AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-3111-2018Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurementsXCO2 retrieval from OCO-2 using RemoTeCWuLianghail.wu@sron.nlhttps://orcid.org/0000-0001-7797-1719HasekampOttoHuHailiLandgrafJochenButzAndrehttps://orcid.org/0000-0003-0593-1608aan de BrughJoostAbenIlsePollardDave F.https://orcid.org/0000-0001-9923-2984GriffithDavid W. T.https://orcid.org/0000-0002-7986-1924FeistDietrich G.https://orcid.org/0000-0002-5890-6687KoshelevDmitryHaseFrankToonGeoffrey C.OhyamaHirofumihttps://orcid.org/0000-0003-2109-9874MorinoIsamuhttps://orcid.org/0000-0003-2720-1569NotholtJustusShiomiKeiIraciLaurahttps://orcid.org/0000-0002-2859-5259SchneiderMatthiashttps://orcid.org/0000-0001-8452-0035de MazièreMartineSussmannRalfKiviRigelhttps://orcid.org/0000-0001-8828-2759WarnekeThorstenGooTae-YoungTéYaoSRON Netherlands Institute for Space Research, Utrecht, the NetherlandsInstitute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Wessling-Oberpfaffenhofen, GermanyMeteorologisches Institut, Ludwig-Maximilians-Universität (LMU), Munich, GermanyNational Institute of Water and Atmospheric Research (NIWA), Lauder, New ZealandUniversity of Wollongong, Wollongong, AustraliaMax Planck Institute for Biogeochemistry, Jena, GermanyLERMA-IPSL, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Observatoire de Paris, PSL Research University, 75005, Paris, FranceKarlsruhe Institute of Technology (KIT), IMK-ASF, Karlsruhe, GermanyJet Propulsion Laboratory, California Institute of Technology, Pasadena, USANational Institute for Environmental Studies (NIES), Tsukuba, JapanUniversity of Bremen, Bremen, GermanyJapan Aerospace Exploration Agency, Tsukuba, JapanNASA Ames Research Center, Moffett Field, USARoyal Belgian Institute for Space Aeronomy, Brussels, BelgiumKarlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Garmisch Partenkirchen, GermanyFinnish Meteorological Institute, Sodankylä, FinlandNational Institute of Meteorological Research, Seoul, Republic of KoreaLianghai Wu (l.wu@sron.nl)30May20181153111313021November20173May20181May201830January2018This 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/11/3111/2018/amt-11-3111-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/3111/2018/amt-11-3111-2018.pdf
In this study we present the retrieval of the column-averaged dry air mole
fraction of carbon dioxide (XCO2) from the Orbiting Carbon
Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm,
previously successfully applied to retrieval of greenhouse gas concentration
from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2
product has been validated with collocated ground-based measurements from the
Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2
data from September 2014 to July 2016. We found that fitting an additive
radiometric offset in all three spectral bands of OCO-2 significantly
improved the retrieval. Based on a small correlation of the XCO2
error over land with goodness of fit, we applied an a posteriori bias
correction to our OCO-2 retrievals. In overpass averaged results,
XCO2 retrievals have an SD of ∼1.30ppm and
a station-to-station variability of ∼0.40ppm among collocated
TCCON sites. The seasonal relative accuracy (SRA) has a value of
0.52 ppm. The validation shows relatively larger difference with
TCCON over high-latitude areas and some specific regions like Japan.
Introduction
Carbon dioxide (CO2) concentration is rapidly
increasing in the atmosphere due to fossil fuel combustion and
deforestation . This can lead to significant
changes in climate . Any
mitigation strategy to reduce CO2 in the atmosphere requires a better
understanding of the global carbon cycle, especially identifying carbon
dioxide emissions from both natural and anthropogenic sources and sinks that
absorb carbon dioxide. Our ability to quantify sources and sinks of
CO2 is still insufficient due to the sparseness of current
ground-based stations .
To get a better understanding of the spatial and temporal pattern of sources
and sinks of CO2, efforts have been made to retrieve
XCO2 from satellite observations. The thermal infrared
observations of CO2 from instruments like the Atmospheric Infrared
Sounder (AIRS), the Tropospheric Emission Spectrometer (TES) and the Infrared
Atmospheric Sounding Interferometer (IASI) can provide CO2
measurements at altitudes between 5 and
15 km. These measurements have a limited sensitivity to
CO2 in the lower troposphere where CO2 sources and sinks are
located. Satellite observations measuring in the shortwave infrared (SWIR)
spectral range, however, are sensitive to CO2 down to the Earth's
surface in the absence of clouds and so this spectral range is used to
measure XCO2 by several space missions. The SCanning Imaging
Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), operational
between 2003 and 2012, is the pioneering instrument measuring
XCO2 from the SWIR spectra with sensitivity in the boundary
layer . showed that accurate
XCO2 can be inferred from SCIAMACHY observations, taking
atmospheric scattering processes into account in the retrieval. The
Greenhouse Gases Observing Satellite (GOSAT), in orbit since January 2009, is
the first satellite primarily dedicated to monitor global atmospheric levels
of CO2 and CH4 from space . The
XCO2 derived from GOSAT has an accuracy on the order of a few
tens of a percent .
XCO2 retrievals with this level of accuracy can provide valuable
information on the variation of CO2. In
July 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite was
successfully launched. OCO-2 is designed with three standard observational
modes (nadir, glint and target) for accurate monitoring of the geographic
distribution of carbon dioxide sources and sinks on a regional
scale . By taking advantage of the target mode where many
observations are acquired over ground-based validation sites, the biases in
the XCO2 retrievals from OCO-2 measurements can be accurately
evaluated. Furthermore, with a spatial sampling size of about
3 km2, the number of cloud-free XCO2 OCO-2
observations exceeds significantly those of previous missions.
One of the main challenges of XCO2 retrieval from SWIR satellite
measurements is to characterize the light path through the atmosphere
affected by atmospheric scattering and surface
reflection . For this purpose, current missions include
measurements in the near-infrared (NIR) spectral range covering the
O2 A absorption band. Measurements in the NIR and SWIR spectral bands
allow for the simultaneous retrieval of carbon dioxide concentration with
proper accounting of scattering properties introduced by aerosols or clouds.
Several algorithms have been developed to retrieve CO2 from NIR and SWIR
measurements from space, including the differential optical absorption
spectroscopy (DOAS) retrieval method developed for the retrieval of
SCIAMACHY , the
algorithm developed at the National Institute for Environment Studies (NIES)
for GOSAT observations , the Atmospheric
CO2 Observations from Space (ACOS) retrieval algorithm developed for
the OCO instrument and later applied to the GOSAT and OCO-2
observations , the algorithm developed in
the University of Leicester (UoL)
and the RemoTeC algorithm developed by SRON Netherlands Institute for Space
Research and Deutsches Zentrum für Luft- und Raumfahrt e.V.
(DLR) .
The operational XCO2 data product of the OCO-2 mission is
derived with the ACOS algorithm and validated against ground-based
measurements and a dataset is available for
assessing regional-scale sources and sinks . To
enhance the reliability and confidence of the data product, however,
analyzing the data with independent algorithms is essential. For example, in
the greenhouse gas project of ESA's Climate Change Initiative (GHG-CCI)
extensive comparisons were made between different XCO2 retrieval
algorithms, which showed similar results when comparing with Total Carbon Column Observing Network (TCCON) data.
However, in other regions the differences were sometimes significantly
larger . In this paper, we adapt and apply the RemoTeC
retrieval algorithm, previously applied to the GOSAT measurements, to OCO-2
measurements obtained under nadir, glint and target modes and evaluate the
XCO2 retrieval data quality with collocated ground-based
measurements from the TCCON . To screen out too challenging soundings
(i.e.,
clouds, high aerosol loading, large spectral uncertainties) we optimized the
a posteriori data filtering and developed an XCO2 bias
correction based on goodness of fit. We expect that application of RemoTeC to
OCO-2 data will lead to a better understanding to the capabilities and
limitations of the OCO-2 instrument and the operational level-2 data product.
Furthermore, we see this work as a first step towards processing a larger
data set with RemoTeC.
The paper is organized as follows: Sect. 2 describes the OCO-2 data and
Sect. 3 introduces the RemoTeC full physics retrieval algorithm including
cloud screening and adjustments specific to OCO-2 type of measurements. In
Sect. 4, we evaluate our retrieval results using collocated TCCON
measurements. Here, the effect of bias correction is also discussed. To
further evaluate the RemoTeC/OCO-2 retrievals, Sect. 5 discusses the TCCON
validation of XCO2 data product from ACOS/OCO-2 and
RemoTeC/GOSAT retrievals. Finally, Sect. 6 concludes the paper.
Data
The OCO-2 satellite provides measurements of sunlight backscattered by the
Earth's surface and atmosphere in three channels including the molecular
oxygen (O2) A band (around 0.765 µm, NIR), a weak
CO2 band (around 1.61 µm, SWIR-1) and a strong
CO2 band (around 2.06 µm, SWIR-2) with a spectral
resolution of ∼0.042, ∼0.076 and ∼ 0.097 nm, respectively, defined as the full width at half maximum (FWHM)
of the instrument spectral response. Each FWHM is over-sampled by a factor of 2
to 3 in the direction of dispersion. In each band, a linear polarizer is
mounted in front of the imaging spectrometer and selects polarization vector
parallel to the entrance slit. During operation, OCO-2 can collect
observations with high signal-to-noise ratios under nadir, glint and target
modes and each sounding provides measurements in eight footprints adjacent to
each other. The typical size of one footprint is around 1.3km×2.25km under the nadir observation mode and can be a bit
larger for the other modes .
In this study, we use version 7 OCO-2 data for the period September 2014 to
July 2016. These data include observations obtained under nadir, glint and
target observation modes. A few percent of the pixels of the OCO-2 detectors
show performance anomalies and so we exclude the
corresponding spectral samples using the mask information provided in the L1b
files. Finally, only spectra are processed where at least half of the
spectral samples pass this quality check.
For validation purpose, we focus on satellite observations that are
collocated with measurements from the TCCON, which is a global network of
ground-based instruments that can measure XCO2 in the
atmosphere . The XCO2 measured by the TCCON
has an uncertainty better than 0.25 % (∼1ppm) . More information on TCCON sites
including locations and operational status can be found at
https://tccon-wiki.caltech.edu/. The collocation criteria between OCO-2
measurements and TCCON measurements include a geographical distance less than
5∘ in both latitude and longitude and a time difference less than
2 h. Due to the high spatial sampling of OCO-2 (24 spectra per second over
the swath), there are generally more than 150 cloud-screened spectra
available for each collocated TCCON measurement. In this case, we use
a maximum of 150 nadir or glint spectra, which are spatially closest to TCCON
site, while for target observations we select those obtained with a viewing
zenith angle smaller than 30∘. This viewing zenith angle
restriction has only been applied for target observations for time
efficiency.
In addition to the OCO-2 spectra, the retrieval algorithm requires
information on vertical profiles of pressure, temperature, humidity and
surface wind speed, which are interpolated from the ECMWF (European Centre
for Medium-Range Weather Forecasts) high-resolution 10-day forecast analysis
data on a 0.125∘×0.125∘ latitude × longitude
grid. The interpolation is performed with linear interpolation in time and
nearest neighbor in space. The surface elevation information of the OCO-2
footprint is extracted from the 90 m digital elevation data of NASA's
Shuttle Radar Topography Mission (SRTM) . For each
OCO-2 footprint, all SRTM grid points within the boundary are collected to
get mean surface elevation and its variation. We extrapolate the lowest ECMWF
pressure point to the surface elevation provided by the SRTM data using the
hypsometric equation. To provide the algorithm initial guess of the
CO2 vertical concentration profiles and the CH4 total column
at each location, we use data from CarbonTracker and TM5 model for the year
2013 and 2010 ,
respectively. The high-resolution solar irradiance data by Dr. R. Kurucz
(http://kurucz.harvard.edu/sun/irradiance2008/) is used as reference
solar spectrum in the forward radiative transfer model.
Method
The RemoTeC algorithm has been described in detail by and and has been applied for CO2 and CH4 retrievals from GOSAT measurements .
For OCO-2 application, the two most important algorithmic modifications are (1)
a vector radiative transfer model (LINTRAN V2) employed in the retrieval scheme and
(2) aerosol scattering effects taken into account for ocean glint retrievals.
In the following, we assume that the OCO-2 radiance measurements y, comprising of measurements in all three bands, can be described by a forward radiative transfer model F via
y=F(x,b)+e.
Here, x is the state vector containing all parameters to be retrieved
and b includes a set of auxiliary input parameters. The error term
e contains uncertainties in both instrument and forward model. To
infer XCO2, RemoTeC resolves Eq. () with respect to
the state vector x.
The OCO-2 instrument measures the backscattered sunlight in a single
polarization direction, and so the forward model for spectral sampling i
reads
Fi(x,b)=m11Ii+m12Qi+m13Ui,
where Ii, Qi and Ui are the first three Stokes parameters of
a line-by-line top of the model atmosphere spectrum convolved with the OCO-2
instrument spectral response function. The elements of the Mueller matrix
m11, m12 and m13 describe the instrument polarization
sensitivity depending on the illumination and observing geometries of the
OCO-2 instrument. For the simulation of the line-by-line spectra, we employ
the LINTRAN vector radiative transfer model . To simulate efficiently the
spectral dependence of the Stokes parameter I, Q and U, defined at the
top of the model atmosphere, the multiple scattering calculations are
performed following the k-binning approach of while
the single scattering is calculated line by line. In the algorithm, the model
atmosphere is divided into 36 sub-layers for the radiative transfer
calculation and further divided into 72 sub-layers for absorption
cross-section calculation, which is highly dependent on temperature and
pressure.
Since the measurement y does not contain sufficient information to
retrieve all elements of state vector x, the algorithm employs
a Phillips–Tikhonov regularization scheme to solve the minimization problem
iteratively :
x^=minxSy-12F(x)-y2+γWx-xa2,
where Sy is the diagonal measurement error covariance matrix that
contains the measurement error estimates of OCO-2, xa is a prior
state vector, γ is the regularization parameter and W is the
weighting matrix making the side constraint dimensionless. The value for
γ is fixed such that the degree of freedom for signal (DFS) for the
carbon dioxide profile is in the range 1.0–1.5. To avoid diverging
retrievals, following a Gauss–Newton scheme
a filter factor (Λ=11+ξ,ξ≥0) is also introduced to
limit the update of the state vector per iteration step. More details on this
aspect of the RemoTeC implementation can be found in .
The retrieval is considered successful when the following conditions are all met:
(1) the update of the state vector x becomes smaller than its
theoretical uncertainty; (2) the step-size parameter ξ has reached 0; and (3)
the state vector elements have never reached unrealistic values during the
iteration.
The forward model assumes the land surface reflection to be Lambertian,
whereas ocean surface reflection is modeled using a wind-speed-dependent
Cox-and-Munk reflection model with a wavelength-dependent Lambertian term added to it. Oxygen absorption lines in the A band
are calculated by a spectroscopic model that accounts for line mixing and
collision-induced absorption processes . Absorption
lines of CO2 are modeled accordingly to the HITRAN 2008 spectroscopic
database, by taking line mixing into account . HITRAN 2008 is also used to model absorption lines of
CH4 and H2O assuming a Voigt lineshape model. In the
retrieval, we treat aerosol as spherical particles with a constant refractive
index (1.400–0.003i) over the whole OCO-2 spectral range. The aerosol
size distribution is described by a power-law function (n(r)∝r-αs) with size parameter αs while the aerosol height
profile is assumed to be Gaussian with a central height parameter zs and
a fixed geometric width of 2 km. Based on this aerosol model, we
calculate the optical properties of aerosol particles using the tabulated
kernels of .
In the retrieval, the state vector x includes the 12-layer profile of
CO2 sub-column number densities along with total column number
densities of interfering absorbers CH4 and H2O and surface
parameters, including a second-order spectral dependence of the Lambertian
surface albedo in all OCO-2 bands. Moreover, x contains the aerosol
size parameter αs of the power-law distribution, the total column
density of aerosol particles and the central height parameter zs of the
Gaussian height distribution. Finally, in all three bands we infer an
intensity offset, a first-order spectral shift of the Earth radiance spectrum
and a spectral shift of the solar reference spectrum. To initialize the
retrieval, we choose an aerosol total column, which corresponds to an aerosol
optical depths of 0.1 in the NIR spectral band, a size parameter
αs=4.5 and an aerosol layer height zs=3000m.
Table lists the state vector elements and prior values, if
applicable, considered in the retrieval. After convergence, the spectral fit
residuals are generally less than 1.0 % with a typical reduced chi
squared distribution of 3.0.
State vector elements and their a priori values considered in the
retrieval.
State vector elementA prioriUnitTwelve CO2 sub-columnsCarbonTracker 2013moleccm-2H2O total columnECMWFmoleccm-2CH4 total columnsTM5moleccm-2Three parameters of the Lambertian surface albedo, NIREstimated from mean radiance–Addictive intensity offset, NIR0.0Ph s-1m-2sr-1µm-1Zero-order spectral shift, NIR0.0–Solar reference spectral shift, NIR0.0–Three parameters of the Lambertian surface albedo, SWIR-1Estimated from mean radiance–Addictive intensity offset, SWIR-10.0Ph s-1m-2sr-1µm-1Zero-order spectral shift, SWIR-10.0–Solar reference spectral shift, SWIR-10.0–Three parameters of the Lambertian surface albedo, SWIR-2Estimated from mean radiance–Addictive intensity offset, SWIR-20.0Ph s-1m-2sr-1µm-1Zero-order spectral shift, SWIR-20.0–Solar reference spectral shift, SWIR-20.0–Aerosol size parameter4.5µmAerosol column6.84×1012m-2Aerosol layer central height3000m
Since clouds are not considered in RemoTeC, a cloud screening of the OCO-2
data is required before performing full physical retrieval. For this purpose,
we implemented a fast non-scattering retrieval as part of the RemoTeC and
compare columns of O2, CO2 and H2O, which are
retrieved independently from the NIR, SWIR-1 and SWIR-2 bands of OCO-2,
respectively. When neglecting cloud and aerosol scattering a large deviation
can be introduced between CO2 and H2O columns retrieved from
SWIR-1 and SWIR-2 bands due to different light path sensitivity. Similarly,
for scenes with larger photon path-length modification, the retrieved
O2 column will deviate more from the O2 column provided by
the ECMWF. Cloud filtering is performed by applying the following criteria:
0.885<O2ret/O2ecmwf<1.020,
0.990<CO2swir1/CO2swir2<1.035 and
0.950<H2Oswir1/H2Oswir2<1.060. Here,
around 30 % of total soundings are identified as cloud-free cases by the
cloud screening. If estimated separately, the percentage of clear soundings
are 24, 28 and 34 % for target, land and ocean glint observations,
respectively. For now, we mainly use those ratios as a option to filter cloud-contaminated cases for the full physical retrieval. Apart from cloud
screening, observations with solar zenith angle >70∘ and large
surface roughness (SD of surface elevation >75m) are also
excluded before performing the operational retrievals.
Time variation of XCO2
retrievals from OCO-2 observations over land (red dots) and ocean (blue
pentagon) and collocated TCCON measurements (black square) for Lamont and
Darwin stations. SD of individual TCCON measurement and satellite retrievals
are presented with the length of bar. In each subplot, the mean bias (b)
and SD (σ) of the difference between RemoTeC/OCO-2 retrievals and
TCCON measurements and site location in latitude and longitude are included.
The shown results here are bias-corrected data.
Validation of individual XCO2
retrieved from OCO-2 measurements with collocated TCCON data before bias
correction. Here, target retrievals are separated intentionally from land
retrievals results, which thus include only measurements obtained under nadir
and glint modes. Ocean retrievals only include glint mode observations over
ocean. For retrievals collocated with multiple TCCON stations, we use data
from the closest station. The bias (b), sounding precision (σ), number
of points (N), the Pearson correlation coefficient (cor) and one-to-one
line are included. Different colors represent the frequency of point
occurrence.
Estimated swath-dependent biases using Target mode
observations.
Error on XCO2 retrievals as a function of
the goodness of fit in the NIR, SWIR-1 and SWIR-2 bands. Different colors
represent the frequency of point occurrence. The dashed line is a linear
regression fit to the data.
Validation with the TCCON
In this section, we evaluate the XCO2 retrieved from OCO-2
measurements using the RemoTeC algorithm against ground-based measurements at
a comprehensive set of TCCON stations. Figure
shows an example of validation between RemoTeC/OCO-2 retrievals and TCCON
measurements over Lamont and Darwin stations. Here, we can see that
RemoTeC/OCO-2 retrievals can capture well the seasonal XCO2
variation features of both the Northern Hemisphere and Southern Hemisphere. To
better evaluate our retrieval quality, we use the bias (b) as the mean
difference between collocated TCCON and OCO-2 retrievals, the sounding
precision (σ) as the SD of the difference and the station-to-station
variability (σs) as the SD of the biases for different TCCON
stations. Here, retrievals over land and ocean are evaluated separately. The
separation is due to the fact that land and ocean surface reflections are
modeled differently. Land retrievals include observations obtained under
nadir and glint modes and ocean retrievals only include observations under
glint mode. Target mode observations, mostly performed coincidentally around
TCCON sites over land, are evaluated separately. Moreover, the SD over all
seasonal bias results, known as seasonal relative accuracy (SRA) as
introduced by , is also derived for all three types of
retrievals. The SRA value is a good indicator of the variability of the bias
in both space and time. In the following validation, we assume that TCCON
measurements themselves are consistent over all stations with
a station-to-station variability of zero. However, as discussed
by and , individual stations
have a year-to-year variability of ∼0.3ppm and the overall
TCCON XCO2 uncertainty is around 0.4 ppm (1 sigma).
Although some limitations may exist, TCCON measurements are the most
appropriate validation data product for satellite observations. Here, we
exclude stations located either close to source region such as Caltech or at
very high latitude such as Eureka. Land retrievals obtained over Réunion, located within areas with significant topography and an active
volcano, will also not be used for validation.
Settings of the filters used for excluding RemoTeC/OCO-2
XCO2 retrievals. The sign “–” indicates using the same option
as in land retrievals.
ParameterDefinitionAllowed range LandOceanszaSolar zenith angleval≤70∘–vzaViewing zenith angleval≤45∘–iterNumber of retrieval iterationsval≤30–dfsDegrees of freedom for signal for CO2val≥1.0–χ2Overall goodness of fitval≤7.0–χ1st2Goodness of fit in O2 A bandval≤7.0–Blended albedo*2.4× albedo_NIR – 1.13× albedo_SWIR-2val≤0.9Nonealb2Added Lambertian term in SWIR-2 bandNoneval≤0.065sevSurface elevation variationval≤75mNoneαsAerosol size parameter3.5≤val≤8.03.5≤val≤5.5τ0.765Aerosol optical depth in O2 A bandval≤0.35val≤0.55Aerosol ratio parameterτ0.765*zs/αs, zs is aerosol layer heightval≤300m–XerrRetrieval uncertainty for XCO2val≤2.0ppm–IOFF1Fitted intensity offset ratio in NIR band-0.005≤val≤0.015–IOFF2Fitted intensity offset ratio in SWIR-1 band-0.001≤val≤0.015–IOFF3Fitted intensity offset ratio in SWIR-2 band-0.001≤val≤0.015–
* The blended albedo filter was first introduced
in .
Filters and bias correction
We first compare our retrieval results with collocated TCCON data to
establish a set of values for the filters shown in Table to
screen out retrievals with larger uncertainties. In our retrieval, around 83,
81 and 72 % of cloud-free cases successfully converge and, after applying
the filters in Table , 66, 50 and 47 % of retrievals
remain with good quality in cloud-screened target, land and ocean types of
measurements, respectively. The overall L2-processed throughput is around
15 %. When estimated separately, the percentages are 15.8, 14.0 and
16.0 % for target, land and ocean soundings, respectively.
Similar to the work of and , we apply filters
to reject retrievals with bad quality of fit (χ2>7.0,
χ1st2>7.0 or not converged with number of iterations >30),
with high aerosol loading (τ0.765>0.35), with extreme aerosol
parameters (αs<3.5, αs>8.0 or aerosol ratio parameter
>300m), or with surface types like snow or ice (blended albedo
>0.9). Here, the reduced χ2 is defined as
1/N∑i=1Ny(i)-F(i)δi2,
in which N is the number of measurements minus the DFS, y(i) is the OCO-2 measurement, F(i) is the simulated result
and δi is the uncertainty of the OCO-2 measurements. In OCO-2
retrievals, intensity offset parameters are fitted for all the three spectral
windows and we use the ratio between retrieved intensity offset and mean
spectral radiance to filter out soundings with larger spectral uncertainties.
Here, target retrievals have the same filter settings as land retrievals.
Validation of averaged XCO2 retrieved
from OCO-2 target measurements with collocated TCCON data. The retrieval
results shown here are overpass averages of single soundings per station
within 2 h. The SD of individual TCCON data and that of RemoTeC/OCO-2
retrievals are presented with error bars. The bias (ba), SD
(σa), number of points (N), the Pearson correlation
coefficient (cor) and one-to-one line are included.
Same as Fig. , but for OCO-2 land type
measurements obtained under nadir and glint modes.
Ocean glint measurements require different filter settings because of their
different sensitivity due to unique viewing geometry and different surface
properties. Moreover, in the measured radiance of ocean glint measurements,
the contribution from aerosol scattering is negligible when compared with
that from ocean surface reflection. As a consequence, the measured radiances
are mainly sensitive to ocean reflection and aerosol layer extinction
properties. Aerosol filter settings used here are different from land
retrievals due to the limitation of aerosol information and aerosol
parameters like particle size and layer height usually retain their prior
values.
Same as Fig. , but for OCO-2 ocean type
measurements obtained under glint mode.
The dependence of the bias between RemoTeC/OCO-2
target XCO2 retrievals coincident with TCCON data on the
latitude of each station. Shown are the averaged results for bias-corrected
XCO2 retrievals. Stations with less than five collocation points
(marked with red pentagon) should be interpreted with care and are therefore
excluded from the calculation of the derived parameters including mean bias
(ba‾) and the station-to-station variability
(σs). For each station, number of valid points (N), bias (b) and
SD (σ) are listed next to the dot point. The size of each dot
represents the SD of the difference at each station.
Same as Fig. , but for OCO-2 land
type measurements obtained under nadir and glint modes.
Same as Fig. , but for OCO-2
ocean type measurements obtained under glint mode.
When comparing individual retrieval results with collocated TCCON
measurements, we look for possible corrections of errors with instrumental,
geophysical, meteorological and retrieved parameters. This correction should
be valid for each single sounding and thus evaluated with individual results.
In this paper, a positive bias means XCO2 is overestimated by
the RemoTeC/OCO-2 retrievals. Figure shows that only
a small overall bias of 0.31, 0.37 and 0.70 ppm exist in the
RemoTeC/OCO-2 retrievals for target, land and ocean types of retrievals,
respectively. Here, the dependency of difference between OCO-2 and TCCON with
collocation distance and surface pressure is negligible. However, if we look
at retrievals from eight individual footprints within a swath separately, the
XCO2 retrievals show statistically significant differences on
overall biases ranging from -0.25 to 0.65 ppm with an SD of
0.3 ppm. These biases arise from uncertainties in the L1 processing
depending on the viewing direction in across-flight direction and have to be
removed before performing an overall bias correction. To identify the
footprint-dependent biases, we use target mode observations when all eight
footprints in one sounding frame converged, which provides around 7000
available retrievals per footprint. By using a large amount of target
observations we can reduce the uncertainties in the footprint-to-footprint
bias estimation. Here, we assume a constant XCO2 field in the across-track direction. The estimated swath-dependent biases, as shown in
Fig. , are directly subtracted from each footprint.
Time variation of XCO2 difference
between retrievals from OCO-2 observations over land (red dots) and ocean
(blue pentagon) and collocated TCCON measurements for each TCCON station. SD
of individual TCCON measurement and satellite retrievals are presented with
the length of bar. In each subplot, the overall bias (b), SD (σ) and
site location in latitude and longitude are included. The shown results here
are bias-corrected data used in Table . An second-order
polynomial (blue dot lines) is fitted for distinguishing the time-variation
of biases.
Density distributions of the XCO2 differences between
OCO-2 land retrievals and collocated TCCON data for two different retrieval
settings. In the black solid line we fit intensity offsets in all three OCO-2
bands while in the red dashed line we only fit the intensity offset in
O2 A band. Here we only do algorithm convergence filtering for both
and take the intersection of them for fair comparison. The bias b and
sounding precision σ for each retrieval are included.
Variation of fitted intensity offset with respect to mean signals
measured in each OCO-2 band for observations over land. Linear regression fit
for the intensity offset (IOFF) and noise equivalent radiance (NEN) is
overplotted along with fitted coefficients on top right. Different colors
represent the frequency of point occurrence.
After removing the swath-dependent biases, a bias dependence on the χ2
in SWIR-1 band is found for RemoTeC/OCO-2 retrievals over land. As shown in
Fig. , a typical χ2 in SWIR-1 band is around 2.0 and the
correlation coefficient is 0.20. Here, χ2 in SWIR-1 and SWIR-2 bands
are highly correlated with corresponding retrieved surface albedos.
A possible explanation for the correlation between bias and χ2 is that
χ2 correlates with surface brightness. For bright surfaces, the noise
becomes small and some “constant” fit residuals show up. This “constant”
fit residuals can be attributed to many factors like spectroscopic errors,
inconsistent aerosol assumptions and instrument or algorithm uncertainties.
Here, we correct this bias by
XCO2corr=XCO2d+k⋅χswir12,
where the coefficients k=-0.001261 and d=1.001938 are derived with
a linear regression fit through the difference between individual retrievals
and TCCON measurements. This correction reduces the error correlation with
most parameters in Table such as overall χ2, surface
albedo in the NIR band (albedo_NIR), solar zenith angle and DFS, even though these parameters are not used in the bias
correction and the remaining correlations with related parameters are
generally less or around 0.15. After applying this bias correction the
swath-dependent biases remains low around -0.1ppm with an SD of
0.01 ppm. Similar improvement can be achieved by using χ2 in
SWIR-2 band in the bias correction.
For ocean glint retrievals, we only subtract the swath-dependent bias and
a constant bias of 0.65 ppm from the XCO2 results. The
constant bias is obtained by validating retrieval results with collocated
TCCON measurements from sites as listed in Fig. . The
XCO2 swath-dependent bias for ocean glint observations is very
similar to the one of XCO2 target observations and so the same
correction is applied.
Overall, with the bias correction in Eq. () the sounding
precisions σ are slightly improved by ∼0.1ppm for land
retrievals in Fig. . The land and ocean bias corrections
are developed for reducing globally relevant biases and thus geographically
related or time-dependent biases may remain in the results and need further
investigation.
TCCON validation
For a detailed validation of the bias-corrected XCO2 product, we
will evaluate the XCO2 retrieved from OCO-2 target, land and
ocean measurements using the RemoTeC algorithm for different TCCON stations
separately. The average of the retrieved XCO2 is compared with
the corresponding TCCON average values. We exclude cases where less than five
individual data points are available within 2 h in either OCO-2 retrievals
or TCCON data. To evaluate the retrieval quality, we take into account the
bias (ba), SD (σa), station-to-station
variability (σs) and SRA against TCCON
measurements station by station. Here, the station-to-station variability is
an important evaluation parameter known as a measure of regional-scale
accuracy, which is most important for estimating CO2
surface-to-atmosphere fluxes on regional scales. The SRA value further
indicates the potential bias variation in both space and time. Moreover, we
study the effect of the bias correction by analyzing the retrieval
performance station by station.
Figures , and show the overall
comparisons between RemoTeC/OCO-2 retrievals after bias correction and TCCON
measurements for target, land and ocean retrievals, respectively. In the
overpass averaged results, the bias and SD (ba,
σa) are (-0.07, 1.24), (0.00, 1.36) and (0.00,
1.20) ppm for target, land and ocean retrievals, respectively. Before
bias correction, the mean biases are 0.51, 0.44 and 0.75 ppm for the
above three type of retrievals, respectively. The bias correction mainly
improves the mean bias though the SDs are also reduced by
∼0.1ppm for land retrievals.
Figures , and show the bias
(ba) at each TCCON site as a function of its latitude for the
target, land and ocean types of retrievals. The mean
(ba‾) and the SD (σs) of all the biases are
also derived. Stations with less than five valid points have been excluded from
the analysis. The number of stations used in the validation are 10, 17 and 18
for target, land and ocean retrievals, respectively. Within those stations,
most of them have a bias less than 0.5 ppm for both land and ocean
retrievals.
In Fig. , the remaining XCO2 bias for target
observations varies from -0.81ppm (Tsukuba, Japan) to
0.47 ppm (Lauder, New Zealand). The developed bias correction reduces
the station-to-station variability from 0.54 to 0.35 ppm. The effect
of the bias correction is largest for Lamont, Dryden and Darwin stations
(>0.50ppm on the mean station bias) while in other stations the
difference is small. This happens because the goodness of fit is highly
correlated with surface albedo and thus make the corrections apparently to
regions with large albedos. Land retrievals as shown in Fig. ,
validated among 17 stations, have a station-to-station variability of
0.41 ppm. The remaining bias varies from -0.66ppm (Lamont,
OK, USA) to 1.03 ppm (Sodankylä, Finland). Here, most stations have
similar biases as found for the corresponding target observations. The bias
correction also helps to reduce the station-to-station variability for land
retrievals although not that much. Among all the stations, Tsukuba station in
Japan has relatively larger SD of 2.07 ppm. For retrievals in
Figs. and , there is a tendency for
validations over stations in higher-latitude areas have relatively larger
biases in both the northern and southern hemispheres. In addition, target
observations have a smaller station-to-station variability than land
observations although different TCCON stations are involved.
For ocean retrievals, since we only subtract swath-dependent bias and a mean
bias, the station-to-station variability (0.44 ppm) is the same
before and after bias correction. The biases vary from -0.86ppm
(Saga, Japan) to 0.75 ppm (Bremen, Germany). There is no clear
indication of latitude-dependent bias variation.
Moreover, we investigated temporal variations in RemoTeC/OCO-2
XCO2 retrievals. As shown in Fig. ,
seasonal XCO2 variation features in the Northern Hemisphere can
be captured well by both RemoTeC/OCO-2 retrievals and TCCON measurements. At
the Southern Hemisphere, the XCO2 is more stable throughout the
whole time range. Figure shows the time series of
XCO2 difference between TCCON measurements and XCO2
retrievals from OCO-2 target, land and ocean types of measurements. At most
stations, no time-dependent biases can be clearly observed. For some stations
in the Northern Hemisphere like Sodankylä, Bremen and Paris, time-dependent
features can also be attributed to inhomogeneous seasonal data distribution.
There are some outliers in XCO2 retrievals from both land and
ocean glint observations, such as those at the Tsukuba over land and Lauder
over ocean, that need further investigation.
Bias between XCO2 retrieval from RemoTeC/OCO-2,
including target, land and ocean retrievals, and TCCON data at individual
stations in four different time intervals of a calendar year (Q1:
1 January–31 March; Q2: 1 April–30 June; Q3: 1 July–30 September; Q4:
1 October–31 December). For each time interval, we only use data from
stations with more than five collocated points. In each table cell, bias, SD and
number of points are included and those with larger standard error
(σ/N>0.5ppm) after bias correction will also be
neglected as done by . For stations with all four seasonal
biases, the SD of these four biases (“Seas”) are also calculated. This
parameter is an indicator of their seasonal variability.
StationsQ1Q2Q3Q4SeasReferenceSodankylä, Finland (67.3∘ N, 26.6∘ E)–0.70(1.49, 39)1.18(1.28, 30)––Białystok, Poland (53.2∘ N, 23.0∘ E)-0.34(1.34, 14)0.02(1.31, 40)0.62(1.60, 25)0.02(0.93, 7)0.34Bremen, Germany (53.1∘ N, 8.8∘ E)–-0.04(0.95, 7)1.04(1.20, 14)––Karlsruhe, Germany (49.1∘ N, 8.4∘ E)–-0.16(1.37, 25)0.09(1.75, 24)0.59(0.75, 6)–Park Falls, WI (USA) (48.4∘ N, 2.3∘ E)-0.14(1.16, 17)-0.37(1.53, 38)0.10(1.52, 46)-0.44(1.27, 20)0.21Paris, France (48.4∘ N, 2.3∘ E)–-0.15(1.10, 11)0.33(1.44, 19)––Izana, Tenerife (48.4∘ N, 2.3∘ E)-0.24(0.73, 7)––––Orléans, France (47.9∘ N, 2.1∘ E)0.36(1.01, 19)0.34(1.04, 34)0.32(1.81, 25)0.98(1.47, 15)0.28Garmisch, Germany (47.4∘ N, 11.0∘ E)-0.04(1.47, 15)-0.49(1.56, 28)0.02(1.34, 23)––Rikubetsu, Japan (43.4∘ N, 143.7∘ E)-1.21(1.64, 11)-0.13(1.64, 13)0.81(1.03, 6)-0.24(1.04, 7)0.71Lamont, OK (USA) (36.6∘ N, 97.4∘ W)-0.71(1.06, 55)-0.35(1.01, 53)-0.51(1.29, 59)-1.00(0.83, 49)0.24Anmyeondo, South Korea (36.5∘ N, 126.3∘ E)-0.26(0.58, 5)–0.67(0.85, 7)––Tsukuba, Japan (36.0∘ N, 140.1∘ E)-1.31(1.18, 26)0.07(1.17, 12)–-1.00(1.17, 29)–Dryden, USA (34.9∘ N, 117.8∘ W)0.10(1.08, 40)0.85(0.99, 59)0.55(1.56, 48)0.16(1.24, 39)0.30Saga, Japan (33.2∘ N, 130.2∘ E)-1.24(0.80, 14)-0.93(1.05, 27)-0.32(1.86, 24)-0.19(1.33, 23)0.43Ascension Island (7.9165∘ S, 14.3325∘ W)0.19(1.03, 12)0.07(0.92, 18)-0.04(0.99, 14)-0.12(0.99, 23)0.12Darwin, Australia (12.4∘ S, 130.9∘ E)-0.21(0.88, 55)0.01(0.71, 61)0.38(0.58, 49)0.04(0.81, 66)0.21Réunion (20.901∘ S, 55.485∘ E)0.10(0.69, 9)-0.23(0.75, 17)0.12(0.61, 25)0.50(0.73, 19)0.26Wollongong, Australia (34.4∘ S, 150.8∘ E)0.04(0.98, 41)0.26(0.93, 17)0.21(1.18, 26)0.19(0.76, 37)0.08Lauder, New Zealand (45.0∘ S, 169.6∘ E)0.19(0.99, 29)0.53(0.67, 10)0.13(0.92, 8)0.31(0.97, 37)0.15ALL0.520.420.430.54–SRASRA = 0.52
Finally, we check the SRA, which is derived
for all three types of observations. For each station, all the data
regardless of the year are sorted into four intervals of a calendar year.
Table summarizes seasonal bias per station, SD of biases
per season, seasonal variability (“Seas”) and the SRA value. The derived
SRA of 0.52 ppm is close to the requirement of 0.50 ppm as
discussed by . Here, the developed bias correction helps
to improve the SRA from 0.60 to 0.52 ppm. In stations where seasonal
variability can be calculated, the value is generally around 0.30 ppm
except stations Rikubetsu (0.71 ppm) and Saga (0.43 ppm) in
Japan. In Table the SRA values are mainly driven by large
negative biases from Rikubetsu, Tsukuba and Saga stations in Japan. Further
investigations are needed to diagnose the remaining larger biases in certain
season over stations in Japan.
Importance of intensity offset
As mentioned in Sect. , the implementation of the RemoTeC
algorithm, used in this study, fits an intensity offset for all three OCO-2
bands. In previous GOSAT retrievals we only fit intensity offset for the NIR
band. To identify its importance, we performed the same retrieval as in
Fig. but without fitting intensity offset in the SWIR-1 and
SWIR-2 bands. Figure shows that without fitting
intensity offsets in the SWIR-1 and SWIR-2 bands the validation exhibits
a negative bias of -2.95ppm and the SD increased by
∼0.5ppm.
As shown in Fig. , in the SWIR-1 and SWIR-2 bands
the fitted intensity offsets are proportional to the mean radiance with
a slope of 0.0025 and 0.0035, respectively. This slope is about two times
larger than that of noise. Generally, the fitted intensity offset in these
two bands are ∼0.4 % of the corresponding mean radiance. There are
no clear time-dependent features in the fitted intensity offset. The
intensity offset in the O2 A band shows a less strong dependence on
the signal level itself. Here, it could be partly introduced by light
reflection by degraded anti-reflection coating on the focal plane
array . However, this can not explain the amount of
intensity offset retrieved in our algorithm for the SWIR-1 and SWIR-2 bands
since for those channels much thicker and higher index anti-reflection
coatings are used . Potential causes could be
stray light from reflection of nearby ground pixels or from components of the
optical system.
Discussion
As we mentioned before, OCO-2 level-2 products delivered by the ACOS
retrieval algorithm are also validated with collocated TCCON data
by . Before comparing our results with the
results in , we need to point out several
differences between the validation approach by and
our study. (1) The considered time range of the study
by is from September 2014 to January 2017. (2)
A collocation criterion of 5∘ in latitude and 10∘ in
longitude is applied for most stations but, for Caltech and Dryden and those
located on the Southern Hemisphere, a specific local collocation criterion is
employed. (3) Daily median values of both OCO-2 retrievals and TCCON are used
for comparison. (4) Observations over land under nadir and glint modes are
validated separately. (5) The employed filter settings and bias corrections
are also different from here.
For bias-corrected data, albeit with so many differences, we still see a lot
of common aspects when looking at the SD and station-to-station variability
in . For example, for the results under warn
level 11 the best 50 % of the total L2 data; seefor more details
on warn level the SD of the difference (OCO-2-TCCON)
for land retrievals is around 1.3 ppm. Looking at station-to-station
variability for ACOS land retrievals, the σs is
∼0.45ppm over 12 stations. For ocean glint retrievals, the
σs is 0.46 ppm over 9 stations. These values are more or less
the same, albeit a bit higher, as what we see in the validation in
Figs. and .
In order to perform a more direct comparison between ACOS and RemoTeC, we
took the common data points that passed quality filtering for both algorithms
for the period under consideration of this study. In total, we collect
34 560 individual retrievals collocated with 18 TCCON stations. These
retrievals only take 31.0% of total land retrievals in
Fig. , which means we have quite different data
coverage with ACOS/OCO-2. As expected, after bias correction, the results
between ACOS and RemoTeC are similar with a bias, SD and station-to-station
bias of -0.02, 1.36 and 0.44 ppm in RemoTeC and 0.13, 1.31 and
0.55 ppm in ACOS. However, when looking at the results before bias
correction the differences are larger. Before bias correction, the overall
bias and SD are 0.58 and 1.62 ppm in RemoTeC/OCO-2 retrievals and
-1.38 and 1.89 ppm in ACOS/OCO-2 retrievals. When looking at
overpass averaging results, in total 646 cases, RemoTeC/OCO-2 retrievals have
a mean bias of 0.67 ppm with an SD of 1.43 ppm, while
ACOS/OCO-2 retrievals have a mean bias of -1.04ppm with an SD of
1.53 ppm. The station-to-station biases are 0.47 and 0.63 ppm
for RemoTeC/OCO-2 and ACOS/OCO-2, respectively. The ACOS product depends much
more on bias correction than RemoTeC. Possible reasons are the zero-level
offset fits in RemoTeC that are not performed by ACOS or the difference in
the treatment of aerosols.
XCO2 retrievals from GOSAT measurements using the RemoTeC
algorithm have also been validated with TCCON data . There are several
improvements on the RemoTeC/GOSAT XCO2 retrieval quality since
the first report by . Here we will use the latest results
over land reported by . It should be noted that there
are quite a few differences between RemoTeC/GOSAT and RemoTeC/OCO-2 including
instrument polarization sensitivity, collocation criteria, filtering options
and so on. In the validation between RemoTeC/GOSAT XCO2
retrievals and TCCON data, the sounding precision is 1.9 ppm with
a station-to-station variability (estimated over 12 stations) of
0.43 ppm. The derived SRA is 0.51 ppm. Looking at those
overall statistic values, there are no significant differences between
XCO2 retrievals from RemoTeC/OCO-2 and RemoTeC/GOSAT. Before
bias correction, the major difference between RemoTeC/OCO-2 and RemoTeC/GOSAT
is the overall bias (0.35 ppm vs. -2.25). However, further
investigation is needed to identify the difference between XCO2
retrievals from those two satellites, especially over regions where TCCON
data are not available.
Conclusions
In this paper, we extended and adapted the full physics retrieval algorithm
RemoTeC, previously applied to GOSAT, for OCO-2 satellite measurements. The
algorithm was applied to OCO-2 nadir, glint and target observations obtained
over land and ocean (glint only). We defined both an a posteriori data
filtering approach and bias correction as a function of the swath position by
comparing with TCCON. Additionally, we introduced a linear bias correction
for land observations as a function of the spectral fit quality. Comparison
of the retrieved XCO2 with collocated ground-based TCCON
stations showed that for both land and ocean observations our retrieval
results exhibit a residual bias less than 0.10 ppm with an SD around
1.30 ppm (for overpass means) and a station-to-station variability
variation around 0.40 ppm. Among the individual TCCON stations, the
biases are generally less than 0.50 ppm. In land retrievals, middle-
to high-latitude areas have relatively larger biases and in ocean retrievals
no latitude-dependent bias can be clearly seen. The target observations have
a station-to-station variability around 0.35 ppm which approaches the
systematic error required for regional CO2 source–sink
determination . The better comparison with TCCON for target mode retrievals compared to
regular land retrievals could be attributed to the fact that under the target
mode the OCO-2 satellite is directly looking at the place where TCCON sites
are located and this provides a better collocation and therefore prevents
apparent biases caused by local XCO2 variations. Time series
validation indicates that RemoTeC/OCO-2 retrieval results can capture well
the seasonal cycle of XCO2 in both hemispheres and no
time-dependent bias can be clearly observed in the retrieval. The SRA investigated over 66 time intervals of collocated stations
has a value of 0.52 ppm. Most of stations have a seasonal variability
around 0.30 ppm except for those in Japan. For the XCO2
retrieval from OCO-2 measurements, we see that intensity offsets need to be
fitted for all three bands otherwise a larger bias (2.50 ppm) and SD
(0.50 ppm) would be introduced in the results.
The OCO-2 data (version 7) used here were produced by the
OCO-2 project at the Jet Propulsion Laboratory, California Institute of
Technology, and obtained from the OCO-2 data archive maintained at the NASA
Goddard Earth Science Data and Information Services Center
(https://daac.gsfc.nasa.gov/). TCCON data were obtained from the TCCON Data
Archive, hosted by the Carbon Dioxide Information Analysis Center (CDIAC)
at that time. Since October 2017, the TCCON Data Archive is
hosted by CaltechDATA, California Institute of Technology, CA (US),
https://doi.org/10.14291/tccon.archive/1348407. The RemoTeC/OCO-2
XCO2 retrievals used in this paper are available upon request
from Lianghai Wu (l.wu@sron.nl).
The Supplement related to this article is available online at https://doi.org/10.5194/amt-11-3111-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was funded by the Netherlands Space Office as part of the User
Support Programme Space Research under project ALW-GO/15-23. We are grateful
to Université Pierre et Marie Curie, the French space agency CNES and
Région ĺle-de-France for their financial contributions as well as to
Institut Pierre-Simon Laplace for support and
facilities. Edited by: Joanna
Joiner Reviewed by: Christopher O'Dell and two anonymous
referees
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