, OMI only, and joint
AIRS+OMI). b AIRS single-footprint infrared geolocated and
calibrated radiance data (Aumann et al., 2003) are used directly rather than
Level 2 cloud-cleared spectra, which are calculated using nine adjacent AIRS
infrared footprints. Using single-footprint spectra improves the performance
of horizontal resolution of the AIRS retrieval from ∼45 to ∼13.5 km at nadir, leading to improved representation of horizontal details
(Irion et al., 2018). c Retrievals' normalized radiances (i.e.,
IEarthshine/ Isolar_irradiance) were used in the
retrievals. OMI Level 1B global geolocated earthshine radiance
(IEarthshine) and solar irradiances
(Isolar_irradiance) (Dobber et al., 2006a, b; Van den Oord et
al., 2006).
The joint AIRS+OMI retrievals start with the list of the fitting
parameters, a priori values, and a priori variance shown in Table 2. In
addition to the initial guess for the trace gas concentration (O3,
H2O, and CO2), the initial guess for auxiliary parameters
used in the simulation of AIRS radiances (including temperature profile,
surface temperature and emissivity, and cloud extinction and cloud top
pressure) are also retrieved from AIRS radiances in order to take into
account their spectral signatures in the O3 spectral regions. The
joint AIRS+OMI algorithm incorporated a suite of treatments in order to
optimize the spatial resolution, retrieval stability, data throughput, and
consistency to TES data products (version 6): (1) when the clouds travel
across its field of view, a space sensor for atmospheric composition
measurements often faces the challenge of obtaining high-precision and
high-accuracy measurements of the trace gas vertical distribution due to the
interference among retrieval parameters, and MUSES algorithm uses
single-footprint AIRS Level 1B radiances in the retrievals (Irion et al.,
2018), which leads to a footprint 9 times smaller in area than the AIRS
version 6 operational algorithm (Susskind et al., 2003, 2014), mitigating the
chance of the impacts of cloud interference on the trace gas retrievals;
(2) global infrared land surface emissivity database from the University of
Wisconsin-Madison (UOW-M) (Seemann et al., 2007), which improves clear land
throughput by 4.5 %; (3) an initial-guess refinement step of cloud
fraction prior to the step of joint AIRS+OMI ozone retrievals; (4) a priori
constraint vector and matrix identical to the TES version 6 operational
algorithm to obtain error estimates consistent with TES data products; (5) an
updated a priori and initial-guess information of atmospheric temperature
profiles taken from the near-real-time Goddard Earth Observing System Model,
Version 5 (GEOS-5) (Rienecker et al., 2008) model data for AIRS TIR
temperature profile retrievals; (6) updated a priori ozone built from the
Model for OZone and Related chemical Tracers (MOZART)-4 (Emmons et al., 2010)
as offline climatology; (7) HIgh-resolution TRANsmission (HITRAN) 2012
(Rothman et al., 2013) spectroscopic parameters and a priori information of
water vapor, the primary interfering species in TIR ozone measurements
jointly retrieved with ozone; and (8) labeling the target scenes with a
retrieved cloud fraction less than 30 % within the AIRS+OMI field of
view as quality-assured, in order to minimize the impacts of cloud
interference on ozone data quality. The throughput of AIRS+OMI data
processing over the globe is about 30 %.
List of parameters in state vector.
Case selectionaFitting parametersNumber ofA prioriA prioriparameterserrorAIRS+OMI, AIRS, OMIO3 at each pressure level25MOZART-4bMOZART-3 ∼10–40 %AIRS+OMI, AIRSH2O at each pressure level16GEOS-5cNCEPd∼30 %AIRS+OMI, AIRSSurface temperature1GEOS-50.5 KAIRS+OMI, AIRSSurface emissivitye23UOW-Mf∼0.006AIRS+OMI, AIRSCloud extinctiong11Initial BT difference300 %AIRS+OMI, AIRSCloud top pressureg1500 mbar100 %AIRS+OMI, OMIUV1 surface albedo1OMI climatologyh0.05AIRS+OMI, OMIUV2 surface albedo (zeroth order)i2OMI climatology0.05AIRS+OMI, OMIUV2 surface albedo (first order)i00.01AIRS+OMI, OMIUV1, UV2 ring scaling factors21.91.0AIRS+OMI, OMIUV1, UV2 radiance/irradiance wavelength shifts200.02 nmAIRS+OMI, OMIUV1, UV2 radiance/O3 cross-section wavelength shifts200.02 nmAIRS+OMI, OMICloud fractionj1Derived from 347 nm0.05
a The parameters are included in the retrievals for different cases
(AIRS only, OMI only, and joint AIRS+OMI).
b Model for OZone and Related chemical Tracers (MOZART)-4 (Emmons et
al., 2010).
c Goddard Earth Observing System, Version 5 (GEOS-5) (Rienecker et al.,
2008).
d National Center for Environmental Prediction (NCEP) reanalysis
(Kalnay et al., 1996).
e Retrievals over land; spectral surface emissivity is factored in.
f Global infrared land surface emissivity database at University of
Wisconsin-Madison (UOW-M) (Seemann et al., 2007).
g For cloud treatment in TIR spectral region, we adopt the approach
used in the TES Level 2 full-physics retrieval algorithm (Kulawik et al.,
2006b; Eldering et al., 2008). Gaussian parameters represent the total
optical depth, peak altitude, and profile width.
h The surface reflectance climatology was constructed using 3 years of
OMI measurements obtained between 2004 and 2007 (Kleipool et al., 2008).
i The surface is assumed to be Lambertian with a variable slope in
wavelength to the albedo, such that the albedo can vary linearly across the
spectral band.
j For cloud treatment in UV spectral region, we adopt the approach used
in the TES+OMI retrieval algorithm (Fu et al., 2013) by adding in an
initial-guess refinement step for retrieving the cloud fraction prior to
joint AIRS+OMI ozone retrievals.
Retrieval characteristics of TES, AIRS, OMI, and joint AIRS+OMI
For moderately nonlinear problems, the estimated state can be written as the
linear expression (H. M. Worden et al., 2007)
x^=xa+Axtrue-xa+Gε+δcs,
where xa is the a priori constraint vector; A is
the averaging kernel matrix, whose rows represent the sensitivity of the
retrieval to the true state; xtrue is the true state
vector; ε is the spectral noise of satellite instruments; and
G is the gain matrix, which can be written as
G=KTSϵ-1K+Sa-1-1KTSϵ-1. The
“cross-state” error, δcs=ACSxCS-xCSapriori (H. M. Worden et al.,
2007), is incurred from retrieving xCS, which contains
multiple parameters (e.g., water vapor, surface temperature, cloud extinction
and cloud top pressure in TIR, cloud fraction in UV, surface albedo, and
wavelength-shifting parameters).
The use of OE in the MUSES algorithm also provides the averaging kernel and
error matrices for each sounding needed for trend analysis, climate model
evaluation, and data assimilation. Based on optimal-estimation theory, the
averaging kernel matrix (A) and total error covariance matrix
(S) can be calculated as follows:
A=GK,S=I-ASaI-AT︸smoothing error+GSεGT︸satellite
instrumentmeasurement
error+AcsScsAcsT︸cross-state error︸satellite instrument observation
error,
where I is the identity matrix, Sa is the a
priori covariance matrix of the full retrieved state containing both
atmospheric and auxiliary parameters, and Sε is the
measurement noise covariance of both TIR and UV radiances. The error variance
represented by the diagonal elements in the Sε
matrix is computed from the square of spectral noise values obtained from
Level 1 data products of AIRS and OMI missions, while the off-diagonal
elements are equal to zero. Acs is the submatrix of
the averaging kernel for the full-state vector of all jointly retrieved
parameters that relates the sensitivity of x (the vector of
cross-state parameters) to xCS. The diagonal elements of
Scs contain the a priori covariance for the other
jointly retrieved parameters, including water vapor, surface temperature,
surface emissivity, cloud parameters in infrared (extinction and cloud top
pressure), surface albedo in UV, wavelength shifting in UV, and cloud
parameter in UV (cloud fraction) parameters, while the off-diagonal elements
are equal to zero. It is worth noting that the retrieval scheme does not
include the radiative transfer model error, which is negligible since
(1) both the ELANOR for the TIR and Vector Linearized Discrete Ordinate
Radiative Transfer (VLIDORT) for the UV (Spurr, 2006, 2008) are full-physics
radiative transfer models that have high accuracy and (2) the comparisons of
satellite–ozonesonde presented in Sect. 4.2 show that agreement of the
collocated ozonesonde–satellite measurements is within the expected ranges.
The trace of the averaging kernel matrix (A) gives the number of
independent pieces of information in the vertical profile, or the degrees of
freedom for signal (DOFS) (Rodgers, 2000). A larger DOFS value indicates a
better vertical sensitivity. Figure 1 shows sample averaging kernel matrices
for TES, AIRS, OMI, and joint AIRS+OMI transect observations over the
western United States on 23 August 2006. The joint AIRS+OMI and TES
retrievals show similar capability for resolving the lower/upper troposphere
(tropospheric DOFS: 1.64 for TES, 1.55 for joint AIRS+OMI). Both AIRS and
OMI tropospheric DOFS are ∼1 – capable of estimating the tropospheric
columns but lacking vertical sensitivity in the troposphere.
Averaging kernels of collocated measurements of TES (version 6),
joint AIRS+OMI, AIRS alone, and OMI alone over California, USA, on
23 August 2006. The green, blue, and magenta curves in four panels indicate
the averaging kernels in the pressure range of surface–400 hPa, 400–100 hPa, and above 100 hPa, respectively.
Validation of joint AIRS+OMI data
An initial comparison between TES, AIRS, OMI, and AIRS+OMI is shown by a
transect from ∼6∘ N to 55∘ N taken on 23 August 2006
(Fig. 2a) and processed through the MUSES algorithm. The tropospheric ozone
concentration profiles of joint AIRS+OMI retrievals show better agreement
with TES data (Fig. 2g, green curve; mean differences < 2 % from
surface to 400 hPa, and < 5 % from 400 to 100 hPa) than the
retrievals for both AIRS and OMI alone (Fig. 2g, blue curve for AIRS, purple
curve for OMI). The joint retrievals improve the agreement due to the
increased vertical sensitivity in comparison to each instrument alone since the
multispectral retrievals have the advantage of obtaining the vertical
distribution information of atmospheric composition from multiple physical
regimes, including the atmospheric thermal emissions, pressure- and
temperature-dependent spectral line broadenings and absorption cross
sections via both TIR and UV radiances, and
wavelength- and altitude-dependent atmospheric scattering events via UV radiances.
Further evaluation of the joint AIRS+OMI O3 retrievals is shown
in two modes: global survey (GS) and regional mapping (RE). The GS mode
provides profile data at nadir position along the satellite ground track,
i.e., a spatiotemporal sampling identical to TES GS, while RE mode processes
all available AIRS+OMI measurements over a region; specifically in this
case we have considered the Korean Peninsula during the 2016 Korea–United
States Air Quality (KORUS-AQ) campaign (Miyazaki et al., 2018). The global
joint AIRS+OMI retrievals have been compared to the well-validated TES data
(Sect. 4.1) and high accuracy in situ global ozonesonde measurements
(Sect. 4.2) to quantify the performance of this multispectral tropospheric
ozone profile data product. These comparisons were made using measurements in
2006, when neither the TES instrument degradation nor OMI row anomaly played
a role.
Collocated ozone (O3) measurements from A-Train
nadir-viewing spectrometers over the western United States on 23 August 2006.
(A) Geolocation of 110 TES–AIRS–OMI triads (spatiotemporal
differences ∼8 km, ∼16 min); (B) vertical profile of
TES O3 volume mixing ratio (VMR) data (version 6) in units of parts
per billion (ppb); (C) joint AIRS+OMI retrievals;
(D) AIRS alone; (E) OMI alone; (F) a priori used
in retrievals; and (G) averaged percentage differences of retrieved
O3 profiles in comparison to TES O3 data (version 6): TES
vs. joint AIRS+OMI (green dash-dotted line), TES vs. AIRS alone (blue), and
TES vs. OMI alone (purple dashed line). The white curves in the panels of
(B–F) indicate the tropopause pressure taken from the Goddard Earth
Observing System Model, Version 5.
Global maps of monthly averaged ozone (O3) volume mixing
ratio (VMR) in units of ppb. The A-Train measurements in August 2006 were
used in creating these global maps. Comparison of joint AIRS+OMI
(A), TES (B), and a priori (C) ozone VMR for the
pressure level of 316, 510, and 750 hPa (columns left, middle, right),
respectively. All data have been gridded to 2.5∘×2.5∘ cells. Results for the remaining months of 2006 are available
in Figs. S1–S11 in the Supplement.
Comparison to the TES data
Joint AIRS+OMI ozone retrievals apply only to daytime scenes, since OMI
measurements depend on the sunlight, though the MUSES algorithm processes
both daytime and nighttime TIR space measurements. The “species retrieval
quality” flag of joint AIRS+OMI data, a master quality flag available
in the Level 2 product files, was determined by evaluating a suite of
retrieval characteristics including the spectral fitting residuals, cloud
fraction within field of view (when effective cloud fraction in OMI
> 30 %), and the lapse rate of tropospheric ozone vertical
distribution. The retrieval scheme processes the AIRS+OMI measurements over
all sky conditions, though only the scenes of the cloud fraction within field
of view less than 30 % were flagged as quality-assured. The retrieval
acceptance rate of joint AIRS+OMI ozone in 2006 is about 30 %.
Both TES and joint AIRS+OMI 2006 ozone profile data were screened prior to
the comparison using (1) the species retrieval quality, (2) the retrieved
cloud effective TIR optical depth (removed when OD > 2.0), and
(3) solar zenith angle (SZA; excluded when SZA > 80∘,
i.e., daytime only). We excluded profiles with thick clouds in the field of
view because these obscure the infrared emission from the lower troposphere,
which greatly reduces the satellite sensitivity of both TIR and UV radiances.
For cloud treatment, we adopt the approach used in the joint TES+OMI
retrieval algorithm (Fu et al., 2013) by adding in an initial-guess
refinement step for retrieving the cloud fraction within OMI field of view,
prior to joint AIRS+OMI ozone retrievals. The impacts of cloud and surface
properties have been into taken account in the retrievals, since the MUSES
algorithm simultaneously retrieves both the trace gases profiles and the
cloud/surface parameters. The retrieved values and estimated errors of the
TIR cloud effective optical depth and cloud height, UV cloud fraction within
the field of view, and cloud top height are provided in the joint AIRS+OMI
data product files.
Joint AIRS+OMI global tropospheric O3 retrievals (Fig. 3A1–3,
August 2006 monthly mean data) show good agreement with TES data, as shown in
Fig. 3B1–3. Both data sets are significantly different from the a priori and
capture the synoptic ozone patterns such as the midlatitude Atlantic and the
biomass burning events (e.g., southern Africa). Results for the remaining
months of 2006 are available in Figs. S1–S11 in the Supplement.
The correlation coefficients of joint AIRS+OMI and TES version 6 data
(Table 3) are greater than 0.71 and up to 0.92 for all months across the
troposphere, where the mean and root mean square (rms) of the differences of
two data sets (Table 3) are well within the estimated total error. The period
of September–November coincides with the slight drop of the Pearson
correlation coefficient values. For September 2006 data, the different
spatiotemporal sampling between TES and joint AIRS+OMI data is the reason
for the slight drop. In September 2006, TES and joint AIRS+OMI data
deliver nine and 15 global surveys, respectively (bottom row of Table 3).
TES did not deliver measurements from 1 to 9 September. In support of the
TEXAQS II flight campaign, TES delivered additional special observations by
reducing the number of global surveys in the end of September. For October
and November 2006 data, the slight drop of the correlation coefficients might
relate to the slight difference of measurement sensitivity between TES and
joint AIRS+OMI, as shown in Figs. S20 and S21 in the Supplement.
The characteristics of the joint AIRS+OMI retrievals, in terms of vertical
sensitivity and estimated error characteristics, are similar to those of TES
data. The DOFS, which quantify the vertical sensitivity of global
tropospheric ozone retrievals, show distributions similar to TES data (Fig. 4
panels A2 and B2 for August 2006). Figures S12–S22 in the Supplement present the
DOFS for the remaining months of 2006. Both the estimated observation and
total errors of joint AIRS+OMI retrievals (black curves of Fig. 5) show
peaks and widths equivalent to those of TES data products (green curves of
Fig. 5) across troposphere over the globe. Figures S23–S33 in the Supplement
present the estimated errors for the remaining months of 2006. The peak of
the estimated observation errors, which are the sum of second and third terms
in Eq. (6), resides in the range of 6 %–8 % (or ∼3 ppb) for
the joint AIRS+OMI retrievals – equivalent to the observation error of
5 %–7 % (or ∼2–3 ppb) from TES data across the troposphere.
Finally, the joint AIRS+OMI retrievals have total errors within 3 %
agreement over the globe – equivalent to TES data.
Comparisons between joint AIRS+OMI and TES gridded (2.5∘×2.5∘) global survey measurements of ozone
concentration at three pressure levels (316, 510, and 750 hPa) for
the year 2006.
DOFS for O3 over globe shown in Fig. 3. Here, we used the
A-Train measurements from August 2006. Results for the remaining months of
2006 are available in Figs. S12–S22 in the Supplement. (A1) Total DOFS;
(A2) tropospheric DOFS; (B1) histogram of total DOFS: joint
AIRS+OMI (black line) and TES version 6 (green dashed line); and
(B2) histogram of tropospheric DOFS joint AIRS+OMI (black line)
and TES version 6 (green dashed line).
Estimated (predicted) error of retrieved global O3
concentration shown in Fig. 3. Here, we used the A-Train measurements from
August 2006. Results for the remaining months of 2006 are available in
Figs. S23–S33 in the Supplement. (A1–A3) Observational error;
(B1–B3) total error; (C1–C3) observational error in ppb;
and (D1–D3) total error in ppb. Joint AIRS+OMI data are shown as
a black line, and TES version 6 data are shown as a green dashed line.
Comparison to ozonesonde measurements
We identified 424 sonde–AIRS–OMI triads and 556 sonde–TES pairs following
the coincidence criteria in Sect. 2. Following H. M. Worden et al. (2007),
satellite observation operators Hxa,A defined in the equation for joint
AIRS+OMI and TES were applied to the in situ ozonesonde profiles accounting
for known bias and precision. As a result, the expected covariance matrix of
the differences between the satellite retrievals and ozonesonde measurements
smoothed by instrument averaging kernels can be written similarly to
Eq. (6) (H. M. Worden et al., 2007; Fu et al., 2013):
Ex^-x^sondex^-x^sondeT=ASsondeAT︸ozonesonemeasurement error+GSεGT︸satellite instrumentmeasurement error+AcsScsAcsT︸cross-state error︸satellite instrument observation error+GSϵrGT︸remaining radiancecalibration error+SSS︸sonde–satellitetemporal spatial
sampling.
Equation (7) indicates that the error covariance matrix is not biased by the
a priori xa, and the biases of O3 retrievals relative
to ozonesondes are due to the errors of the sonde measurements
Ss, the random spectral noise
Sε, the interfering parameters in retrieval
state vector Scs, the remaining radiometric
calibration errors Sϵr, or sonde–satellite
spatiotemporal samplings SSS.
Figure 6 and Table 4 illustrate that both joint AIRS+OMI and TES data are
in good agreement with ozonesonde measurements across seasonal variations in
the troposphere. Here, the biases of ozone from remote-sensing measurements
are within 3, 2, and 5 ppb for joint AIRS+OMI at three pressure levels
(316, 510, and 750 hPa, respectively) and within 6, 4, and 3 ppb,
respectively, for TES version 6 data. The biases of these satellite data show
an improvement for all seasons when compared to a high bias of 3–10 ppb
estimated for the TES tropospheric ozone data prior to version 6 via
validation using ozonesonde measurements (Nassar et al., 2008; Boxe et al.,
2010). Additionally, the rms's of the differences are 10–17, 8–11, and 7–9 ppb
for the tropospheric ozone of joint AIRS+OMI retrievals and 12–22,
8–15, and 7–13 ppb for TES version 6 data, consistent with those reported by
the existing TES validations. Overall comparisons of AIRS+OMI to
ozonesondes (with observation operator applied to account for sensitivity)
yield similar biases and errors to matching comparisons between TES and
sondes. Note that Fig. 6 and Table 4 report that single band retrievals
(AIRS-alone and OMI-alone data) have larger bias in comparison to the joint
AIRS+OMI data. Table 5 shows comparisons to the original ozonesonde
measurements (i.e., without satellite observation operator applied). These
direct comparisons are often used for comparing instruments of differing
sensitivities, because more sensitive instruments are expected to show better
agreement to the ozonesondes. The joint AIRS+OMI performs best, as seen in the
reduction of measurement bias at three pressure levels and improved rms at
the 750 hPa level.
Joint AIRS+OMI–sonde (A1–A4), TES–sonde
(B1–B4), AIRS–sonde (C1–C4), and OMI–sonde
(D1–D4) percentage differences of measured ozone concentration for
the four seasons (months abbreviated in parentheses) on a global scale. Individual
profiles are shown in black, and the mean and 1σ standard deviation
range are overlaid in solid magenta (mean) and as dashed magenta lines. The
profiles were plotted after removing cloudy scenes and flagged satellite
(joint AIRS+OMI and TES) data. (A1–A4) Joint AIRS+OMI vs.
ozonesonde; (B1–B4) TES data (version 6) vs. ozonesonde;
(C1–C4) WOUDC sonde location that have coincident measurements with
joint AIRS+OMI (green plus signs) and TES (purple diamonds).
Comparisons between satellite remote-sensing and ozonesonde
in situ measurements for 2006 at three pressure levels (316, 510,
and 750 hPa), with satellite observation operators applied to the ozone
measurements in order to account for sensitivity.
Comparisons between satellite remote-sensing and ozonesonde
in situ measurements for 2006 at three pressure levels (316, 510,
and 750 hPa), without the satellite observation operators applied to the
ozonesonde measurements.
Mean rms ppb % ppb % 316510750316510750316510750316510750(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)(hPa)AIRS+OMI-2.8-0.50.3-7.0-1.01.120.68.95.829.916.212.4AIRS-4.90.31.5-8.40.63.514.07.96.220.715.114.0OMI-5.2-1.00.4-9.3-2.8-0.117.515.211.224.227.322.8TES1.81.70.91.73.24.427.515.416.236.527.937.5
Joint CrIS+OMPS ozone profile retrievals over Africa on
21 October 2013. The elevated ozone concentrations between 2 and
20∘ S are associated with biomass burning. (A) The
retrieved ozone concentration profiles along the transect measurements. The
white curve indicates the tropopause pressure reported by GEOS-5.
(B) TES monthly mean ozone concentration at 510 hPa. The black line
indicates the joint CrIS+OMPS measurement location. (C) The
averaging kernels of joint CrIS+OMPS measurements.
Conclusions
We have shown multispectral retrievals using both AIRS TIR and OMI UV
measured radiances for tropospheric O3 profiling. This technique
enables the continuation of the TES capability to distinguish between upper-
and lower-tropospheric ozone abundances. The global-scale comparisons between
joint AIRS+OMI (version 1) and TES (version 6) O3 profile
products across four seasons in the troposphere on a global scale show that
these two data products are comparable for a wide variety of geophysical
conditions: correlation coefficients are 0.7–0.9 at three pressure levels
(316, 510, and 750 hPa), and both the mean (0.8–4.2 ppb) and rms
differences (±4.8–23 ppb) are within the estimated total errors. The
joint TIR+UV retrieval provides equivalent vertical sensitivity and error
characteristics of high-spectral-resolution TES measurements, which have a spectral
resolution that is ∼8–12 times higher than AIRS and OMI measurements,
though about 3-times-lower SNR. Comparisons of collocated joint
AIRS+OMI, TES, and ozonesonde measurements show that both mean and standard
deviation of the differences are within the estimated measurement error of
these space sensors. The joint AIRS+OMI ozone products have a high bias of
2–5 ppb similar to TES data (3–6 ppb). Consequently, the similarities of
the retrieved concentration, vertical sensitivity, and error characteristics
between joint AIRS+OMI and TES ozone data demonstrate that combining the
measurements of the existing TIR and UV hyperspectral imaging spectrometers
can extend the well-validated NASA EOS high-spectral-resolution TES
tropospheric ozone profile data products.
Both AIRS and OMI have wide swath widths (AIRS: 1650 km; OMI: 2600 km)
across satellites' ground tracks; consequently, the joint AIRS+OMI
measurements promise to extend and even improve the number of available
observations by over 100 times that of TES. The product files of the joint
AIRS+OMI 2006 ozone global survey retrievals, including a validation report
and a reader program, are available via the Aura Validation Data Center
(AVDC) website
(https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/TES/AIRS_OMI-version0.1Beta/,
last access: 4 October 2018). The GS and RE modes of joint AIRS+OMI data
from March to June 2016 in support of KORUS-AQ are also available on the same
website. These results were also applied in the post-flight data analysis by
Miyazaki et al. (2018) that showed great error reductions in the tropospheric
ozone analysis, especially in the middle troposphere, through assimilation of
joint AIRS+OMI data. Overall comparisons of AIRS+OMI to ozonesondes and
aircraft for the year 2016 yield similar biases and errors to matching
comparisons for the year 2006. Using the MUSES algorithm, the AIRS+OMI
global survey mode data (2004 to present) with a footprint size of about 15
by 24 km is being processed using the facilities within the JPL TES Science
Investigator-led Processing (SIP) system to build up a decadal record of
tropospheric ozone products.
The current spatial coverage of AIRS+OMI is sufficient to extend the TES
ozone record beyond 2010, when TES ceased the global survey mode measurements.
The combined AIRS+OMI product can provide a record of tropospheric and
total ozone spanning the full Aura satellite time periods (2005–current).
However, the daily global coverage of OMI measurements has been decreasing
since 2009 due to the OMI row anomaly (Schenkeveld et al., 2017; Huang et
al., 2017; Levelt et al., 2018). Looking to the future and as a way to
further increase science return, we have investigated the feasibility of
constructing an additional multiple-decade-long tropospheric ozone profile
data set using a MUSES-based multispectral approach that combines the
radiance measured by the CrIS and Ozone Mapping Profiler Suite (OMPS)
instruments. This additional data set has the potential to fill the spatial
gaps in the joint AIRS+OMI data record since 2012. Both the CrIS and OMPS
instruments are on the Suomi National Polar-orbiting Partnership (NPP)
satellite, which launched in 28 October 2011. The spectral characteristics of
the CrIS instrument (Han et al., 2013; Strow et al., 2013) are similar to the
AIRS instrument, and those for OMPS (Flynn et al., 2006, 2014; Kramarova et
al., 2014; Pan et al., 2017) are similar to the OMI instrument. Hence, as
expected, joint CrIS+OMPS retrievals present characteristics (Fig. 7)
similar to the joint AIRS+OMI retrievals (Fu et al., 2017).
It is worth noting that the second set of CrIS and OMPS instruments on board
the Joint Polar Satellite System-1 (JPSS-1, also known as NOAA-20) satellite
were successfully launched to space on 18 November 2017. The JPSS-2 (also
known as NOAA-21) satellite, which is the platform of the third set of CrIS
and OMPS instruments, is scheduled to launch in 2022. The NOAA-20/JPSS-1 OMPS
Nadir Mapper products' resolution has improved from 50×50 km2
field of view to 12×17 km2 (JPSS-1) and will further improve
to 10×10 km2 (JPSS-2) in the operational NOAA processing
(Lawrence E. Flynn, personal communication, 2018). The NASA Goddard Space
Flight Center (GSFC) Level 1 products of the JPSS-1 OMPS Nadir Mapper will
have a spatial resolution of 10×10 km2 to help detect sources
of sulfur dioxide, including volcanoes and coal-burning power plants (press
release via https://spacenews.com/ by Glen Jaross, last access:
4 October 2018). As a result, the joint CrIS+OMPS retrievals, with
characteristics similar to AIRS+OMI retrievals but with improved spatial
coverage, illustrate the potentials of extending the tropospheric ozone
profile data record to the next decades using the measurements from the
Suomi-NPP, JPSS-1, and JPSS-2 satellites. The TROPOMI instrument (Veefkind et
al., 2012) on board the sentinel-5 Precursor (S5P) satellite was successfully
deployed into its orbit on 13 October 2017 and formed a new satellite
constellation with Suomi-NPP, currently 5 min apart and with the plan of
reducing to 3 min time difference in the future. The spatial resolution of
TROPOMI is an unprecedented 3.5×7.0 and 7.0×7.0 km2
in the UV-VIS and shortwave IR (SWIR) spectral bands, respectively, providing
another opportunity to obtain the high-resolution tropospheric ozone ESDR via
the multispectral retrieval technique, which combines CrIS and TROPOMI
measurements.
The joint AIRS+OMI ozone data and WOUDC sonde data used in
the data analysis can be freely downloaded from the websites of AVDC
(https://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/TES/AIRS_OMI-version0.1Beta/
last access: 4 October 2018) and WOUDC (http://www.woudc.org, last
access: 4 October 2018) accordingly.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-5587-2018-supplement.
DF and SK developed the joint AIRS+OMI retrieval
algorithm; KM, KB, JW, AE, and NL helped in the estimation of joint AIRS+OMI
measurement uncertainty; KM, RH, GO, AT, and ML helped in the validation and
quality flagging of joint AIRS+OMI data products; XL and PL shared
knowledge of the OMI Level 1B data and helped in the UV radiative transfer
modeling; and JT and FI shared knowledge of the AIRS Level 1B data. All
authors participated in writing the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors thank Barry L. Lefer, Brendan M. Fisher, Bradley R. Pierce, Brian
Drouin, Bryan N. Duncan, Chris D. Barnet, David Crisp, Eric Fetzer, Evan
Fishbein, Gordon J. Labow, Helen M. Worden, Irina V. Strickland, Jassim A.
Al-saadi, James H. Crawford, James F. Gleason, Glen Jaross, Jessica L. Neu,
Joao Teixeira, Joanna Joiner, Karen Cady-Pereira, Kelly Chance, Krzysztof
Wargan, Kuai Le, Lawrence E. Flynn, Larrabee L. Strow, Louisa Emmons, Michael
R. Gunson, Monika Kopacz, Nickolay A. Krotkov, Pepijn Veefkind, Pawan K.
Bhartia, Richard R. Lay, Richard S. Eckman, Robert J. D. Spurr, Seftor Colin,
Scott E. Gluck, Thomas Pagano, Stanley P. Sander, Vivienne H. Payne, and
Shanshan Yu for many helpful discussions. We are grateful to all members of
the TES, AIRS, CrIS, OMI, and OMPS instrument, algorithm, validation, and
science teams for their work on supporting the TES, AIRS, CrIS, OMI, and OMPS
missions. We thank Erin Wong and Eugene Y. Chu at JPL for their help on joint
AIRS+OMI data production and releasing ozone data files to the NASA AVDC
website. We thank Pranjit Saha and Vance R. Haemmerle for their help on the
comparisons with the WOUDC ozone data. Support from the NASA ROSES-2013
Atmospheric Composition: Aura Science Team program (grant number: NNN13D455T)
is gratefully acknowledged. Part of the research was carried out at the Jet
Propulsion Laboratory, California Institute of Technology, under a contract
with the National Aeronautics and Space Administration. Kazuyuki Miyazaki
acknowledges support from JSPS KAKENHI grant numbers 15K05296, 26220101,
26287117, and 18H01285. We thank the editor, Mark Weber, for his excellent
work. Edited by: Mark Weber
Reviewed by: two anonymous referees
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