Remote sensing of greenhouse gases (GHGs) in cities,
where high GHG emissions are typically associated with heavy aerosol
loading, is challenging due to retrieval uncertainties caused by the imperfect
characterization of scattering by aerosols. We investigate this problem by
developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and
CH4) by accounting for aerosol scattering effects in polluted urban
atmospheres. In particular, the algorithm includes coarse- (including sea
salt and dust) and fine- (including organic carbon, black carbon, and
sulfate) mode aerosols in the radiative transfer model. The performance of
GFIT3 is assessed using high-spectral-resolution observations over the Los
Angeles (LA) megacity made by the California Laboratory for Atmospheric
Remote Sensing Fourier transform spectrometer (CLARS-FTS). CLARS-FTS is
located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA
Basin, and it makes observations of reflected sunlight in the near-infrared
spectral range. The first set of evaluations are performed by conducting
retrieval experiments using synthetic spectra. We find that errors in the
retrievals of column-averaged dry air mole fractions of CO2 (XCO2)
and CH4 (XCH4) due to uncertainties in the aerosol optical
properties and atmospheric a priori profiles are less than 1 % on average. This
indicates that atmospheric scattering does not induce a large bias in the
retrievals when the aerosols are properly characterized. The methodology is
then further evaluated by comparing GHG retrievals using GFIT3 with those
obtained from the CLARS-GFIT algorithm (used for currently operational CLARS
retrievals) that does not account for aerosol scattering. We find a
significant correlation between retrieval bias and aerosol optical depth
(AOD). A comparison of GFIT3 AOD retrievals with collocated ground-based
observations from AErosol RObotic NETwork (AERONET) shows that the developed algorithm produces very
accurate results, with biases in AOD estimates of about 0.02. Finally, we
assess the uncertainty in the widely used tracer–tracer ratio method to
obtain CH4 emissions based on CO2 emissions and find that using
the CH4/CO2 ratio effectively cancels out biases due to aerosol
scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations
improves our understanding of the impact of aerosol scattering on the remote
sensing of GHGs in polluted urban atmospheric environments. GHG retrievals
from CLARS-FTS are potentially complementary to existing ground-based and
spaceborne observations to monitor anthropogenic GHG fluxes in megacities.
Introduction
Remote sensing of greenhouse gases (GHGs) in cities provides abundant
datasets for quantifying urban carbon sources and sinks, complementary to
in situ ground-based measurements. However, large source regions such as megacities
are also typically associated with heavy aerosol loading. Atmospheric
aerosols modify the path of solar radiation and thereby introduce
uncertainties in the retrieval of GHGs from reflected and scattered sunlight
measurements. It has been suggested that the imperfect characterization of
aerosol optical and microphysical properties is a significant source of
error for GHG retrievals (Butz et al., 2009; O'Dell et al., 2011).
Many different full physics retrieval algorithms, which explicitly account
for atmospheric absorption and scattering and surface reflection in the
radiative transfer (RT) forward modeling, have been developed for
spaceborne instruments for retrieving column-averaged dry air mole fractions
of atmospheric carbon dioxide (XCO2) and methane (XCH4). Examples
of these instruments include Orbiting Carbon Observatory-2 (OCO-2;
Boesch et al., 2011; O'Dell et al., 2018; Reuter et al., 2017), the
Greenhouse gases Observing SATellite (GOSAT; Bril et al., 2012; Butz
et al., 2011; Yoshida et al., 2013), and TanSat (Wang et al., 2020; Yang et al., 2020). Full physics algorithms for retrieving GHGs
explicitly fit aerosol optical and microphysical properties in order to
minimize biases induced by scattering. Although the GHG retrievals show good
agreement with ground-based Total Carbon Column Observing Network (TCCON)
results, the retrieved aerosol optical depth (AOD) values have large
differences compared with collocated AErosol RObotic NETwork (AERONET)
measurements (Nelson et al., 2016) probably due to limited
information content for aerosols and interference from other factors. To
minimize data uncertainty, many operational GHG retrieval algorithms filter
out retrievals with optically thick aerosols. Observations from these
spaceborne instruments are made in side scattering directions (scattering
angles between ∼90 and 150∘), where aerosol scattering
effects are greatly reduced compared to the forward scattering direction.
However, for an observatory targeting urban GHGs from other vantage points,
such as the California Laboratory for Atmospheric Remote Sensing Fourier
transform spectrometer (CLARS-FTS) (Fu et al., 2014), which is
located on the top of Mt. Wilson overlooking the Los Angeles (LA) Basin, the
measurements are made in both backscattering and forward scattering
directions (Zeng et al., 2020a). There has been very little prior
work investigating impacts of aerosol scattering on GHG retrievals for a
wide range of viewing geometries.
The main objective of this study is to demonstrate the performance of a full
physics algorithm (hereafter referred to as GFIT3), which is an
extension of the widely used GFIT model, to retrieve GHGs in polluted urban
atmospheres from spectra of reflected solar radiation. GFIT is a
state-of-the-art profile scaling algorithm to retrieve gas concentrations
and related atmospheric and instrumental parameters from absorption spectra.
It has been the primary retrieval algorithm for the TCCON network
(Wunch et al., 2011), which has been the benchmark for validating
satellite-based trace gas observations. GFIT has also been used to analyze
spectra from the MkIV balloon interferometer (e.g., Sen et al.,
1996) and ATMOS (e.g., Irion et al., 2002) and is also a critical
component of the currently operational CLARS-GFIT retrieval algorithm
(Fu et al., 2014). GFIT2 (Connor et al., 2016; Roche et
al., 2021) is an upgraded version of GFIT that enables the retrieval of vertical
profiles of trace gases. While GFIT scales profiles based on optimal
estimation, GFIT2 uses Bayesian optimal estimation theory (Rodgers,
2000) as the inverse method to retrieve GHGs at different altitudes.
However, both GFIT and GFIT2 do not account for scattering from molecules
and particulates in the atmosphere. Such contributions are negligible in the
near-infrared domain for instruments that measure directly transmitted solar
spectra, such as TCCON. However, for GHG retrievals based on reflected solar
radiation measurements (e.g., CLARS-FTS, GOSAT, and OCO-2), the aerosol
scattering effect is important and needs to be accurately modeled. GFIT3 is
designed specifically for the purpose of retrieving GHGs in polluted
atmospheres from reflected solar radiation observations. It includes an
aerosol model and a fast RT model to simulate the aerosol scattering
contributions. The technical details can be found in Sect. 3.
In this study, we specifically focus on measuring GHGs in the LA megacity,
which is one of the most polluted cities and the second largest contributor
to carbon emissions in the US, using observations from CLARS-FTS. We
investigate the impacts of aerosol scattering on GHG retrievals using GFIT3
to jointly retrieve GHGs (CO2 and CH4) and AODs of coarse- and
fine-mode aerosols. CLARS-FTS observes reflected sunlight in the
near-infrared spectral range. CLARS-FTS observations provide a unique
dataset to study the impact of aerosol scattering effect because of (1) the
large viewing zenith angle (>80∘) and larger range of
scattering angles compared to spaceborne instruments (Zeng et al., 2020a) and (2) the longer light path in the planetary boundary layer (PBL)
that is a consequence of the large viewing zenith angle. While a longer
light path increases the sensitivity of the measurements to anthropogenic
emissions from LA, it also makes the measurements susceptible to light path
change due to aerosol particles in the PBL (Zhang et al., 2017). As
a result, any effects from aerosol scattering on GHG remote sensing become
amplified for CLARS-FTS due to its observation geometry. It is therefore
very important to have proper aerosol models with accurate optical
properties, including phase function and single scattering albedo (SSA), in order
to obtain accurate GHG retrievals.
Another scientifically unique feature of CLARS-FTS, among instruments that
measure surface-reflected sunlight, is that it uses the O21Δ band at 1.27 µm instead of the O2 A band at 0.76 µm that is traditionally used by spaceborne instruments to constrain
surface pressure and aerosols. Since the 1Δ band is closer to
the CO2 and CH4 absorption bands around 1.6 µm, scattering
effects in the GHG absorption bands are likely to be better constrained.
Also, the spectroscopy of the 1Δ band is more accurately known
than that of the A band. The 1Δ band was not selected by
current spaceborne instruments because of contamination from airglow emitted
by oxygen molecules in the upper atmosphere. Recently, Bertaux et
al. (2020) showed that the airglow contribution can be distinguished and
separated from the O2 absorption signal. Usage of the 1Δ
band will be tested in the upcoming MicroCarb mission (Bertaux et
al., 2020), the first such attempt for a spaceborne instrument. Since
CLARS-FTS looks downwards toward the basin, the measured spectra are not
affected by airglow, which originates in the upper atmosphere. Outcomes from
this study will shed light on the merits of using the 1Δ band
for GHG remote sensing.
The paper is organized as follows. The CLARS-FTS instrument is introduced
in Sect. 2. The GFIT3 retrieval algorithm is described in
Sect. 3. In Sect. 4, we demonstrate retrieval
experiments using synthetic spectra to evaluate GFIT3. Retrieval results for
CLARS observations are presented in Sect. 5, followed by
discussions and conclusions in Sects. 6 and 7,
respectively.
California Laboratory for Atmospheric Remote Sensing (CLARS)CLARS-FTS
CLARS-FTS was designed and built at the Jet Propulsion Laboratory. It is
optimized for reflected sunlight measurements with high spectral resolution
in the near-infrared region (4000–15 000 cm-1). CLARS-FTS uses a
pointing system to target a set of predefined surface reflection targets
(Fig. 1) in the LA Basin, as well as a local diffuse reflector
(Spectralon) for measurements of the free tropospheric background
(Zeng et al., 2020b). In the Los Angeles Basin Survey (LABS)
operating mode, the pointing system stares at each surface reflection target
in the LA Basin and records atmospheric absorption spectra using reflected
sunlight as the light source. In the absence of aerosols, as shown in
Fig. 1a, sunlight travels through the PBL twice with a defined
path: once on the way to the surface target and a second time from the
surface target to CLARS-FTS. The resulting light path through the PBL is
greater than 5 km (see Table 1 in Wong et al., 2015), which is
several times longer than other commonly used viewing geometries, e.g.,
observing the direct solar beam from the surface, or measurement of
surface-reflected sunlight from aircraft and spacecraft vantage points. In
the presence of aerosols, the light path changes mainly due to aerosol
scattering along the path from the basin to the mountain top. Examples of
single and multiple scattering are demonstrated in Fig. 1a. CLARS covers
the whole basin every 1.5 to 2 h. Depending on the season, the total
number of observations within a single day ranges from 160 to 260, and the
number of repeated scans of the whole basin is between five to eight times
over the same timeframe. Additional details can be found in Fu et
al. (2014). Figure 1b shows examples of the observed radiance in
the O2 absorption band centered at 7885 cm-1, the weak CO2
absorption band (hereafter referred to as WCO2) at 6220 cm-1, the
CH4 absorption band at 6076 cm-1, and the strong CO2
absorption band (hereafter referred to as SCO2) at 4852 cm-1. The
absolute radiance, which is needed to constrain the aerosol scattering and
the surface reflectance, is derived by calibrating the raw spectral data of
digital numbers. The calibration factor is derived by comparing the
CLARS-FTS spectra with that of a collocated ASD spectroradiometer. The
signal-to-noise ratio (SNR) for the WCO2 and CH4 bands is about
300±80; for the O2 and SCO2 bands, the SNR is about
100–150 depending on the surface target.
Compared to low earth orbiting satellites such as OCO-2/3, observations from
CLARS-FTS have a larger range of aerosol scattering angles mainly due to
the diurnal and seasonal change in incident solar geometry (Zeng et
al., 2020c). Figure 2 shows the diurnal change in aerosol scattering
angle for six selected surface reflection points. In the morning, the
surface reflection points to the west (West Pasadena and Santa Monica) have
large scattering angles that gradually change to smaller scattering angles
in the late afternoon. The opposite pattern of change can be observed at
reflection points to the east (Santa Fe dam and Rancho Cucamonga). At
reflection points to the south (Santa Anita and Long Beach), the changes are
smaller than at other targets. These changes are a result of the fixed
viewing geometry for each surface reflection target but varying solar
geometry. A detailed description of the angular scattering effect can be
found in Zeng et al. (2020c). This large range of angles, from
forward scattering (<90∘) to backward scattering
(>90∘), means that a majority of the change in aerosol
scattering comes from angular variations. This also indicates that the
aerosol scattering phase function is a key parameter that needs to be
accurately modeled in order to obtain high-fidelity RT calculations.
Diurnal change in aerosol scattering angle for six selected
surface reflection points, separated into two groups. Group 1 includes
points #1 Santa Anita Racetrack, #2 West Pasadena, and #3 Santa Fe
Dam that are close to CLARS-FTS; group 2 includes #15 Santa Monica Mt.,
#17 Rancho Cucamonga, and #19 Long Beach that are further away. Hourly
scenarios from 20 June and 22 December 2013 are used to represent summer and
winter solar geometries, respectively.
GFIT3: a full physics approach for retrieving XCO2 and XCH4 from
CLARS-FTS observations
GFIT3 incorporates the following four major components: (1) a pre-processing
step using the CLARS-GFIT algorithm to generate gas absorption coefficients
and other related parameters, as well as the O2 slant column density
(SCD) for excluding cloudy and heavy aerosol loading soundings; (2) a
forward RT model (RTM) to generate synthetic spectra in order to simulate
observed CLARS-FTS spectra; (3) an inverse model based on optimal
estimation to update the surface and atmospheric state vector to minimize
the difference between model and observation; and (4) a post-processing
screening step to filter out bad retrievals. The workflow chart is shown in
Fig. 3.
Workflow of GFIT3 for retrieving XCO2 and XCH4 from
CLARS-FTS observations. There are four major components: (1) a
pre-processing step to identify soundings free of clouds and heavy aerosol
loading; (2) a forward RTM to generate synthetic spectra in order to
simulate observed CLARS-FTS measurements; (3) an inverse model based on
optimal estimation to update the surface and atmosphere states to minimize
the difference between model and observation; and (4) a post-processing
screening step to filter out bad retrievals.
Pre-processing using CLARS-GFIT
The objective of pre-processing is to identify measurements that are
affected by clouds and/or heavy aerosol loading and to exclude them before
the full physics retrieval. We employ CLARS-GFIT (Fu et al., 2014),
which is a modified version of the GFIT program (version GGG2014), to
retrieve O2 SCD using the same spectral bands and spectroscopic
parameters used by TCCON. The recently released GGG2020 with major updates
to the spectroscopic line lists will be incorporated into CLARS-GFIT in the
near future. Aerosol scattering is not considered in CLARS-GFIT; the ratio
of retrieved O2 SCD to calculated O2 SCD estimated from surface
pressure reanalysis data (National Center for Environmental Prediction (NCEP) reanalysis in this study), denoted by O2 ratio, acts as a proxy
(Zeng et al., 2020c). We filter out data with (1) a O2 ratio
less than 0.85 (low clouds and high aerosol loading) and larger than 1.02
(high clouds); (2) SNR less than 100; (3) solar zenith angle (SZA) larger
than 70∘; and (4) spectral fit error larger than 1σ above the
mean of all the spectral fitting residuals. The gas absorption coefficients,
a priori atmospheric profiles, and solar lines processed by CLARS-GFIT will also be
used in the forward RTM of GFIT3.
Calibrating O2 absorption cross section
Analysis of the O2 ratio under different aerosol conditions reveals a
systematic bias (about 2 %) between the retrieved O2 SCD and that
calculated using the NCEP reanalysis surface pressure even in situations
when the atmosphere is clear (Appendix Fig. A1). Such a bias in the
1Δ band has been reported by Washenfelder et al.
(2006), who found that for the TCCON spectra, the retrieved column O2
is consistently 2.27%±0.25% higher than the dry pressure column
estimated from the surface pressure. This bias in TCCON retrievals is
consistent with values for CLARS-GFIT retrievals. A similar systematic bias
was found by Butz et al. (2011) for the O2 A band at 0.76
µm from satellite observations. These biases are most likely
attributable to spectroscopic uncertainties. We adopt a simple method of
scaling the absorption cross sections in the 1Δ band by a
factor of 1.02 to make our modeled radiances in the 1Δ band
consistent with observations.
Forward modelOptical-property-based principal component analysis RTM
RT models simulate the radiance based on inputs of the state vector and
related model parameters. In theory, a sophisticated line-by-line RTM (e.g.,
LIDORT; Spurr, 2008) with a high number of computational
quadrature angles (streams) is needed to accurately simulate the propagation
of sunlight through the atmosphere. However, simulation of high-resolution
CLARS-FTS spectra that require resolving gas absorption lines with fine
spectral sampling is computationally expensive. Instead, many fast RTMs
(e.g., Butz et al., 2011; O'Dell et al., 2012; Somkuti et al.,
2017) have been developed to speed up the radiance calculation without
introducing large systematic errors in the trace gas retrieval. In this
study, we adopt an optical-property-based principal component analysis
(O-PCA) RTM developed by Natraj et al. (2005, 2010) and improved by
Kopparla et al. (2016, 2017). The O-PCA procedure was linearized
and analytic Jacobians developed for the PCA-based radiation fields by
Spurr et al. (2013). It has been shown to be fast and accurate for
retrieving CO2 from satellite measurements (Somkuti et al.,
2017). The O-PCA method first divides the spectral region into bins. Each
bin is characterized by grouping certain optical properties (such as
atmospheric layer trace gas optical depth values or single scattering
albedos) that are similar within the bin. The selection for spectral binning
is typically based on the division of (the logarithms of) the
total-atmosphere gas optical depths into decadal intervals. We use 11 bins
in this study. For each bin, PCA is implemented on a dataset that includes
the extinction optical depth and single scattering albedo profiles, as well
as the (wavelength-dependent) surface albedo and column optical depth for
each aerosol type. High-accuracy line-by-line multiple scattering
calculations (using LIDORT in this work) are then performed for profiles
representing the bin mean and PCA-perturbed properties. For this analysis,
we use 32 streams for these calculations. The multiple scattering
calculations are computationally expensive; the reduction of the number of these
calculations is the main reason for the speed-up afforded by O-PCA. O-PCA
also performs a fast and low-accuracy line-by-line calculation of the
radiances using the two-stream exact single scattering (2S-ESS; Spurr
and Natraj, 2011) model for every spectral point in the band. The 2S-ESS
model computes both the single scattering contribution to the radiance and a
two-stream approximation to the multiple scattering contribution. Finally,
the total radiance field is obtained for every point in the bin by
calculating a wavelength-dependent correction factor to adjust the 2S-ESS
calculations. A detailed description of the O-PCA methodology can be found
in Kopparla et al. (2017) and Spurr et al. (2013).
Simulations (see Fig. 4) show that, while the accuracy of O-PCA
depends on the aerosol loading, almost all of the spectral calculations have
an error of less than 0.1 %. The root mean square error (RMSE) is less than
0.01 %.
Ratio of the difference (relative to the continuum value) between
simulated radiances (using O-PCA) and high-accuracy computations (using
LIDORT with 32 streams). These calculations are based on the 240 scenarios,
with different observation geometries and atmospheric profiles, described in
the inverse experiments in Sect. 4. Four empirical orthogonal functions are used for the O-PCA
computations. Three different aerosol scenarios are considered, with AOD values of
0.01, 0.05, and 0.1 in the 1.27 µmO21Δ band. The overall RMSEs are also indicated.
State vector
The state vector includes all variables that are to be retrieved by GFIT3 in
order to fit the observed spectra. These variables are inputs to the forward
RTM. Table 1 summarizes all the variables in the state vector and
the values used for their uncertainties in the retrieval.
Summary of variables in the state vector and their uncertainties.
VariablesNo. of variablesA priori valueA priori uncertaintyDescriptionsCO2 scale factor11.00.05A priori profile from CarbonTracker modelCH4 scale factor11.00.05A priori profile constructed from GFIT andground observationsH2O scale factor11.00.40A priori profile from NCEPSurface pressure1NCEP2 hPaSurface albedo4Zeng et al. (2018)0.10, 0.07, 0.07, 0.04For the four bands: O2, WCO2, CH4,and SCO2Spectral continuum5×400.01, 0.005, 0.002,Zeroth to fourth orders of Legendre0.0016, 0.001polynomialFrequency shift400.1For the four bandsAOD coarse10.020.02Optical properties from GOCARTAOD fine10.010.02Optical properties from GOCARTAerosol layer height10.70 km0.05 kmEstimates from MiniMPL at CaltechInterference gas scale2 (HDO, 13CO2)1.00.4, 0.02A priori profiles from GFITfactors(1) CO2 and CH4 profiles
We follow the TCCON methodology and perform a retrieval that scales
predefined vertical shapes of CO2 and CH4 to obtain XCO2 and
XCH4. This is faster and simpler than a full profile retrieval that
independently scales gas mixing ratios at different altitudes. The profile
scaling method is also less sensitive to systematic errors related to the
shape of the calculated spectral lines, such as instrumental line shape and spectroscopic line
widths (Wunch et al., 2011). Although a profile retrieval is
possible, there are not enough degrees of freedom in the measurement to
fully resolve the gas profile. Therefore, the retrieval problem will be
ill-posed and underdetermined if strong constraints are not imposed on the
vertical profile. Sensitivity tests show that the profile scaling approach
is efficient and that errors from possible bias in the profiles are small
(Sect. 4).
To account for GHG enhancement in the LA PBL, we used CO2 simulations
from the widely used CarbonTracker CO2 model (Peters et al.,
2007), which is an assimilation model incorporating available observations.
The 3-hourly simulations are available from the CarbonTracker CO2 model.
Monthly averaged CO2 profiles are used as the a priori profiles in GFIT3
(Fig. 5a). For CH4, since high-resolution simulations are
not available at city scale, we reconstruct the profiles based on CLARS-GFIT
a priori. A constant PBL enhancement of 91 ppb (parts per billion), as estimated by Verhulst et
al. (2017; Table 5) using the NASA megacity network, is added to the
monthly averaged CH4 profiles, as shown in Fig. 5b. Diurnal
changes in the PBL enhancement are not considered in this analysis.
(a)CO2 vertical profiles are extracted from the
CarbonTracker model over Los Angeles with 3-hourly temporal resolution.
Monthly averaged profiles are used as a priori profiles in GFIT3. (b) Monthly averaged
CH4 vertical profiles are adopted from CLARS-GFIT. A constant PBL
enhancement of 91 ppb, as estimated by Verhulst et al. (2017; Table 5) using
the NASA Megacity network, is added. The hourly variability in CH4 in
the PBL is assumed to be the same as that of CO2 since they are
co-emitted and follow a similar atmospheric mixing process.
(2) Surface albedo and aerosol properties
The contributions to the observed radiance from surface reflectance and
aerosol scattering are coupled. Similar to Zeng et al. (2018), we
assume a Lambertian surface and calculate the a priori surface albedo by ratioing
the measured radiance reflected from the surface target by that reflected by
a Spectralon board beside the FTS. The Spectralon measurement represents the
incident radiance before entering the PBL. For aerosols, we use AOD values
from Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero) data (Rienecker et al., 2011) and
associated optical properties from the Georgia Institute of
Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART; Chin et al., 2002) model,
which includes five aerosol types: sea salt, dust, organic carbon, black
carbon, and sulfate. In light of the difficulty in resolving so many aerosol
types from measurements, we separate the five aerosol types into two groups
based on size: coarse-mode (sea salt and dust) and fine-mode (organic
carbon, black carbon, and sulfate). While the sizes, extinction efficiency,
and phase function of aerosols in the fine mode are similar, the black
carbon has a much smaller single scattering albedo (SSA). For sea salt and
dust aerosols, five differently sized bins are separately tracked in the
Modern-Era Retrospective analysis for Research and Applications (MERRA) model. The sea salt, black carbon, organic carbon, and sulfate are all
hygroscopic. GFIT3 uses monthly average aerosol optical properties
(extinction efficiency, SSA, and phase function) at four daytime hours
(07:00, 10:00, 13:00, 16:00 LT). The monthly averaged density fraction of
aerosols is shown in Fig. 6. While the fine-mode aerosols show
identical monthly variabilities, the coarse-mode particles show a clear
seasonal cycle, with more sea salt in summer originating from the ocean and
more dust in winter originating from the Mojave desert and transported to
the LA Basin. Figure 7 shows the wavelength dependence of aerosol
optical properties averaged over all months in 2013. Fine-mode aerosols have
a larger Ångström exponent, and hence a greater wavelength dependence, than
coarse-mode aerosols. To illustrate changes in phase function, the asymmetry
factor (that quantifies the extent of forward scattering) is used. An
asymmetry factor value of 0 represents isotropic scattering; the value
increases to 1.0 as the phase function peak sharpens in the forward
direction.
Aerosol composition from Modern-Era Retrospective Analysis for
Research and Applications (MERRA) reanalysis data for LA (07:00, 10.00, 13:00, 16:00 LT). (a) Monthly averaged density fraction of aerosols for dust
and sea salt. The dry size bins for dust (DU01 to DU05) correspond to the
radius limits (in microns) 0.1–1, 1–1.8, 1.8–3, 3–6, and 6–10,
respectively. Similarly, for sea salt (SS01 to SS05), the corresponding
values are 0.03–0.1, 0.1–0.5, 0.5–1.5, 1.5–5, and 5–10, respectively.
(b) Monthly averaged density fraction for hydrophilic black carbon
(BC_PHI), hydrophobic black carbon (BC_PHO),
hydrophilic organic carbon (BC_PHI), hydrophobic organic
carbon (BC_PHO), and sulfate (SU). MERRA data below the
CLARS-FTS elevation (1.67 km) are used.
Wavelength dependence of aerosol optical properties (averaged over
a year) in the 1.27 µmO21Δ absorption band, 1.61 µm
weak CO2 absorption band, 1.65 µmCH4 absorption band, and
2.06 µm strong CO2 absorption band from the Georgia Institute of
Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport
(GOCART) model. (a) Mass extinction efficiency, (b) single
scattering albedo, and (c) asymmetry factor for fine (blue) and coarse
(red) modes. These aerosol optical properties are density weighted on a
monthly basis for daytime only (07:00, 10:00, 13:00, 16:00 LT). For
aerosols that are hygroscopic (size dependent upon relative humidity),
monthly average humidity is used.
In the retrieval algorithm, we retrieve AODs for the coarse and fine modes,
in addition to the aerosol layer height (ALH; Table 1). The single
scattering albedos (SSAs) and phase functions of the coarse and fine modes
are prescribed and not retrieved. The effective SSA for the coarse mode is
calculated as the mean of the SSA values (from the GOCART model) of sea salt
and dust, weighted by their simulated AODs from MERRAero. The same approach
is applied to fine-mode aerosols except using black carbon, organic carbon,
and sulfate. The effective phase functions can be calculated in a similar
manner, except that the weighting is done by the scattering AOD. We do not
consider the geometric thickness of the aerosol layer since it has a much
smaller impact on the observed radiance compared to the total AOD
(Zeng et al., 2019). Practically, in the forward model, the
aerosols are placed in two adjacent layers. The fractions of AODs in each
layer are adjusted (with total AOD conserved) to change the effective ALH.
Since both fine- and coarse-mode aerosols are relatively well mixed in the
atmosphere, we assume that they have the same effective ALH. The a priori AODs are
derived from monthly averaged AERONET observations at Caltech and the a priori ALH
from an aerosol profiling lidar (MiniMPL), also at Caltech (Zeng et
al., 2018). For the retrievals, the a priori ALH is set to 0.7 km, representing an
average from all available MiniMPL observations.
(3) Surface pressure
The a priori surface pressure is extracted from NCEP reanalysis data (Kalnay
et al., 1996), which is used for GGG2014 TCCON retrievals (Wunch et
al., 2015). A comparison with ECMWF ERA5 reanalysis (Hersbach et
al., 2020), which has a higher resolution, indicates that the two surface
pressure datasets are highly correlated, with a standard deviation of the
difference of about 2 hPa (Zeng et al., 2020b). In the GFIT3
retrieval, we assume this value as the uncertainty for surface pressure.
Solar model
To construct the high-resolution solar irradiance, we combine the solar
continuum level estimated from the solar spectrum developed by
Kurucz (2005) (http://kurucz.harvard.edu/sun/irradiance2008/, last access: 20 September 2021) and
the high-resolution solar pseudo-transmittance spectrum from GFIT
(Toon, 2014; https://mark4sun.jpl.nasa.gov/toon/solar/solar_spectrum.html, last access: 20 September 2021). The Kurucz spectrum was created from the solar
spectrum measured by a high-resolution FTS at the Kitt Peak National
Observatory. In the near-infrared spectral regions of relevance to this
work, Toon's solar pseudo-transmittance spectrum is a combination of
high-resolution spectra from balloon FTS, ground-based Kitt Peak, and TCCON
observations. A similar combination of Kurucz and Toon reference spectra was
also used by GOSAT (Yoshida et al., 2013). The absolute solar
irradiance is necessary to constrain aerosol scattering and surface
reflectance.
Jacobian
The Jacobian matrix contains the first order derivative of the simulated
radiance with respect to all state vector elements and is a key variable in
inverse modeling to fit the observed spectra by iteratively optimizing the
state vector. This matrix has a dimensionality of m×n, where m refers to the
number of measurement channels and n is the number of state vector elements.
Figure 8 illustrates a sample Jacobian matrix calculated by O-PCA.
Sample Jacobian values from O-PCA for representative state vector
elements in the GFIT3 retrieval algorithm. This Jacobian matrix is based on
observations over the Santa Anita surface reflection point on 28 September
2013, with a solar zenith angle (SZA) of 36∘. The y-axis labels
indicate the units of the Jacobian values.
Inverse modelingOptimal estimation
Mathematically, the measurement vector y, which is the
observed CLARS-FTS radiance, is related to the state vector
x, including O2, CO2, and CH4 SCDs and
other relevant geophysical parameters, through a forward
model F and model parameter vector
b.
y=F(x,b)+ε
Specifically, b is a set of input parameters for the
forward model that are not retrieved, such as gas absorption coefficients
and observing and solar geometries, while the state vector x is a
set of parameters to be retrieved, such as trace gas columns, aerosol
properties, and surface properties. The forward model F is an RT
model (O-PCA in this study) that simulates the radiance based on input
parameters b and x.
ε is the error vector containing both the measurement
noise and the forward model error. The goal of optimal estimation is to
obtain the state vector with maximum a posteriori probability by minimizing the
following cost function (Rodgers, 2000):
J(x)=χ2=[y-F(x,b)]TSε-1[y-F(x,b)]2+(x-xa)TSa-1(x-xa),
where xa is the a priori state vector, Sa is
the a priori covariance matrix for the state vector, and
Sε is the measurement error
covariance matrix. In this study, the measured radiance from the O21Δ, WCO2, CH4, and SCO2 absorption bands
constitutes the measurement vector y. For the sake of
simplicity, we assume that the measurement noise dominates and that there is
no cross-correlation between different spectral channels, resulting in a
diagonal Sε matrix. In theory,
the spectral error term ε includes the measurement
noise, which can be characterized by the SNR, and uncertainty in the forward
model. While it is reasonable to assume that the measurement noise
dominates, the forward model error, including multiple components such as
RTM uncertainty, errors in spectroscopic constants, and biases in prescribed
aerosol optical properties, may not be negligible. These uncertainties
propagate through the retrieval algorithm to the retrieved GHGs. Further
investigation of the measurement error covariance matrix from post-retrieval
analysis of spectral residual and goodness of fit is discussed in
Sect. 6.3.
To estimate forward model uncertainty related to RT approximations, we use
the results from Fig. 4, representing spectral fitting error
estimates between O-PCA and LIDORT. The RMSE is less than 0.01 %, which is
much smaller than the measurement noise. We therefore use the measurement
noise to generate the matrix Sε.
We adopt the Levenberg–Marquardt method (Levenberg, 1944; Marquardt,
1963; Rodgers, 2000) to obtain the optimal estimate of the state vector
x that minimizes the cost function J(x) through
an iterative process:
xi+1=xi+[(1+γ)Sa-1+KiTSε-1Ki]-13×{KiTSε-1[y-F(xi,b)]-Sa-1[xi-xa]},
where the subscript i indicates the ith iteration, and the parameter
γ is chosen at every step to minimize the cost function. Initially
it is set to be 10. K is the Jacobian matrix, which is
the first derivative of F(x,b) with respect to
x:
Ki=∂F(xi,b)∂xi,
where each element in Ki defines the sensitivity of
the simulated radiance to the corresponding geophysical variable in the
state vector. At each step, the parameter γ is updated based on the
ratio R (Fletcher, 1971):
R=χi2-χi+1,true2χi2-χi+1,forecast2,
where χi+1,true2 refers to the cost function computed with the
updated state vector xi+1 in the forward model
Fi+1=F(xi+1,b), while χi+1,forecast2 is
computed using a linear approximation to the forward model
Fi+1=Fi+Ki⋅(xi+1-xi). R quantifies the impact of forward
model nonlinearity on cost function reduction. If the linear approximation
is perfect, then R will be unity since χi+1,true2=χi+1,forecast2. The strategy for updating R is as follows: if R is
greater than 0.75, then reduce R by a factor of 2; if R is less than 0.25,
then increase R by a factor of 10; otherwise, leave R unchanged.
Convergence is achieved when the change in the state vector, di, is small
compared to the a posteriori error:
di2=(xi-xi+1)TS^-1(xi-xi+1)≪n,
where n is the number of state vector elements, and S^ is the a posteriori
error covariance matrix for the estimated state vector x^. At
convergence, S^ can be estimated as follows:
S^=(KTSε-1K+Sa-1)-1,
where S^ includes the a posteriori uncertainties of all retrieved elements in the
state vector and their correlations.
Averaging kernel
Similar to TCCON, we use the column averaging kernel calculated from our
retrieval algorithm to quantify the altitude-dependent sensitivity of the
total column retrievals to changes in the vertical profile of partial column
densities. Ideally, the column averaging kernel would be unity at all
altitudes, meaning a unit change in partial column at any altitude would
lead to the same amount of change in the total column. In practice, however,
the column averaging kernel is not a perfect unit vector. To derive the
column averaging kernel, we first calculate the full averaging kernel matrix
(m×m):
A=(KTSε-1K+Sa-1)-1KTSε-1K,
where m is the number of atmospheric layers. Aij represents the
derivative of the retrieved mixing ratio at level i with respect to the true
mixing ratio at level j. The jth element of the column averaging kernel
is given by
aj=∑iAijΔpiΔpj,
where Δpi is the pressure thickness at level i, and aj describes
the change in the retrieved total column abundance with respect to a
perturbation of the partial column at the jth atmospheric level.
Figure 9 shows examples of column averaging kernels for CO2 and
CH4 at different SZA values. Both spectral channels show a similar shape
and have higher averaging kernel values (close to 1) in the troposphere than
in the stratosphere. For a comparison of CLARS-FTS measurements with other
datasets (such as satellite observations), the above averaging kernels and
a priori profiles from CLARS-GFIT should be taken into account. Details about
implementation of the averaging kernel correction can be found in
Wunch et al. (2011).
Examples of column averaging kernels for (a)CO2 and (b)CH4 with different SZAs. These are from observations of the Santa Anita
surface target on 28 September 2013.
Post-processing
After obtaining the SCDs for O2, CO2, and CH4, XCO2 and
XCH4 can be calculated as follows:
10XCO2=CO2SCDO2SCD×0.2095,11XCH4=CH4SCDO2SCD×0.2095,
where the constant 0.2095 is the column-averaged dry-air mixing ratio of
O2 in the atmosphere. In the post-processing, multiple filters are
applied to ensure good retrieval quality. First, retrievals that fail to
converge after 15 iterations according to the procedure outlined in
Eq. (6) are excluded. Second, the spectral fitting residual
(RMSE) for each window should be smaller than 0.01 for all four bands.
Third, outliers in retrieved state vector parameters, including O2,
CO2, and CH4 SCDs, which have a large impact on XCO2 and
XCH4, are filtered. In this study, we define outliers as values that
are more than 3 standard deviations away from the mean. For retrievals
of CLARS-FTS observations from June 2013 to May 2014, about 80 % of all
pre-filtered observations pass the post-processing filters.
Inversion experiments based on synthetic spectra
The goal of applying the GFIT3 algorithm to simulated synthetic spectra is
to assess the performance of the algorithm in retrieving XCO2 and
XCH4 and to quantify the impacts on the accuracy due to factors such as
aerosol scattering, imperfect meteorological data, RTM errors, uncertainty
in gas absorption, and instrument noise. In this study, we primarily
concentrate on three potentially important error sources: imperfect
characterization of aerosol scattering, assumptions about the vertical
distributions of CO2 and CH4, and biases due to usage of the O-PCA
RTM.
We first generate synthetic spectra using LIDORT with high accuracy (32
streams) to reproduce the “true” spectra under three aerosol loading
scenarios (total column AOD=0.01, 0.05, and 0.1), which covers the AOD
range for non-cloudy days based on Caltech AERONET measurements
(Appendix Fig. A2). Given that CLARS-FTS observes large air mass
factors (more than 8 times the vertical column) in the PBL because of
the long slant column in the line of sight, the aerosol loading along the
slant path is much higher than the column AOD. To simulate the synthetic
spectra, we use 3-hourly aerosol composition data from MERRA aerosol
reanalysis and other optical properties (SSA and phase function) from the
GOCART model (Sect. 3.2.2). CO2 and CH4 vertical
profiles are derived as described in Sect. 3.2.2. The hourly
variability in CH4 in the PBL is assumed to be the same as that in
CO2 since they are co-emitted and follow a similar atmospheric mixing
process. Surface albedos for the O2, WCO2, CH4, and SCO2
bands are estimated from CLARS-FTS observations. All other inputs are the
same as the state vector described in Sect. 3.2.2. Measurement
noise (which we assume to be white noise with a mean of 0 and a standard
deviation of 1/SNR) is added to generate the synthetic spectra as a proxy
for CLARS-FTS observations. We test the GFIT3 algorithm on the synthetic
spectra for the three surface targets at Santa Anita, Santa Fe, and West
Pasadena over a wide range of observing geometries encompassing four seasons
(January, April, July, and October) and 5 h from early morning to late
afternoon (∼ 08:00–09:00, 10:00–11:00, 12:00–13:00, 14:00–15:00, 16:00–17:00). Since
data in the early morning and late afternoon hours may not be available in
winter, we select observations from the available daytime data with a time
step of at least 1 h. In total, 60 different observation scenarios are
selected.
We conduct four retrieval tests on the synthetic spectra, as listed in
Table 2. In Test 1, we assume perfect knowledge of aerosol
composition and GHG profiles. The goal is to assess the capability of O-PCA
and the inverse framework for retrieving XCO2 and XCH4. In Test 2,
we use O-PCA but with monthly average aerosol composition and GHG profiles.
The goal is to investigate retrieval uncertainty due to assumptions about
aerosols and GHG profiles, as well as RT calculation approximations. Test 3 is
similar to Test 2, except that we use 3-hourly GHG profiles. The goal is to
isolate the impact of uncertainty in aerosol composition. Test 4 is also
similar to Test 2, except that we use 3-hourly aerosol composition. The goal
is to isolate the impact of imperfect knowledge of GHG vertical
distribution. For each observation scenario in these tests, we calculate the
difference between the retrieved state vector and the “truth” that was
used to generate the synthetic spectra. The retrieval error (in percentage)
is defined as the ratio of the calculated difference to the “truth”.
Figure 10 shows results from Test 1. It is evident that all
simulations have a mean absolute error (MAE) of less than 0.5 %. The retrieval
error, however, increases as AOD increases. In the haziest scenario (AOD=0.1), the largest retrieval error is around 1 %. Results from Test 2
(Fig. 11) are broadly similar to those from Test 1. The errors are
generally larger than those in Test 1 due to the bias in aerosol optical
properties and atmospheric profiles. On average, the MAEs are less than
1 %; the largest errors are greater than 2 %. The bias in the retrieved
AOD is smaller at larger AOD values because of the stronger aerosol
scattering signal. Moreover, the bias in ALH is about -10 % on average,
indicating an average error of less than 1 km. Figure 12 shows results
from tests 3 and 4. No clear correlation can be observed between bias in
XCO2 and XCH4 retrievals and that in aerosol optical properties
for either coarse- or fine-mode aerosols. This indicates that a combination
of fine- and coarse-mode aerosols is able to accurately capture the
scattering effects. On the other hand, there is a clear correlation between
bias in the trace gas columns and that in PBL enhancement (defined as the
difference in PBL GHG mixing ratios between 3-hourly and monthly a priori
atmospheric profiles). However, the MAE is still almost always less than
1 %.
Results for Test 1. Errors in retrieved XCO2 and XCH4
are quantified for simulations with three different values of AOD (0.01,
0.05, and 0.1). The errors arise mainly due to the bias caused by the O-PCA
approximation compared to the exact atmospheric radiative transfer process.
MAE represents the mean absolute error.
Results for Test 2. Errors in retrieved (a)XCO2 and
XCH4 for three different values of AOD (0.01, 0.05, and 0.1), (b) AOD
for the same scenarios as in (a), and (c) ALH for all AOD scenarios. The errors
have contributions from biases due to the O-PCA RTM and due to the imperfect
knowledge of aerosol optical properties and vertical distribution of
atmospheric trace gases.
Results for Test 3 (a and b) and Test 4 (c). Panels (a)
and (b) show XCO2 and XCH4 biases as a function of biases in SSA
and g (asymmetry factor) of coarse- and fine-mode aerosol, respectively. Panel (c)
shows the same as a function of biases in PBL CO2 and CH4
enhancement. This bias is defined as the difference in PBL GHG mixing ratios
between 3-hourly and monthly a priori atmospheric profiles.
Synthetic experiments to assess the impact of RTM, aerosol
composition, and GHG profiles on retrievals of XCO2
and XCH4 from CLARS-FTS observations.
ExperimentAerosolAtmosphericRT modelObjectivecompositionprofileSynthetic spectra3-hourly3-hourlyLIDORTTo create synthetic spectraTest 1: noise free simulation3-hourly3-hourlyO-PCATo investigate the error due to RTM approximationsTest 2: operational algorithmMonthlyMonthlyO-PCATo investigate the error due to the operational algorithmTest 3: aerosol impactMonthly3-hourlyO-PCATo investigate the error due to assumptions about aerosol compositionTest 4: vertical profile impact3-hourlyMonthlyO-PCATo investigate the error due to assumptions about vertical distribution of CO2 and CH4Retrieval results for CLARS-FTS observations
We applied the GFIT3 retrieval algorithm to 1 year of CLARS observations
from June 2013 to May 2014. Over this period, CLARS-FTS spent a large
portion of measurement time observing the Santa Anita, Santa Fe, and West
Pasadena targets. Therefore, these three surface reflection points are our
focus in this section. In total there are 36 170 observed spectra from
CLARS-GFIT. After pre-processing, we obtain 12 911 spectra that pass the
filters for processing by the GFIT3 algorithm. Most of the retrievals
converge after less than 10 iterations. However, about 20 % of the
measurements fail to converge, and another 20 % fail to pass the
post-processing filters; these are discarded. Eventually, 7733 spectra are
available for further analysis.
Residuals from spectral fitting
Figure 13 shows normalized residuals with respect to the continuum
level from spectral fitting for the O2, WCO2, CH4, and
SCO2 bands. The RMSE values are less than 1 %, and the majority of
residuals are less than 0.5 %. The SCO2 band shows a larger residual
compared to the other bands, partly due to imperfect spectroscopic data
(Crisp et al., 2012) and partly due to the large aerosol scattering
contribution, especially in the strong absorption lines (of which there are
several due to the high spectral resolution of CLARS-FTS). It is
instructive to compare these results with fitting residuals from CLARS-GFIT
(Appendix Fig. A3), in which aerosol scattering is neglected. It is
evident that the residuals from GFIT3, especially in the SCO2 band, are
significantly smaller. In the GFIT3 algorithm, the aerosols are primarily
constrained by the O2 and the SCO2 bands. This is because a priori atmospheric pressure is very accurate (∼0.2 % uncertainty)
and O2 concentration well known, thereby resulting in the O2
absorption spectra providing strong constraints on the aerosol scattering
effects. For the SCO2 band, since most of the absorption lines are
saturated, any extra radiance in this spectral region is attributable to
aerosol scattering. Ignoring aerosol scattering results in higher residuals,
especially for the strong absorption lines (Appendix Fig. A3).
Fitting residuals are significantly reduced using GFIT3. Results from this
study suggest that the effects of scattering in the observed spectra can be
accurately characterized by the aerosol models used in the GFIT3 algorithm.
Not accounting for scattering leads to large spectral fitting residuals, and
therefore large biases, in GHG retrievals.
(a) Upper left: median fitting residual (black) and ±1σ range (grey) for the O2 band. Lower left: sample measured
spectrum. Right: histogram of fitting residuals. Panels (b–d) are the
same as (a) but for the weak CO2 band, the CH4 band, and the
strong CO2 band, respectively.
Comparison of retrieved AOD with AERONET and ALH with MiniMPL
We compare the retrieved AOD with ground-based AERONET observations at
Caltech. AERONET is a global ground-based aerosol monitoring network
(Holben et al., 1998) that has been providing high-accuracy
measurements of total AOD from the ultraviolet to the near infrared. The
AERONET instrument at Caltech is located on the university campus in
Pasadena, which is geographically close to the Santa Anita, Santa Fe, and
West Pasadena surface targets. The Caltech AERONET measurements cover the
wavelength range from 340 to 1020 nm. To derive the AOD in the O21Δ band, we extrapolate from AERONET measurements using the
Ångström exponent law (Seinfeld and
Pandis, 2006). Figure 14 shows that the retrieved AOD is in good
agreement with Caltech AERONET AOD, with RMSE values of about 0.02. The
AERONET AOD uncertainty is on the order of 0.01–0.02 in the
0.34–0.87 µm spectral range (Eck et al., 1999); our estimated RMSE value of
0.02 is therefore very close to the noise level. The difference is larger
for higher AOD values. This effect may be due to two reasons. First, GFIT3
retrievals have higher uncertainty at large AOD values because of the
magnification of biases due to the misrepresentation of aerosol optical
properties. Second, CLARS-FTS and AERONET observe different parts of the
atmosphere due to differences in their observing geometries. Considering the
spatial heterogeneity of aerosol distribution, such a difference between
retrieved and observed AOD is expected. The retrieved ALH values agree
closely with MiniMPL observations (Fig. 15); however, they do not
have significant correlation on a point-by-point basis (not shown). This
suggests the difficulty in constraining ALH when it is jointly retrieved
with GHGs. The signal from ALH may be interfered with by the imperfect
characterization of other factors that existing full physics algorithms
cannot resolve. However, when ALH is retrieved independently using
specifically targeted O2 absorption lines, high accuracy can be
achieved (Zeng et al., 2019). This suggests the potential of a
two-step procedure, as proposed in Zeng et al. (2020a), in which the
O2 absorption lines are used to provide strong constraints on AOD and
ALH. The improved AOD and ALH estimates can then be used as inputs for the
retrieval of GHGs.
AOD comparison between measurements from the Caltech AERONET site
and GFIT3 retrievals. The AERONET AOD at 1.27 µm is extrapolated from
actual AERONET observations using the Ångström exponent law. Histograms of
the difference between AERONET and GFIT3 retrievals are also included.
Comparison of effective ALH from the MiniMPL lidar instrument on
the Caltech campus and GFIT3 retrievals for the Santa Anita, Santa Fe, and
West Pasadena surface targets.
Retrievals of XCO2 and XCH4
Figure 16 compares XCO2 and XCH4 retrievals from GFIT3
(after post-processing) and CLARS-GFIT. In general, when aerosols are not
accounted for in the retrieval, as in CLARS-GFIT, XCO2 and XCH4
are overestimated (see discussion in Sect. 6.2). The bias can be
up to about 10 % for both XCO2 and XCH4. The scatter plots
indicate that the differences in XCO2 and XCH4 are significantly
correlated with AOD. The correlation coefficients are higher for Santa
Anita probably because of the smaller changes in scattering angle (and
therefore aerosol effects) compared to the other two surface targets. The
XCO2 and XCH4 differences are in the range of 10–30 ppm (parts per million) and 50–150 ppb,
respectively, for an AOD value of 0.05 in the 1Δ absorption
band. Since CO2 and CH4 are retrieved at similar wavelengths, the
biases in XCO2 and XCH4 retrievals due to aerosol scattering are
expected to be comparable. The impact on the retrieved XCH4/XCO2
ratio in the presence of aerosols is further discussed in Sect. 6.1. Appendix Fig. A4 shows comparisons for all 28 surface targets
based on available measurements from June 2013 to May 2014. In comparison to
the three sites close to the CLARS location (Santa Anita, Santa Fe, and West
Pasadena), for sites that are further away, valid retrievals that pass the
filters have lower AOD values. This is because of their longer slant paths
in the PBL, leading to a larger scattering effect even under the same
vertical aerosol loading.
Comparison of (left) XCO2 and (right) XCH4 retrievals
from GFIT3 and CLARS-GFIT for the (a) Santa Anita, (b) Santa Fe, and (c)
West Pasadena surface reflection targets. The data points are color-coded by
the retrieved AOD. The insets show scatter plots between retrieved AOD and
the difference in XCO2 or XCH4 between GFIT3 and CLARS-GFIT.
DiscussionsTesting the assumption that the ratio between XCH4 and XCO2 is not affected
by aerosol scattering
The tracer–tracer ratio method to retrieve CH4 emissions based on
CO2 emissions or CH4 concentration based on CO2 concentration
assumes that the CH4/CO2 ratio cancels out any systematic errors
caused by aerosol scattering in the two bands (e.g., Frankenberg et
al., 2005; Parker et al., 2011; Wong et al., 2015, 2016; He et al., 2019).
However, the fact that the spectral regions do not exactly overlap and that
the line intensities have different strengths may reduce the validity of
this assumption. Since XCO2 and XCH4 are simultaneously retrieved
from both GFIT3 and CLARS-GFIT, these retrievals serve as good datasets for
testing the ratioing assumption. Figure 17 shows a scatter plot
between CLARS-GFIT XCH4/XCO2 ratios and those from GFIT3. No
systematic bias is observed from this comparison, suggesting the high accuracy
of using the tracer–tracer ratio method to accurately estimate CH4
emissions using remote sensing measurements in the presence of aerosols.
Scatter plot of the XCH4/XCO2 ratio from GFIT3 and
CLARS-GFIT. The 1:1 line is shown in black. The red line denotes the best
fit using type II linear regression to fit the data. The equation for the
regression fit is also shown.
Impact of aerosol scattering on XCO2 and
XCH4 retrievals regulated by surface reflectance
The effects of aerosol scattering and surface reflectance on modifying the
path of solar radiation, and thereby introducing biases in trace gas
retrievals, are coupled. A darker surface means a relatively higher
contribution from aerosol scattering that will shorten the expected light
path. On the other hand, in the presence of a brighter surface, enhanced
multiple scattering between the surface and the aerosols may lead to a
longer light path. With an RTM, this coupling effect can be explicitly
characterized. In general, in the presence of aerosols, XCO2 (or
XCH4) will be overestimated if scattering is not accounted for,
according to Eqs. (10) and (11). This is because there is larger bias
(underestimation) in O2 SCD than in CO2 (or CH4) SCD due to
higher AOD at the O2 wavelength. According to MERRA reanalysis data,
the AOD ratio between 1.6 and 1.27 µm is about 0.8. However,
this is assuming that the surface reflectance is relatively unchanged
between the two bands. In fact, the surface is usually darker in the 1.6
µmCO2 band than in the 1.27 µmO2 band. According to
our estimates, the reflectance ratio between the two bands is about
0.5–0.8, depending on the composition of the target (soil, vegetation,
buildings, etc.). As a result, the darker surface at 1.6 µm may
compensate for the lower AOD and increase the relative aerosol scattering
contribution.
If the reflectance ratio is close to 1 (no spectral dependence), the
XCO2 (or XCH4) bias will be primarily determined by the AOD ratio.
Here we assume the aerosols are mostly non-absorbing or do not have a strong
spectral dependence of absorption. However, if the reflectance ratio is
small (strong spectral dependence), the surface is much darker in the
CO2 band than in the O2 band. In this case, it is possible for the
surface darkening effect to be more dominant than the AOD effect in driving
the bias (underestimation) of retrieved XCO2 (or XCH4). For
example, the West Pasadena location is special in that it is close to a
park, which has different land use types compared to the other surface
targets. This target has a much lower reflectance ratio than other locations
(Appendix Fig. A5), which may explain the underestimation by
CLARS-GFIT compared to GFIT3 for this location, as seen in Figs. 14c and A4.
Post-retrieval analysis of fitting residual and goodness of fit
The benefit of using the GFIT spectroscopy database is that it has been
carefully evaluated based on highly accurate TCCON observations. To further
investigate the errors in spectroscopy, an important contributor to the
forward model error in Eq. (1), we apply principal component analysis
(PCA) to the fitting residuals. This analysis method has been used by the
OCO-2/3 operational algorithm to correct for errors in CO2
spectroscopic parameters and the atmospheric state (O'Dell et al.,
2018). The three principal components (PCs) with the largest variance are
shown in Appendix Fig. A6. The features in these PCs are mostly
related to spectroscopic uncertainties. These PCs might be related to line
width, instrument effects, and the solar spectrum. For example, PC-3 from
the WCO2 band appears to be correlated with absorption features that
may be attributed to very small changes in the line width. However, this PC
can only explain a few percent of the residual variance. Overall, there are
no PCs that can explain more than 10 % of the variance in the fitting
residual. This is because the fitting residual itself is very close to
random and without large systematic errors. We therefore believe that
spectroscopic errors should not be a major issue here.
The reduced χ2, which is the χ2 from Eq. (2)
divided by the total number of measurements and state vector elements,
infers the goodness of fit and can be used to evaluate the error covariance
matrix. Theoretically, if the error covariance matrix is properly
implemented in the retrieval algorithm, the reduced χ2 should be close to 1 after convergence, which means that the fitting
residuals are consistent with the detector noise estimates. The histogram of
reduced χ2 from all converged retrievals (Appendix Fig. A7) indicates that most of the retrievals have a χ2 close to 1 (83 % having χ2 less than 1.5). This
indicates that the error covariance matrix used in the retrieval algorithm,
which assumes that measurement noise is uncorrelated between different
spectral channels, is realistic. It should be noted that inaccuracies in the
spectroscopic input data and improperly modeled instrument effects may
contribute to the small deviation of χ2 from unity.
Conclusions
In this study, we developed GFIT3, a full physics algorithm to retrieve
trace gases in the presence of aerosols, and demonstrated its performance by
retrieving XCO2 and XCH4 from CLARS-FTS measurements. This
algorithm simultaneously retrieves fine- and coarse-mode aerosol properties
including AOD and ALH. Inverse experiments based on synthetic spectra
indicate that the uncertainty in CLARS-FTS retrievals of XCO2 and
XCH4 due to uncertainty in the RTM, aerosol scattering, and atmospheric
profile, which constitute the three most important sources of error, is less
than 1 % (or less than ∼4 ppm for XCO2 and
∼20 ppb for XCH4). The retrieval uncertainty for real
CLARS-FTS observations is partly due to the imperfect characterization of
aerosol properties. Nonetheless, we find that the retrieved AOD has good
agreement with AERONET measurements. Unfortunately, direct comparison of
XCO2 and XCH4 with existing TCCON data at Caltech is not feasible. On one hand,
CLARS-FTS cannot directly target the TCCON site at Caltech due to mountain
ridges that block the line of sight. On the other, TCCON uses directly
transmitted solar spectra to measure GHG columns, which have different
geometries from CLARS observations; the spatial heterogeneity of GHG
distributions between the incident and reflected solar paths in the boundary
layer make the results difficult to compare.
Future research will focus on developing a “divide and conquer” algorithm
for retrieving aerosol properties and GHGs in order to further improve the
accuracy of GHG retrievals. The basic idea is to use a two-step procedure.
First, O2 absorption lines will be used to constrain the AOD and ALH
based on a spectral sorting technique (Zeng et al., 2019). These
values will then provide constraints for AOD and ALH (with uncertainty
estimates) for the retrieval of GHGs.
(a) Time series of O2 volume mixing ratio (VMR) scale factor (VSF) and (b)
histogram of VSF. The VSF value (indicated by the dashed red line) of
∼1.02 represents situations when the atmosphere is clear. See
Sect. 3.1.1 for details.
AOD in the 1.27 µmO2 absorption band estimated from
AERONET observations (2010–2017).
Example of spectral fitting residuals from the CLARS-GFIT (red;
ignoring aerosol scattering) and GFIT3 (blue; accounting for aerosol
scattering) algorithms for the O2, WCO2, CH4, and SCO2
spectral windows. The spectral fitting RMSEs are also indicated. This
example is for an observation over the West Pasadena surface target on
28 September 2013 with a solar zenith angle of 65∘.
Comparison of XCO2 retrievals from GFIT3 and CLARS-GFIT for
all surface reflection targets. The data points are color-coded by the
retrieved AOD.
Histogram of the ratio of reflectance between the WCO2 and
O2 bands for all the surface targets. The reflectance values are
obtained from GFIT3 retrievals. The number in red is the average ratio for
the surface target.
Mean radiance spectrum and the three leading PCs ranked by the variance explained by these PCs, obtained by applying PCA on the fitting residuals, for the (a)O2, (b)WCO2, (c)CH4, and (d)SCO2 bands. The variance explained by each PC is also indicated.
Histogram of reduced χ2 from all converged retrievals in
this study.
Code and data availability
CLARS-FTS data are available from
https://data.caltech.edu/records/1948 (last access: 21 September 2021) or 10.22002/D1.1948 (Zeng, 2021), and part of the CLARS data is also available from the NASA Megacities Project at https://megacities.jpl.nasa.gov (Sander and Pongetti, 2020). The MiniMPL data are available from the NASA Megacity Project data portal: https://megacities.jpl.nasa.gov/portal/ (Ware et al., 2020). AERONET data for the Caltech site are available from https://aeronet.gsfc.nasa.gov/new_web/photo_db_v3/CalTech.html (Stutz, 2021).
Author contributions
ZZ designed this study, conducted the
experiments, analyzed the results, and wrote the paper. VN developed the
O-PCA code and provided guidance on RT modeling. FX provided guidance on
optimal estimation. SC assisted with the RT calculations. FG analyzed the
aerosol observations and made comparisons. TJP collected the CLARS data, and KS
contributed to spectroscopy analysis. GT provided guidance on using GFIT. YLY
and SPS supervised this study. All authors proofread and commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
A portion of this research was carried out at the
Jet Propulsion Laboratory, California Institute of Technology, under a
contract with the National Aeronautics and Space Administration
(80NM0018D0004). We thank Jochen Stutz from UCLA and his staff for their
effort in establishing and maintaining the Caltech AERONET site.
Financial support
Zhao-Cheng Zeng is supported by subcontract funds from the Jet Propulsion Lab (Sponsor Award Number: 1658775). A part of this research has been supported by the National Aeronautics and Space Administration (grant no. 80NM0018D0004).
Review statement
This paper was edited by Helen Worden and reviewed by Brian Connor and one anonymous referee.
ReferencesBertaux, J.-L., Hauchecorne, A., Lefèvre, F., Bréon, F.-M., Blanot, L., Jouglet, D., Lafrique, P., and Akaev, P.: The use of the 1.27 µmO2 absorption band for greenhouse gas monitoring from space and application to MicroCarb, Atmos. Meas. Tech., 13, 3329–3374, 10.5194/amt-13-3329-2020, 2020. Bril, A., Oshchepkov, S., and Yokota, T.: Application of a probability density function-based atmospheric light-scattering correction to carbon dioxide retrievals from GOSAT over-sea observations, Remote Sens. Environ., 117, 301–306. 2012.Butz, A., Hasekamp, O. P., Frankenberg, C., and Aben, I.: Retrievals of atmospheric CO2 from simulated space-borne measurements of backscattered near-infrared sunlight: accounting for aerosol effects, Appl. Optics, 48, 3322–3336, 10.1364/AO.48.003322, 2009.Butz, A., Guerlet, S., Hasekamp, O., Schepers, D., Galli, A., Aben, I., Frankenberg, C., Hartmann, J.-M., Tran, H., Kuze, A., Keppel-Aleks, G., Toon, G., Wunch, D., Wennberg, P., Deutscher, N., Griffith, D., Macatangay, R., Messerschmidt, J., Notholt, J., and Warneke, T.: Toward accurate CO2 and CH4 observations from GOSAT, Geophys. Res. Lett., 38, L14812, 10.1029/2011GL047888, 2011.Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements, J. Atmos. Sci., 59, 461–483, 10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.Connor, B. J., Sherlock, V., Toon, G., Wunch, D., and Wennberg, P. O.: GFIT2: an experimental algorithm for vertical profile retrieval from near-IR spectra, Atmos. Meas. Tech., 9, 3513–3525, 10.5194/amt-9-3513-2016, 2016.Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5, 687–707, 10.5194/amt-5-687-2012, 2012. Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, 1999. Fletcher, R.: A modified Marquardt subroutine for nonlinear least squares fitting, Report, Atomic Energy Research Establishment, Harwell, England, 1971.Frankenberg, C., Meirink, J. F., van Weele, M., Platt, U., and Wagner, T.: Assessing methane emissions from global space-borne observations, Science, 308, 1010–1014, 10.1126/science.1106644, 2005.Fu, D., Pongetti, T. J., Blavier, J.-F. L., Crawford, T. J., Manatt, K. S., Toon, G. C.,
Wong, K. W., and Sander, S. P.: Near-infrared remote sensing of Los Angeles trace gas
distributions from a mountaintop site, Atmos. Meas. Tech., 7, 713–729,
10.5194/amt-7-713-2014, 2014.He, L., Zeng, Z.-C., Pongetti, T. J., Wong, C., Liang, J., Gurney, K., Newman, S., Yadav, V., Verhulst, K., Miller, C., Duren, R., Frankenberg, C., Wennberg, P. O., Shia, R.-L., Yung, Y. L., and Sander, S. P.: Atmospheric methane emissions correlate with natural gas consumption from residential and commercial sectors in Los Angeles, Geophys. Res. Lett., 46, 8563–8571, 10.1029/2019GL083400, 2019.Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P. de, Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 10.1002/qj.3803, 2020.
Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Set- zer, A., Vermote, E.,
Reagan, J. A., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.:
AERONET – A federated instrument network and data archive for aerosol
characterization, Remote Sens. Environ., 66, 1–16, 1998. Irion, F. W., Gunson, M. R., Toon, G. C., Chang, A. Y., Eldering, A., Mahieu, E., Manney, G. L., Michelsen, H. A., Moyer, E. J., Newchurch, M. J., Osterman, G. B., Rinsland, C. P., Salawitch, R. J., Sen, B., Yung, Y. L., and Zander, R.: Atmospheric Trace Molecule Spectroscopy (ATMOS) Experiment Version 3 data retrievals, Appl. Optics, 41, 6968–6979, 2002.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M.,
Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W.,
Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R.,
and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77,
437–471, 1996. Kopparla, P., Natraj, V., Spurr, R., Shia, R. L., Crisp, D., and Yung, Y. L.: A fast and accurate PCA based radiative transfer model: Extension to the broadband shortwave region, J. Quant. Spectrosc. Ra., 173, 65–71, 2016. Kopparla, P., Natraj, V., Limpasuvan, D., Spurr, R., Crisp, D., Shia, R. L., Somkuti, P., and Yung, Y. L.: PCA-based radiative transfer: Improvements to aerosol scheme, vertical layering and spectral binning, J. Quant. Spectrosc. Ra., 198, 104–111, 2017. Kurucz, R. L.: High resolution irradiance spectrum from 300 to 1000 nm, AFRL Transmission Meeting, Lexington, Mass., 15–16 June 2005, 2005. Levenberg, K.: A method for the solution of certain non-linear problems in least squares, Q. Appl. Math., 2, 164–168, 1944. Marquardt, D. W.: An algorithm for least squares estimation of non- linear parameters, J. Soc. Ind. Appl. Math., 11, 431–441, 1963.Natraj, V., Jiang, X., Shia, R.-L., Huang, X., Margolis, J. S., and Yung Y. L.: Application of principal component analysis to high spectral resolution radiative transfer: A case study of the O2 A-band, J. Quant. Spectrosc. Ra., 95, 539–556, 10.1016/j.jqsrt.2004.12.024, 2005.Natraj, V., Shia, R. L., and Yung, Y. L.: On the use of principal component analysis to speed up radiative transfer calculations, J. Quant. Spectrosc. Ra., 111, 810–816, 10.1016/j.jqsrt.2009.11.004, 2010.Nelson, R. R., O'Dell, C. W., Taylor, T. E., Mandrake, L., and Smyth, M.: The potential of clear-sky carbon dioxide satellite retrievals, Atmos. Meas. Tech., 9, 1671–1684, 10.5194/amt-9-1671-2016, 2016.O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, 10.5194/amt-5-99-2012, 2012.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.Parker, R., Boesch, H., Cogan, A., Fraser, A., Feng, L., Palmer, P. I., Messerschmidt, J., Deutscher, N., Griffith, D. W., Notholt, J., and Wennberg, P. O.: Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground-based TCCON data and model calculations, Geophys. Res. Lett., 38, L15807, 10.1029/2011GL047871, 2011.Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Con- way, T. J., Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A. I., Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O., Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American carbon dioxide ex- change: CarbonTracker, P. Natl. Acad. Sci. USA, 104, 18925–18930, 10.1073/pnas.0708986104, 2007.Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Rozanov, V., Bovensmann, H., and Burrows, J. P.: A fast atmospheric trace gas retrieval for hyperspectral instruments approximating multiple scattering – Part 1: Radiative transfer and a potential OCO-2 XCO2 retrieval setup, Remote Sens., 9, 1159, 10.3390/rs9111159, 2017. Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, R., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, 2011.Roche, S., Strong, K., Wunch, D., Mendonca, J., Sweeney, C., Baier, B., Biraud, S. C., Laughner, J. L., Toon, G. C., and Connor, B. J.: Retrieval of atmospheric CO2 vertical profiles from ground-based near-infrared spectra, Atmos. Meas. Tech., 14, 3087–3118, 10.5194/amt-14-3087-2021, 2021. Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific, Singapore, 2000.Sander, S. P. and Pongetti, T. J.: CLARS-FTS data, available at: https://megacities.jpl.nasa.gov, last
access: 12 December 2020.
Seinfeld, J. and Pandis, S.: Atmospheric chemistry and physics: from air pollution to
climate change, Wiley, Inc., New Jersey, USA, p. 1224, 2006. Sen, B., Toon, G. C., Blavier, J.-F., Fleming, E. L., and Jackman, C. H.: Balloon-borne observations of mid-latitude fluorine abundance, J. Geophys. Res., 101, 9045–9054, 1996.Somkuti, P., Boesch, H., Natraj, V., and Kopparla, P.: Application of a PCA-based fast radiative transfer model to XCO2 retrievals in the shortwave infrared, J. Geophys. Res.-Atmos., 122, 10477–10496, 2017. Spurr, R.: LIDORT and VLIDORT: Linearized pseudo-spherical scalar and vector discrete ordinate radiative transfer models for use in remote sensing retrieval problems, in: Light Scattering Reviews 3, 229–275, Springer, Berlin, Heidelberg, 2008.Spurr, R. and Natraj, V.: A linearized two-stream radia- tive transfer code for fast
approximation of multiple- scatter fields, J. Quant. Spectrosc. Ra., 112, 2630–2637,
10.1016/j.jqsrt.2011.06.014, 2011.Spurr, R., Natraj, V., Lerot, C., Van Roozendael, M., and Loyola, D.: Linearization of the principal component analysis method for radiative transfer acceleration: Application to retrieval algorithms and sensitivity studies, J. Quant. Spectrosc. Ra., 125, 1–17, 10.1016/j.jqsrt.2013.04.002, 2013.Stutz, J.: AERONET data at the Caltech site, available at:
https://aeronet.gsfc.nasa.gov/new_web/photo_db_v3/CalTech.html, last access: 28 September 2021.Toon, G. C.: Solar line list for GGG2014, TCCON data archive, 10.14291/tccon.ggg2014.solar.R0/1221658, 2014.Verhulst, K. R., Karion, A., Kim, J., Salameh, P. K., Keeling, R. F., Newman, S., Miller, J., Sloop, C., Pongetti, T., Rao, P., Wong, C., Hopkins, F. M., Yadav, V., Weiss, R. F., Duren, R. M., and Miller, C. E.: Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project – Part 1: calibration, urban enhancements, and uncertainty estimates, Atmos. Chem. Phys., 17, 8313–8341, 10.5194/acp-17-8313-2017, 2017.Wang, S., van der A, R. J., Stammes, P., Wang, W., Zhang, P., Lu, N., Zhang, X., Bi, Y., Wang, P., and Fang, L.: Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements, Remote Sens., 12, 2204, 10.3390/rs12142204, 2020.Ware, J., Kort, E. A. , DeCola, P., and Duren, R.: Mini Micropulse LiDAR (MiniMPL) data at Caltech site, available at: https://megacities.jpl.nasa.gov/portal/, last access: 12 December 2020.Washenfelder, R. A., Toon, G. C., Blavier, J. F., Yang, Z., Allen, N. T., Wennberg, P. O., Vay, S. A., Matross, D. M., and Daube, B. C.: Carbon dioxide column abundances at the Wisconsin Tall Tower site, J. Geophys. Res.-Atmos., 111, D22305, 10.1029/2006JD007154, 2006.Wong, C. K., Pongetti, T. J., Oda, T., Rao, P., Gurney, K. R., Newman, S., Duren, R. M.,
Miller, C. E., Yung, Y. L., and Sander, S. P.: Monthly trends of methane emissions in
Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations, Atmos. Chem.
Phys., 16, 13121–13130, 10.5194/acp-16-13121-2016, 2016.Wong, K. W., Fu, D., Pongetti, T. J., Newman, S., Kort, E. A., Duren, R., Hsu, Y.-K., Miller, C. E., Yung, Y. L., and Sander, S. P.: Mapping CH4 : CO2 ratios in Los Angeles with CLARS-FTS from Mount Wilson, California, Atmos. Chem. Phys., 15, 241–252, 10.5194/acp-15-241-2015, 2015.Wunch, D., Toon, G. C., Blavier, J. F., Washenfelder, R. A., Notholt, J., Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The Total Carbon Column Observing Network, Philos. T. Roy. Soc. A, 369, 2087–2112, 10.1098/rsta.2010.0240, 2011.Wunch, D., Toon, G. C., Sherlock, V., Deutscher, N. M., Liu, C., Feist, D. G., and Wennberg, P. O.: Documentation for the 2014 TCCON Data Release, CaltechDATA, 10.14291/TCCON.GGG2014.DOCUMENTATION.R0/1221662, 2015.Yang, D., Boesch, H., Liu, Y., Somkuti, P., Cai, Z., Chen, X., Di Noia, A., Lin, C., Lu, N., Lyu, D., and Parker, R. J.: Toward High Precision XCO2 Retrievals from TanSat Observations: Retrieval Improvement and Validation against TCCON Measurements, J. Geophys. Res.-Atmos., 125, p.e2020JD032794, 2020.Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A., Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P. O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R., Ahonen, P., Heikkinen, P., Kyrö, E., Mendonca, J., Strong, K., Hase, F., Dohe, S., and Yokota, T.: Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas. Tech., 6, 1533–1547, 10.5194/amt-6-1533-2013, 2013.Zeng, Z.-C.: CLARS-FTS XCH4 and XCO2 retrievals (June 2013–May 2014)
from GFIT3 algorithm (Version 1.0), CaltechDATA [data set],
10.22002/D1.1948, 2021.Zeng, Z.-C. and Sander, S.: CLARS-FTS XCH4 and XCO2 retrievals (June 2013–May 2014) from GFIT3 algorithm (1.0), CaltechDATA [data set], 10.22002/D1.1948, 2021.Zeng, Z.-C., Zhang, Q., Natraj, V., Margolis, J. S., Shia, R.-L., Newman, S., Fu, D., Pongetti, T. J., Wong, K. W., Sander, S. P., Wennberg, P. O., and Yung, Y. L.: Aerosol scattering effects on water vapor retrievals over the Los Angeles Basin, Atmos. Chem. Phys., 17, 2495–2508, 10.5194/acp-17-2495-2017, 2017.Zeng, Z.-C., Natraj, V., Xu, F., Pongetti, T. J., Shia, R.-L., Kort, E. A., Toon, G. C., Sander, S. P., and Yung, Y. L.: Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption, Geophys. Res. Lett., 45, 10772–10780 10.1029/2018GL079286, 2018.Zeng, Z.-C., Chen, S., Natraj, V., Le, T., Xu, F., Merrelli, A., Crisp, D., Sander, S. P., and Yung, Y. L.: Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements, Remote Sens. Environ., 236, 111494, 10.1016/j.rse.2019.111494, 2020a.Zeng, Z.-C., Wang, Y., Pongetti, T., Gong, F.-Y., Newman, S., Li, Y., Natraj, V., Shia, R.-L., Yung, Y. L., and Sander, S. P.: Tracking the Atmospheric Pulse of a North American Megacity from a Mountaintop Remote Sensing Observatory, Remote Sens. Environ., 248, 112000, 10.1016/j.rse.2020.112000, 2020b.Zeng, Z.-C., Xu, F., Natraj, V., Pongetti, T. J., Shia, R.-L., Zhang, Q., Sander, S. P., and Yung, Y. L.: Remote sensing of angular-dependent scattering of aerosols in a North American megacity, Remote Sens. Environ., 242, 111760, 10.1016/j.rse.2020.111760, 2020c.