AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-3527-2016Assessment of errors and biases in retrievals of XCO2, XCH4,
XCO, and XN2O from a 0.5 cm-1 resolution solar-viewing
spectrometerHedeliusJacob K.jhedeliu@caltech.eduhttps://orcid.org/0000-0003-2025-7519ViatteCamilleWunchDebrahttps://orcid.org/0000-0002-4924-0377RoehlColeen M.https://orcid.org/0000-0001-5383-8462ToonGeoffrey C.ChenJiahttps://orcid.org/0000-0002-6350-6610JonesTaylorWofsySteven C.FranklinJonathan E.ParkerHarrisonDubeyManvendra K.https://orcid.org/0000-0002-3492-790XWennbergPaul O.https://orcid.org/0000-0002-6126-3854Division of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, CA, USADivision of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USASchool of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USADepartment of Physics & Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, CanadaEarth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USAnow at: Department of Physics, University of Toronto, Toronto, Ontario, Canadanow at: Electrical and Computer Engineering, Technische Universität München, Munich, Germanynow at: School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USAJacob K. Hedelius (jhedeliu@caltech.edu)3August201698352735465February20164March201624June201628June2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/3527/2016/amt-9-3527-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/3527/2016/amt-9-3527-2016.pdf
Bruker™ EM27/SUN instruments are
commercial mobile solar-viewing near-IR spectrometers. They show promise for
expanding the global density of atmospheric column measurements of greenhouse
gases and are being marketed for such applications. They have been shown to
measure the same variations of atmospheric gases within a day as the high-resolution spectrometers of the Total Carbon Column Observing Network
(TCCON). However, there is little known about the long-term precision and
uncertainty budgets of EM27/SUN measurements. In this study, which includes a
comparison of 186 measurement days spanning 11 months, we note that
atmospheric variations of Xgas within a single day are well
captured by these low-resolution instruments, but over several months, the
measurements drift noticeably. We present comparisons between EM27/SUN
instruments and the TCCON using GGG as the retrieval algorithm. In addition,
we perform several tests to evaluate the robustness of the performance and
determine the largest sources of errors from these spectrometers. We include
comparisons of XCO2, XCH4, XCO, and
XN2O. Specifically we note EM27/SUN biases for January 2015 of
0.03, 0.75, -0.12, and 2.43 % for XCO2, XCH4,
XCO, and XN2O respectively, with 1σ running
precisions of 0.08 and 0.06 % for XCO2 and XCH4
from measurements in Pasadena. We also identify significant error caused by
nonlinear sensitivity when using an extended spectral range detector used to
measure CO and N2O.
Introduction
Measurements of atmospheric mixing ratios of greenhouse gases (GHGs),
including CO2 and CH4, are needed to aid in estimating fluxes and
flux changes, and to ensure international treaties to reduce emissions are
fulfilled. The Total Carbon Column Observing Network (TCCON) makes daytime
column measurements of these gases. The Orbiting Carbon Observatory2
(OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) missions enable
column GHG measurements with global coverage. These GHG monitoring
satellites make measurements at one time of day and, therefore, lack the
temporal resolution that a dedicated ground site provides.
Due to cost, lack of infrastructure, and stringent network requirements,
there are limited ground sites on a global scale; e.g., there are no TCCON
sites currently in operation in continental Africa, South America, or central Asia
(Wunch et al., 2015), and there currently is no urban area with more than
one TCCON site. Cheaper, portable, solar-viewing Fourier
transform spectrometers (FTSs) can make contributions in these settings
provided they have long-term precision. The Bruker Optics™ EM27/SUN,
with the “SUN” indicating a built-in solar tracker, is a transportable FTS
that may supplement global GHG measurements made by current networks (Gisi
et al., 2012). This unit is small and stable enough to easily be transported
for field campaign measurements, including measurements at multiple
locations in 1 day. Column-averaged dry-air mole fractions (DMFs) of gases
(Xgas) are retrieved from the EM27/SUN measurement, like the
TCCON. Xgas is calculated from (Wunch
et al., 2010):
Xgas=columngascolumndryair=0.2095columngascolumnO2,
where the 0.2095 factor is the fraction of dry air that is oxygen.
Retrieved Xgas has been compared with a co-located TCCON site in
Karlsruhe, Germany, in past work for 26 days of XCO2 retrievals
from one EM27/SUN instrument (Gisi et al., 2012), and 6 days of both
XCO2 and XCH4 retrievals from five EM27/SUN instruments (Frey et
al., 2015).
Operators of these instruments have different end goals to better understand
the carbon cycle. XCO2 and XCH4 retrievals from these instruments
have been compared with satellite measurements in areas without a TCCON site
(Klappenbach et al., 2015) as well as with satellite measurements in highly
polluted areas (Shiomi et al., 2015). Emission flux estimates from the
Berlin area (< 30 × 30 km2) were made by combining
upwind/downwind measurements from five spectrometers and were compared with
a simulation (Hase et al., 2015). Chen et al. (2016) have assessed gradient
strengths around a large dairy farm (∼ 100 000 cows) in Chino,
California (< 12 × 12 km2), using measurements from
upwind/downwind spectrometers. Weather Research and Forecast Large-Eddy
Simulations (WRF-LES, 4 km resolution) were used in combination with four simultaneous measurements to estimate fluxes from specific grid boxes in a
subregion of the Chino dairy farm area, which is within a larger urban area
(Viatte et al., 2016).
The column measurements used in these studies provide some advantages over
in situ measurements, including less sensitivity to vertical exchange,
surface dynamics, and small-scale emissions (McKain et al., 2012), which are
difficult to model. Though column measurements can depend on mixed layer height in highly polluted areas, generally, column measurements depend
primarily on regional-scale meteorology, and regional fluxes (Wunch et al.,
2011b; McKain et al., 2012). For example, Lindenmaier et al. (2014) used
observations from a single TCCON site to verify 1 day of emissions from
coal power plants of about 2000 MW each at ∼ 4 and 12 km
away. Because of their large spatial sensitivity, column measurements are
well suited for estimation of net emissions, model comparison, and satellite
validation. A single site has been used to estimate Los Angeles, California
(L.A.), emissions based on a sufficiently accurate emissions inventory and
the observation that Xgas anomalies within L.A. are highly correlated
(Wunch et al., 2009, 2016). Generally though, a single column
measurement site is insufficient to estimate emissions from an entire urban
region (Kort et al., 2013). However, multiple column measurements can be
combined to characterize part or all of an urban area (Hase et al., 2015;
Chen et al., 2016; Viatte et al., 2016).
The main goal of this work is to quantitatively evaluate the robustness of
EM27/SUN retrievals over a long period of time. This is accomplished by
comparing retrievals from the EM27/SUN with a co-located standard (TCCON
site) at Caltech, in Pasadena, California, United States. TCCON
spectrometers make the same type of measurements (direct solar
near-infrared) at high spectral resolution. Here we report XCO2,
XCH4, XCO, and XN2O comparison measurements from an EM27/SUN.
The XCO and XN2O measurements were made possible by a detector
with an extended spectral range provided by Bruker™. The EM27/SUN
XCO2 and XCH4 to TCCON comparison is the longest to date, 186
measurement days spanning 11 months. In part of January 2015, an additional three
EM27/SUN instruments were at Caltech for 9 to 12 days of XCO2 and
XCH4 comparisons to assess their relative biases. In Sect. 2 we
briefly describe differences in instruments and the data acquisition
process. In Sect. 3 we describe the retrieval software. In Sect. 4 we
describe the inherent properties of EM27/SUNs such as instrument line shapes
(ILSs), frequency shifts, ghosts, detector linearity, and external mirror
degradation. Section 5 focuses on biases and sounding precision of different
gases compared with the TCCON. Section 6 describes sources of instrumental
error. We conclude with general recommendations of tests to perform on any
new type of direct solar near-infrared (IR) instrument used to retrieve
abundances of atmospheric constituents.
InstrumentationTCCON IFS 125HR
All TCCON sites employ the high-resolution Bruker Optics™ IR FT
spectrometer (IFS) 125HR that has been described in detail elsewhere
(Washenfelder et al., 2006; Wunch et al., 2011b). For the Caltech TCCON site
(34.1362∘ N, 118.1269∘ W, 237 m a.s.l.), the IFS 125HR
uses an extended InGaAs (indium gallium arsenide) detector, covering
3800–11 000 cm-1 for detection and retrieval of all gases relevant to
this study (O2, CO2, CH4, CO, and N2O). Figure 1 has
example spectra from IFS 125HR and EM27/SUN instruments, with the spectral
regions where individual gases are retrieved highlighted. Oxygen (O2)
abundance is useful in calculating the DMF because it represents the column
of dry air and is combined with the column of the gas of interest to yield
the DMF (Wunch et al., 2010).
Example of scaled spectra from three different detector types, with
retrieval windows highlighted. The spectrum from the EM27/SUN extended
InGaAs detector was scaled 10 times more than the spectrum from the
standard InGaAs detector.
The Caltech IFS 125HR uses a resolution of approximately 0.02 cm-1
(with a maximum optical path difference (MOPD) of 45 cm). It takes about
170 s to complete one forward/backward scan pair. TCCON sites have single
sounding 2σ uncertainties of 0.8 ppm (XCO2), 7 ppb (XCH4),
4 ppb (XCO), and 3 ppb (XN2O) (Wunch et al., 2010). TCCON data are
tied to the World Meteorological Organization (WMO) in situ trace gas
measurement scale through extensive comparisons with in situ profiles
obtained from aircraft and balloon flights. We use the TCCON as a standard
against which to compare the EM27/SUN instruments. TCCON data from this
study are publicly available from the Carbon Dioxide Information Analysis
Center (Wennberg et al., 2014).
Caltech EM27/SUN
EM27/SUN spectrometers have been described elsewhere (Gisi et al., 2012;
Frey et al., 2015; Klappenbach et al., 2015) so we focus on differences in
setup and acquisition here. The standard EM27/SUN configuration uses an
InGaAs detector sensitive to the spectral range spanning 5500–12 000 cm-1, which permits detection of O2, CO2, CH4, and
H2O (Frey et al., 2015). For this study, the Caltech EM27/SUN was
delivered with an extended-band InGaAs detector sensitive to 4000–12 000 cm-1, which allowed for additional measurements of CO and N2O
(Fig. 1). All EM27/SUN spectrometers used in this study (Sects. 2.2,
2.3) used the typical MOPD of 1.8 cm, corresponding to a
spectral resolution of 0.5 cm-1. Interferograms (ifgs) were acquired in
direct-current-coupled mode to allow post-acquisition low-pass filtering of
brightness fluctuations to reduce the impact of variable aerosol and cloud
cover effects (Keppel-Aleks et al., 2007). Ghosts were reduced as data were
acquired by employing the interpolated sampling option provided by
Bruker™ (see also Sect. 4.3). A 10 KHz laser fringe rate is
used to reduce scanner velocity deviations, and each forward/backward scan
took 11.6 s, or 5.8 s per individual measurement.
Pre-averaging and apodization effects on EM27/SUN retrievals.
Measurement compared over 1–10 July 2014. Md denotes the median. NB denotes the medium Norton–Beer apodization.
a As compared to retrievals from 1 fwd/bwd averaged non-apodized measurement averaged over same time post-retrieval. b Same apodization as standard. c Same pre-averaging as
standard.
To be more consistent with the TCCON measurements, no spectrum averaging or
interferogram apodization was applied before retrieving DMFs. We recommend
averaging only after retrievals if disc storage and processor speeds are
sufficient, so spurious data can be filtered. To test the pre- vs. post-averaging effect we used 9 retrieval days with 26 000 forward/backward
measurements and used Bruker™ OPUS software to create spectra from
ifgs. We compared retrievals from using five combined backward/forward
measurements averaged pre-retrieval with those averaged post-retrieval. We also
compared combined forward/backward measurements using a medium Norton–Beer
apodization with those using no special apodization. Results are in Table 1
and suggest that different averaging methods cause only small
inconsistencies, under ∼ 0.02 % for XCO2 and XCH4.
The EM27/SUN was placed within 5 m of the Caltech TCCON solar tracker
mirrors on the roof of the Linde+Robinson building (Hale, 1935).
Measurements started on 2 June 2014 and, for this study, we include 186 measurement days that end on 4 May 2015. About 800 000 individual EM27/SUN
measurements and 40 000 individual TCCON measurements were acquired over
this period. Of these, about 580 000 and 15 000 were considered coincident
and were not screened out by our quality control filters (QCFs). Our QCFs
were conservative, and they required signal > 30 (Sect. 4.4),
solar zenith angle (SZA) < 82∘, 370 ppm < XCO2 < 430 ppm, XCO2,error < 5 ppm,
XCO,error < 20 ppb, and XCH4,error < 0.1 ppm. Other
users may consider stricter QCFs. After averaging data into 10 min bins,
there were about 6500 binned comparison points.
LANL and Harvard EM27/SUN instruments
Three additional EM27/SUN instruments were compared with the Caltech TCCON
site in January 2015 – one owned by Los Alamos National Laboratory (LANL)
and two owned by Harvard University (HU). To be consistent, all the
acquisition and retrieval settings were the same as for the Caltech EM27/SUN.
As opposed to the Caltech EM27/SUN (also abbr. cn), the LANL (abbr. pl) and
HU instruments (abbr. ha and hb) used the original InGaAs detector type
sensitive over 5500–12 000 cm-1 (Frey et al., 2015). The LANL
instrument, however, has a different high-pass filter, allowing it to measure
up to 14 500 cm-1. This different filter is neither beneficial nor
disadvantageous to this instrument as no gas column amounts are retrieved in
that region. The LANL instrument was first used in January 2014 and has been
compared with multiple TCCON sites in the United States, including sites at
Four Corners, LANL, NASA Armstrong, Lamont, Park Falls, and multiple Caltech
comparisons (Parker et al., 2015). The HU instruments have been operational
since May 2014 and were compared against each other at Harvard before
traveling over 4100 km to Caltech. As noted by Gisi et al. (2012) and Chen
et al. (2016), the ILS of these instruments is remarkably stable considering
the long distances they traveled.
Retrieval software
SFIT (Pougatchev et al., 1995), PROFFIT (“PROFile fit”, Hase et al.,
2004), and GGG (Wunch et al., 2015) are the three widely used retrieval
algorithms to fit direct solar spectra and obtain column abundances of
atmospheric gases. PROFFIT is maintained by the Karlsruhe Institute of
Technology (KIT) and has been used to obtain DMFs from EM27/SUN instruments
as well as NDACC-IRWG sites (Gisi et al., 2012; Frey et al., 2015; Hase et
al., 2015). GGG is maintained by the Jet Propulsion Laboratory (JPL) and
has been used to obtain DMFs from other low-resolution instrument
measurements (e.g., an IFS 66, see Petri et al., 2012), in addition to being
used to retrieve DMFs from the MkIV spectrometer in balloon-borne
measurements (Toon, 1991) and for the Atmospheric Trace Molecule
Spectroscopy Experiment (ATMOS) flown on the space shuttle (Irion et al.,
2002). GGG is the retrieval algorithm used by the TCCON (Wunch et al.,
2011b). We chose to use GGG for our analysis because (1) we want to be
consistent with the TCCON for comparison and (2) the GGG software suite
containing GFIT is open-source allowing us to adapt routines if needed. We
used the GGG2014 version for retrievals (Wunch et al., 2015).
All retrievals used the same pTz and H2O modeled profiles as well as
the same a priori profiles (Wunch et al., 2015). We also used the same
meteorological surface data for retrievals from all five instruments. All
retrievals also used the same 0.2 hPa surface pressure offset. This offset
was determined by comparing measurements from the standard barometer with a
calibrated Paroscientific Inc. 765–16B Barometric Pressure Standard that has
a stated accuracy of better than 0.1 hPa.
Interferogram-to-spectrum – double-sided
TCCON uses an interferogram-to-spectrum subroutine part of GGG to
perform fast Fourier transforms (FFTs) to create spectra from ifgs (Wunch et
al., 2015). Though the Bruker™ OPUS software used to operate the
spectrometer can also perform FFTs, we again chose to use GGG to maintain
consistency. A developmental version of GGG was used, which was adapted to
also allow FFT processing on EM27/SUN interferograms. GGG splits a raw
forward/backward ifg into two different double-sided ifgs which are then
FFTed to yield two spectra. GGG also corrects source brightness fluctuations
(Keppel-Aleks et al., 2007).
EM27/SUN GGG and interferogram processing suite (EGI)
To make GGG retrievals simpler for new EM27/SUN users, an add-in software
suite (EGI) was developed at Caltech to create correctly formatted input
files. This suite is open-source and can be obtained through correspondence
to the email address listed. EGI can be run using MATLAB or Python. EGI runs
in UNIX, Mac OS, and Linux environments and runs GGG on multiple
processors. EGI centralizes settings for paths to read and write files, it
coordinates separately acquired ground weather station and GPS data with
EM27/SUN ifgs, and it optimizes processing order. It also provides some
ancillary calculations such as a spectral signal-to-noise ratio (SNR)
calculation. EGI provides a simple way to turn on and off saving of
ancillary retrieval files (i.e., spectral fits and averaging kernels). EGI
can run for instruments employing one or two detectors, such as the type
described by Hase et al. (2016). Like the GGG software suite, EGI also
includes benchmark spectra acquired under different conditions to run simple
tests on. EGI is automated, reducing the learning time as well as the amount
of user time needed to retrieve DMFs. After an initial setup, EGI will run
from ifgs to retrieved Xgas with two commands. On a computer with 1400 MHz processors the code takes ∼ 30 s per CPU to process each
interferogram from the EM27/SUN extended InGaAs detector.
Instrument characterizations and performanceInstrument line shape
Knowledge of the instrument line shape (ILS), or the observed shape of a
spectral line from a monochromatic input, is crucial in assessing instrument
performance and avoiding unknown biases in retrievals. Two parameters in the the LINEFIT algorithm (Hase et al., 1999) are used to characterize the ILS in relation to
an ideal instrument, namely the modulation efficiency (ME) and phase error
(PE). ME and PE both describe the interferogram and vary with OPD (Hase et
al., 1999; Frey et al., 2015). PE is the angle between the real and imaginary parts of the FT of the ILS (Wunch et al., 2007). PE has an ideal value of 0 radians, and indicates the degree of asymmetry in
spectral lines. ME is a measure of the normalized observed interferogram
signal compared with that of a nominal instrument with an ideal value of 1
(unitless) (Hase, 2012). At maximum OPD (MOPD), an ME < 1 causes a
broadening of the measured spectral lines, while an ME > 1 at MOPD causes a
narrowing. The ILS can be
calculated by analyzing absorption lines measured through a low-pressure gas
cell, and varies with OPD (Hase et al., 1999). Here, we use only single ME
and PE values at the MOPD (Frey et al., 2015) to describe the ILS. We
characterized the ILS for the EM27/SUN instruments using the method
described elsewhere (Frey et al., 2015; Klappenbach et al., 2015). This
method is able to characterize ME to within 0.15 % using the LINEFIT
algorithm (Hase et al., 1999), with supplemental MATLAB scripts for
automation purposes (Chen et al., 2016). ILS can affect retrieved column
values. We note that the ME at MOPD of the cn and ha instruments in Table 2
are significantly lower than those reported by KIT on campus of
∼ 0.997 (Frey et al., 2015), and post-campaign of
∼ 0.996 (Klappenbach et al., 2015).
ILS of EM27/SUN instruments.
Instrument num – ID9 January 2015 ME, PE (mrad)a28 January 2015 ME, PE (mrad)Caltech (42 – cn)0.986, 4.880.979b, 3.58LANL (34 – pl)0.999, -1.34Harvard 1 (45 – ha)0.973c, -1.99Harvard 2 (46 – hb)0.991c, 4.180.991, 4.00
Missing values indicate ILS not characterized on that day.
a Phase error values are italicized. b After realigning this instrument the ME was as high as 0.994. c As reported by Chen et al. (2016).
For this study, the ILS is used to help explain biases, to demonstrate the
stability of the instruments, and gives insight into how well the EM27/SUN
instruments are aligned and their optical aberrations. Though GGG2014
retrievals do not account for non-ideal ILS, future versions of GGG will.
For the current study, we assume that ILS impacts using PROFFIT will be
similar to impacts using GGG. This assumption will need to be tested when
GGG also can account for a non-ideal ILS. Because future GGG retrievals
will be revised using historical ILS measurements, a need remains to
monitor the ILS both for future retrievals and as an indicator if
realignment is necessary.
Frequency shifts
EM27/SUN units contain a HeNe 633 nm (15 798 cm-1) metrology
laser to sample the IR signal accurately as a function of the OPD. The laser
is not frequency-stabilized (Gisi et al., 2012). This causes apparent
spectral frequency to change with temperature as is shown in Fig. 2.
Frequency shifts are affected by changes in the input laser wavenumber,
laser alignment, and IR beam alignment. The input laser wavenumber will
affect the spacing between spectral points. Since the frequency shift is
furthest from zero for the Caltech EM27/SUN (on order of -100 ppm, in red
Fig. 2), the spectral spacing is empirically corrected in the EGI suite
based on the CO2 6220 cm-1 window frequency shifts. This made
little difference for the primary gases of interest affecting XCO2 by
0.015 % and XCH4 by -0.005 %, though it did affect XH2O by
4 %.
Frequency shifts (FS) of all four instruments vary with temperature
because the lasers are not frequency-stabilized. FS for the
CO2 6220 cm-1 window are shown. FS of the Caltech (CIT) instrument are far
from zero, so an empirical correction is made to correct the sample spacing
number. Only every 300th CIT point and every 20th LANL point is plotted for
clarity. HU EM27 1 and 2 are also referred to as ha and hb respectively
by Chen et al. (2016).
Ghosts
Ghosts are artificial spectral features linked to the aliasing of true spectral
lines that arise in FTS spectra (Learner et al., 1996). The InGaAs detectors
are optically sensitive at wavenumbers greater than half the HeNe metrology
laser frequency (7899 cm-1). To fulfill the Nyquist criterion and
prevent aliasing, the IR interferogram is sampled twice each laser
interferogram cycle, on the rising and falling edge. However, if the laser
sampling is asymmetric – for example from a faulty electronics
board – aliasing can still occur, folded across the half laser frequency
(Messerschmidt et al., 2010).
Because the asymmetry is typically small, the aliased signal, or ghost
spectrum, is small compared with the true spectrum (Dohe et al., 2013; Wunch
et al., 2015).
In EM27/SUN instruments the laser sampling error (LSE) can be minimized as
data are collected by employing the interpolated sampling option provided by
Bruker™. This resampling mode uses only the rising edge of the laser
interferogram and assumes constant velocity in between the rising edges to
interpolate the sampling (Gisi, 2014). We use a narrow band-pass filter (3 dB band width 5820–6150 cm-1) in the Caltech EM27/SUN to test for
LSE ghosts at 9800 cm-1. The ghost to parent ratio is 1.73 × 10-4 at a 10 kHz acquisition rate without the interpolated
sampling activated. This ghost is eliminated with the interpolated sampling
turned on. In actual solar tests, turning the interpolated sampling on and
off had no noticeable effect on the DMF retrievals for the Caltech EM27/SUN;
however this may not hold true for all instruments. The LSE ghost also
disappeared at an acquisition frequency of 20 kHz, and returned at higher
acquisition frequencies. We opted for the recommended 10 kHz acquisition
rate with the interpolated sampling on for all EM27/SUNs in this analysis
because other instruments may be more significantly affected by LSE ghosts.
A double-frequency ghost remains at ∼ 11 900 cm-1
from radiation passing through the interferometer twice that is much larger
than the LSE ghost, but is not in a region that will affect retrievals.
Mirror degradation and detector linearity
Solar tracking mirrors provided with the EM27/SUN instruments are
gold with a protective coating. Gold is
used because of its excellent reflectance in the near-IR and low reflectance
in the visible region (Bennett and Ashley, 1965), which allows a high signal
while reducing excess heating of the field stop and other optics. Through
extended tests, we noted the first two mirrors (gold on plated aluminum, with
a coating) degrade over time, with an e-folding degradation time of
∼ 90 days as is shown in Fig. 3. Arbitrary units (AUs) for signal are
the maximum ordinate values of the unmodified interferograms multiplied by
6450. The AUs of signal happen to be close to the spectral SNR – a scaling
factor of 1.3 applied to the arbitrary signal has an R2 of 0.63 relative
to the SNR. Cleaning helped restore some signal, but never to the original
values. The mirror change may not have restored full signal because the rest
of the optics were not cleaned at the time of the mirror change. Below the
blue 150 AU line in Fig. 3 the fitted O2 root mean square
(rms) as a percentage of the continuum level dropped 26 times faster with signal
intensity than above it. The instrument did come with an extra set of
mirrors, but because mirrors are consumable parts, it adds recurring cost and
effort to maintain these instruments long-term. After 1 year of use, the
third mirror (gold coated glass) still remains completely intact. Feist et
al. (2016) had success using steel mirrors under the very harsh conditions at
the Ascension Island TCCON site, though at a cost of 35 % reflectivity
per mirror. The JPL TCCON sites near Caltech noted no degradation on the
external gold mirrors over more than 1 year of measurements. The lack of
degradation on the third external mirror and the JPL TCCON mirrors is likely
due to differences in how the mirrors were manufactured, including how the
gold is applied to the substrate and the coatings used. Mirror degradation
has likely not been a widely reported problem for most of the EM27/SUN
community, perhaps because these instruments typically are stored indoors and
only used for a few days for campaigns (for example, Frey et al., 2015).
However, this problem may affect mirrors on other EM27/SUN instruments when
mirrors are exposed outside for extended periods of time.
Interferograms from EM27/SUN instruments are negative, with the
most negative ordinate values at ZPD and saturation occurring at -1. Here
the interferogram maximums (ifm) refer to the maximum (least negative) ordinate
values of the raw interferograms. They were normalized so the maximum is
1000 and are plotted with time showing the loss of signal. These values are
affected by clouds, which are the cause for much of the scatter. They are
also affected by SZA which explains some apparent intermediate increases.
Only every 50th point is plotted for clarity. Mirror cleaning (thin black
lines) helped restore some signal, but never to original values. The 150 AU line is in blue.
With signal loss, we would anticipate that gas measurements would become
noisier but remain unbiased. However, with time, the Caltech EM27/SUN
XCO2 and XCH4 DMFs decreased relative to the TCCON DMFs as mirror
reflectance decreased, and XCO2 and XCH4 increased when the
mirrors were replaced. The TCCON IFS 125HR InGaAs detectors are already
known to be sufficiently linear that no correction is required (Wunch et
al., 2011b). We also performed a simple test repeatedly adding mesh screens
in front of the entrance window to filter some of the light. In these tests
XCO2 and XCH4 changed on order of 3 and 0.01 ppm respectively
when using the extended InGaAs detector in the presence of filters
transmitting ∼ 25 % of the light. Figure 4 shows results
from this test on XCO2; results from XCH4 are similar. This
provides strong evidence that the extended InGaAs detector is nonlinear.
We repeated the test using the standard InGaAs detector, and changes in
XCH4 and XCO2 biases were of the order of 10 times smaller and
could be attributed to scattering off the mesh screen placed in front of the
entrance window. Figure 5 shows the difference between the EM27/SUN and
TCCON XCO2 and XCH4 as the total signal changed. After the mirrors
were changed, the relative difference actually went up with some signal loss
before decreasing again, for reasons we do not understand.
XCO2 retrievals on 11 October 2014 when mesh screens were
repeatedly moved in front of and away from the EM27/SUN (with extended
InGaAs detector) entrance window. Gray points are all EM27/SUN measurements.
Large points are 10 min averages. Error bars are 1σ. This test
was performed a few days after the mirrors were replaced.
Detector nonlinearity in FTS instruments can be corrected in the ifgs
post-acquisition in two ways. The first option deals with artifacts around the
ZPD (zero path difference point) and is already included in GGG (Keppel-Aleks et al., 2007). When the
ifg is smoothed, a nonlinear detector exhibits a dip around the ZPD which can
be used to diagnose and reduce detector nonlinearity effects. EM27/SUN
measurements are too noisy to properly characterize or detect this dip and
so this correction is insufficient. The other option is to compare detector
response with radiance from a controlled external light source, such as a
blackbody, with very accurate radiation flux measurements (on order of
0.01 %) (Thompson and Chen, 1994). By characterizing the response to the
true flux as it is varied, the detector can be characterized and ifgs can be
appropriately scaled and corrected. However, this requires extremely
controlled precise measurements, as all nonlinearity is likely less than
1 %, so measurements must be more precise than 1 %.
An option to prevent nonlinearity from interfering with measurements is to
only use the detector over its linear range by sufficiently attenuating the
incoming sunlight. However, the SNR is already low so we opted against this
method. Ultimately, we purchased the non-extended InGaAs detector at the
loss of CO, and N2O for future measurements for the Caltech instrument.
For the historical field measurements we use a bias correction to match the
TCCON for the nearest comparison days. The nonlinearity has nearly an equal
effect for short times, but has a larger variation on multi-monthly scales
as the mirrors degrade. In future measurements we recommend against using
these extended InGaAs detectors. Addition of band-pass filters or use of
different detectors will be necessary to provide high-quality measurements
of CO, CO2, and CH4 (Hase et al., 2016).
The data shown in Fig. 5 were divided into bins based on the signal
intensity and were separated before and after the mirror change. Within each
bin the relationship was treated as approximately linear. Fits using fewer
than 10 points or with correlation coefficients less than 0.1 were
discarded. The change with half signal was calculated. The analysis was
repeated for 10 bins and again for 20 bins. The weighted mean change in
XCO2 for halving the signal is -1.43 ppm in agreement with the mesh
tests or
ΔXCO2ppm-1=2.06lnS/S0,
where S and S0 are the final and initial
signals respectively. This relationship holds for S and
S0 in the middle 80 %. For a similar methane
analysis the mean change for half signal is -7.25 ppb or
ΔXCH4ppb-1=10.5lnS/S0.
(a) The XCO2 retrieved from the EM27/SUN compared to
TCCON decreased with signal intensity for the first set of mirrors. In
October the mirrors were changed, which caused the retrieved XCO2 to
increase. The inset is the legend for the average date and number of points
in the histogram bins. (b) XCH4 retrieved from the EM27/SUN compared
with TCCON.
Comparisons with Xgas
GGG2014 includes an air-mass-dependent correction factor derived for TCCON Xgas measurements. The air mass correction factor for each gas is
calculated using data obtained at a variety of relatively clean sites as
described by Wunch et al. (2011b). We expect that the air mass dependence,
which is due primarily to spectroscopic uncertainties, should be common for
the same type of measurement. Parker et al. (2015) noted that the average EM27/SUN
factors are similar compared to the TCCON for XCO2 at three clean sites in
the United States. The XCH4β factor was different (-0.0077 EM27/SUN,
0.0053 TCCON) but when applied here it worsened the R2 and standard
deviation of the comparisons. This could be because the air mass dependence
of XCH4 may not be solely from spectroscopic issues. Hence, we used the
same air-mass-dependent correction factors as the TCCON.
To compare measurements between the TCCON and the EM27/SUN instruments, data
were first averaged into 10 min bins to reduce the variance of binned
differences (Chen et al., 2016). The median of the XCO2 differences
between sequential time bins is smallest (around 0.26 ppm) for 10 min bins over the entire ∼ 11 month time period. Less averaging is
more affected by noise, and more averaging starts to include instrument
drift and true atmospheric variations. Averages were weighted using
retrieval errors x^err as in Eq. (4):
x‾^=∑ix^ix^i,err-2∑ix^i,err-2,
where x^i is the retrieved value from the ith
measurement in a bin, and x‾^ is the bin average.
Averaging kernels
When comparing retrieved Xgas measurements (also denoted c^)
from different remote sensing instruments, differences in their averaging
kernels (AKs or ai, where
i represents an instrument indicator number) and a priori profiles must be
taken into account, using for example, the methods described by Rodgers and
Connor (2003). Wunch et al. (2011a) compared GOSAT and TCCON total column
DMFs using this method. Because GGG scales a priori profiles rather than
retrieving the full profile, these AKs are vectors (i.e., column averaging
kernels) rather than matrices.
Averaging kernels depend on several factors including how strong the lines
are in the retrieval windows, and viewing geometry (e.g., SZA for
solar-viewing instruments). Because the TCCON IFS 125HR and EM27/SUN
instruments have different spectral resolutions, the apparent absorption
strengths are different and so are the averaging kernels. Averaging kernels
for a gas differ for each microwindow. We combined AKs of a given gas from
different microwindows using an unweighted average. Averaging kernels for
the Caltech EM27/SUN for the GGG retrieval windows are shown in Fig. 6.
Averaging kernels from the other EM27/SUN instruments are similar. TCCON
averaging kernels have been discussed by Wunch et al. (2011b) and are shown
on the bottom row in Fig. 6. As a numerical example, for XCO2 measured
at 50∘ SZA and 900 hPa using GGG, the AK is 1.10 for EM27/SUN
instruments and 0.93 for TCCON instruments. This means EM27/SUN instruments
are slightly more sensitive to a change in CO2 near the surface
relative to TCCON instruments. More importantly, they have the opposite
sensitivity to an error in the a priori volume mixing ratio (VMR) profile at
900 hPa.
Top row: averaging kernels from the Caltech EM27/SUN instrument.
Bottom row: averaging kernels from the TCCON.
Example spectral fits and residuals (× 20) from several
of the retrieval windows from 28 June 2014, 10:55:16 (UTC-8),
SZA = 17.1∘. The primary species of interest and root mean square
(rms) of the residuals are listed on the left. Other species fit in the
window are listed on the right.
In our particular case, reducing the smoothing error using Eq. (A13) from
Wunch (2011a) and using the a priori as the comparison ensemble changes
little as the effect of the differences in averaging kernels from the top of
the atmosphere tends to cancel out the effect of differences at the bottom.
TCCON and EM27/SUN a priori profiles were the same in this comparison.
However, we need to consider that the a priori profiles used in the
retrieval are not representative of a highly polluted place, such as
Pasadena, which is located in the same air basin as Los Angeles. Because
differences in column measurements compared to background or a priori
profiles occur primarily because of differences at the surface we can adjust
retrievals for one instrument taking into account this knowledge using
c^1=a1,sa2,sc^2-ca+ca.
Definitions of the terms in, as well as a discussion of assumptions needed
to obtain Eq. (5) are in Appendix A. We applied Eq. (5) to the XCO2 and
XCH4 retrievals.
In summary, to compare biases between two instruments, we account for
diurnal dependences, then average data into comparable time bins, and take
into account our prior knowledge of the atmospheric profile and differences
in averaging kernels.
Full comparisons of Xgas from extended-band InGaAs detector with a
TCCON site
Gisi et al. (2012) noted that measurements taken within the first 30 min
of moving the instrument to the roof and turning it on needed to be filtered
out because of high scatter while waiting for the instrument to operate
stably. We did not observe a similar requirement for our data. This could be
because our instruments were not subjected to such fast temperature changes.
It could also be because the laser frequency shift, which changes with
temperature, does not seem to significantly impact our retrievals.
Examples of spectral fits from several of the retrieval windows are shown in
Fig. 7 for a single spectrum. These are not necessarily representative of all
the conditions under which the 800 000 spectra were acquired. The
residuals are larger than those reported by Gisi et al. (2012) and Frey et al. (2015) because of the lower SNR from spectra recorded using the extended
InGaAs detector.
Italicized values in parentheses are percent standard deviations as compared
to the TCCON over the dataset for January 2015.
Full time series of EM27/SUN measurements as compared to TCCON
from June 2014 to May 2015. Thin vertical gray lines represent mirror
cleaning. The thick line represents the mirror change. To the right are
TCCON means over time to get a sense of percent deviations.
The full time series (186 days) of the difference between the Caltech
EM27/SUN and TCCON measurements is shown in Fig. 8. From this figure we see
that XCO2 and XCH4 are the gases most affected by the mirror change in
October 2014 (by about 3 ppm and 12 ppb respectively). For all gases,
scatter of retrieved Xgas increases as signal decreases. Figure 9 shows
the retrieved XCO2 and XCH4 from all four EM27/SUN instruments for
9–12 days in January 2015 plotted against those from TCCON. We report
biases for January 2015 as scaling factors to approximate to the TCCON, or
scaling factors compared to 1. Biases were calculated using a linear least
squares fit forced through the origin. A summary of the biases for all gases
as compared to the TCCON is provided in Table 3.
Retrieved EM27/SUN measurements (10 min averaging) as compared
to the TCCON from January 2015. This provides a visual representation of the
data – offset and scatter of data between Xgas from different
instrument types – in Table 3. The black dashed line is the 1:1 line.
XCO2
We note a smaller bias in XCO2 with respect to the TCCON (+0.03 %,
see Table 3) compared to previous EM27/SUN studies (Gisi et al., 2012; Frey
et al., 2015; Klappenbach et al., 2015). These previous studies retrieved
Xgas from EM27/SUN spectra using PROFFIT. When compared with the TCCON
XCO2 retrievals, Gisi et al. (2012) noted a +0.12 % bias, Frey et al. (2015) noted a +0.49 % bias, and Klappenbach et al. (2015) noted a
+0.43 % bias. Reasons for these differences could be from (1) spectroscopy differences between PROFFIT and GGG2014 used for EM27/SUN
Xgas retrievals, (2) because Gisi et al. (2012) used an earlier version
of GGG for TCCON retrievals, and (3) because Frey et al. (2015) and
Klappenbach et al. (2015) applied empirical corrections before comparing
with the TCCON. In this section, we investigate two possible causes of bias:
spectral resolution and instrument line shape.
Following Gisi et al. (2012), we attempted to determine whether the cause of
the bias is due to the difference in spectral resolution between the
EM27/SUN and TCCON instruments. Petri et al. (2012) also considered
resolution bias in their study using a 0.11 cm-1 resolution instrument
and an older version of GGG. They did not report a bias in XCO2
retrievals, but noted that XCO2 decreased by ∼ 0.12 %
as interferograms were truncated to obtain spectra with resolutions of 0.02 to 0.5 cm-1. Most of the change occurred as the resolution
changed from 0.1 to 0.5 cm-1 (see Fig. 11 therein). In
contrast, Gisi et al. (2012) noted a 0.13 % increase in XCO2 as the
resolution changed from 0.02 to 0.5 cm-1 in PROFFIT. Here we
find a 0.08 % ± 0.16 % (1σ) decrease in XCO2 when the
resolution is decreased from 0.02 to 0.49 cm-1 in GGG,
though part of this change would be offset by considering the differences in
averaging kernels.
Previous studies noted an increase in XCO2 of 0.15 % for a 1 %
increase in modulation efficiency at max OPD (Gisi et al., 2012; Frey et
al., 2015). Using PROFFIT we performed a similar test for spectra taken
under various conditions at various times of day and obtained a similar
result of a 0.10 % ± 0.02 % (1σ) increase in XCO2 for
a 1 % increase in ME at the MOPD. For this study we assume that impacts of the
ILS on retrievals will be similar in GGG and PROFFIT. Though we report a
single value, there is an air mass dependence of ∼ 0.05 %
increase in EM27/SUN PROFFIT retrievals for a 1 % increase in ME and
air mass change of 1.
For instruments using the standard InGaAs detectors, the XCO2
10 min running 1σ precision is 0.075 % [0.034 to 0.18 %, 95 %
CI]. The wide confidence interval (CI) is from a combination of atmospheric
variability being aliased into the running standard deviation as well as different SNRs among instruments.
The spectral SNRs for measurements using this detector were in the range
1000–5000 and their precision for XCO2 retrievals was only weakly
correlated with 1/SNR. Chen et al. (2016) found that the 1σ XCO2 precision among
10 min binned EM27/SUNa-EM27/SUNb differences is 0.01 %.
These data were acquired in a way that about 67 spectra were acquired every
10 min, and because two instruments were used, the single sounding
precision is ∼0.01%×67/2≈0.058%, which
falls in our measured running 1σ precision range. Comparing to the
TCCON, Gisi et al. (2012) reported that the 1σ daily precision is
0.08 %. The extended InGaAs detector naturally has a lower spectral SNR,
in the range 100–1000, with a median of 400 over the full time series. Most
of the variation in the SNR is due to loss of mirror reflectivity, but even
with non-degraded gold mirrors, it is ∼ 5 times lower because
of the different detector. The median running 1σ precision over the
full time series is 0.26 % for the XCO2 product from the
extended InGaAs detector. Because the SNR changed with time due to loss of
mirror reflectivity, so did the precision. The correlation between
1/SNR and running 1σ XCO2 precision was strong (R2=0.75)
for retrievals from this detector and followed
σXCO2=0.17+8.4SNR-57.
An additional study we have not performed that could help in reducing bias
would be to omit all or part of a CO2 window with strong water lines.
Because of the low resolution of these spectrometers (see inset Fig. 1),
water lines and CO2 lines often overlap. This can lead to
inaccurate retrievals despite a good overall fit because H2O and
CO2 can both be wrong, but in compensating ways. Reducing the size of a
window would reduce precision but would decrease water and temperature
sensitivity. This adjustment could also be performed for CH4, which is
retrieved over three windows in GGG.
XCH4
The EM27/SUN XCH4 retrievals are 0.75 % higher than those of TCCON
(see Table 3). In previous work, high biases of 0.47 % for a 0.11 cm-1 instrument (Petri et al., 2012), and 0.49 % (Frey et al., 2015)
and 1.87 % (Klappenbach et al., 2015) for EM27/SUNs, were noted. Petri et al. (2012) attributed most (0.26 %) of their bias to differences in
resolution and noted for a single day that the bias increased as resolution
decreased. In our simulations we find a 0.28 % ± 0.20 % (1σ) increase in XCH4 when the resolution is reduced from
0.02 to 0.49 cm-1. Using PROFFIT the impact of a 1 % decrease in ME is a
0.15 % ± 0.01 % (1σ) increase in XCH4. Again,
although we report a single value there is an air mass dependence of about a
0.12 % decrease in XCH4 using PROFFIT retrievals for an air mass
change of 1, and a 1 % decrease in ME. Resolution and ME combined account
for only half of the observed methane bias. Petri et al. (2012) suggested
improper dry air mixing ratio and pT profiles, or spectroscopy as sources of
error. Improper surface pressure, error in the calculated Observer-Sun
Doppler Stretch (OSDS) due to pointing errors coupled with solar rotation,
or error in the assumed field of view (FOV) may also contribute to the bias
(see Sect. 6).
Chen et al. (2016) found that the 1σ XCH4 precision among 10 min
binned EM27/SUNa-EM27/SUNb differences is 0.01 %, which is
equivalent to a single sounding 1σ precision of ∼ 0.058 %. Using the same method as for XCO2, the XCH4 running
1σ precision from instruments using the standard InGaAs detectors is
0.057 % [0.037 to 0.25 %, 95 % CI], in agreement with Chen et al. (2016). The median running 1σ precision for XCH4 from
instruments using the extended InGaAs detector is 0.33 %. XCH4
precision from the extended InGaAs measurements is also correlated with
1/SNR.
XCO and XN2O
XN2O and XCO were also measured using an EM27/SUN spectrometer in
this study. Hase et al. (2016) have also
reported on XCO measurements using an EM27/SUN modified to include a
second InGaAs detector with optical filters. Column CO measurements are
desirable because CO is a tracer of combustion. Here these measurements were made
possible because the extended detector is sensitive to the region 4200–4800 cm-1, which contains useful windows where N2O and CO molecules
absorb IR radiation. Both the XCO and XN2O retrievals are highly
sensitive to changes in the modeled temperature profile. The nonlinearity
of the detector had a less pronounced effect on XCO and XN2O
retrievals than it had on XCO2 and XCH4 retrievals (Fig. 8).
XCO and XN2O also have poorer precision than XCO2 and
XCH4, so any nonlinearity effect could be less than the noise. The
4200–4800 cm-1 spectral region is also affected differently from the
nonlinearity than the 5000–7000 cm-1 region where column CH4 and
CO2 are retrieved; the continuum levels changed more for the latter
region. This may also explain in part why there is no noticeable change in
XCO and XN2O with signal. For XCO the median 1σ
precision is 3.7 %. In our simulations reducing the spectral resolution
from the TCCON (0.02 cm-1) to near the EM27/SUN (∼ 0.5 cm-1), XCO decreases 2.5 % ± 4.2 % (1σ) in
low-resolution spectra, and at Caltech this change varies with time.
In general, as is seen in Fig. 8, XN2O retrievals were highly scattered
and had a large offset from TCCON. In our simulations, reducing the
resolution from TCCON (0.02 cm-1) to EM27/SUN (0.5 cm-1)
decreased XN2O by 1.5 % ± 0.6 % (1σ). Retrievals from
the 4430 cm-1 window were low (∼ 6 %), while the 4719 and 4395 cm-1 regions were biased slightly high
(∼ 1 %). The retrievals from the 4719 cm-1 region
additionally had some long-term trends for reasons we do not understand. For
XN2O the median 1σ precision is 1.9 %.
XH2O
Because of the significantly lower spectral resolution of the EM27/SUN
spectrometers, the spectral band widths for the H2O retrievals were
increased as compared to the standard TCCON approach (Wunch et al., 2010).
For lower resolution spectra, the H2O lines appear much broader and the
observed transmittance is much lower at the edges of standard TCCON spectral
window. Thus, the spectral ranges of the low-resolution windows were
expanded. Some of the standard TCCON windows used to retrieve H2O had
too few spectral points from the low-resolution instrument for good fits and
were omitted. When expanding the windows, we ensured that no lines were
admitted that made the effective ground-state energy E′′ greater
than ∼ 400 cm-1. This reduces the temperature sensitivity
to the modeled temperature profiles. As with the TCCON windows, we tried to
keep a wide range of H2O line strengths to accommodate large seasonal
and site-to-site variations of the H2O column. Windows were kept as
wide as possible without encountering large spectral fitting residuals.
For XH2O, we find a median 1σ precision of 1.9 % from the
instrument using the extended InGaAs detector. For instruments using the
standard-InGaAs detectors, the XH2O 1σ precision is 0.81 %
[0.36 to 2.12 %, 95 % CI].
Standard deviations and biases from using wrong model pTz and
H2O profiles as compared to using the standard option for time and
location. Tests are in order of increasing full σ. Red represents
intraday variability. Cyan represents interday variability.
Sensitivity tests on retrievals
As with the TCCON, EM27/SUN retrievals require modeled atmospheric pressure,
temperature, altitude (pTz), and water profiles (Wunch et al., 2015). Here
atmospheric profiles are generated from the NCEP/NCAR 2.5∘
reanalysis product (Kalnay et al., 1996) by interpolating to the correct
location at local noon of the desired day. These profiles also include the
tropopause height which is used to vertically shift a priori profiles, as
tropopause height can significantly affect column DMFs such as XCH4 and
XHF (Saad et al., 2014). Selecting a profile for an incorrect location
or day could lead to errors.
We ran test retrievals for the July 2014 period with incorrect profile
information derived separately at latitudes north (1, 2, and 5∘) and
longitudes west (1, 2, and 5∘) of our observation site, and well as
from profiles derived 1, 5, 10, and 100 days prior to the measurement dates.
In general, the profiles generated from a more distant location in space and
time caused larger retrieval errors. For XCH4 and XCO, the main
variability from the standard retrievals was in daily offsets (standard
deviation of daily medians σMddaily) which had values of 3 and 4 ppb
respectively for the 100 day prior model. The medians of daily standard
deviations Mdσdaily
were 0.5 ppb for both XCH4 and XCO for the 100 day prior model.
XN2O and XH2O also had more errors from σMddaily, except for profiles within 2∘, which more strongly affected diurnal
variability Mdσdaily. For these two
species, the 100 day prior model σMddaily were 2 ppb and 50 ppm and
Mdσdaily were 1 ppb and
20 ppm respectively. These values are shown for XCO2 in Fig. 10 for all
tested models. The 100 day prior model had σMddaily=0.16 ppm and
Mdσdaily=0.4 ppm, as well as a 1.2 ppm bias when using these models for XCO2.
Meteorological sensitivity tests on EM27/SUN retrievals.
XCO2XCH4XCOXN2OErrorOffsetDailyOffsetDailyOffsetDailyOffsetDaily+1 hPa surf0.0320.0040.0360.0100.100.140.060.18+10 K (surf – 700 hPa)0.2570.076-0.0060.03610.11.20.530.23
Errors expressed as percentages. Daily is the median of the daily standard
deviations, Mdσdaily.
Perturbations used in uncertainty budget.
PerturbationMagnitudeapa volume mixing ratio (VMR)downshift by 1 kmbap temperature+1 K all altitudesap pressure+1 hPa all altitudesPointing offset (po)increased by 0.05∘Surface pressure+1 hPaCalculated OSDSc+2 ppmField of view (FOV)+7 %
See also Fig. 11. a ap denotes a priori. b ap VMRs were shifted
independently. For XH2O and XHDO, concentrations were decreased by
50 % at all levels. c OSDS = observer sun Doppler stretch.
Uncertainty budget for EM27/SUN instruments using GGG2014. See
Table 5 for magnitudes of perturbations.
Tests for assessing biases and sensitivities of solar-viewing, remote sensing instruments.
AssessmentTest/observationTypeAccepted correctionaRoot causeSimilar instr. effectEM27/SUN testIncoming radiation attenuation effectGray filter after solar tracker & before interferometerMRecom'd replace detector. Alt. empiricalDetector nonlinearityConsistent for same detectorsSect. 4.4ILSMeasure with low-p gas cell (preferred), stable laser, or ambient air (least recom'd)MRetrievals with non-ideal ILSInstrument misalignment; in-builtPotentially large differencesGisi et al. (2012); Chen et al. (2014); Frey et al. (2015); Sects. 4.1 (measured), 5.3, 5.4 (impacts)Adjust FOV (if ILS is measured but not accounted for in retrieval)RIANot recom'dGhost to parent ratioUse blackbody source & narrow band filter post-interferometerMLaser mis-samplingLikely similar, potentially large diffsGisi et al. (2012); Frey et al. (2015); Sect. 4.3Ghost effectsMeasurements with & without ghost correction (e.g., XSM, or ifg resampling before FFT)bM or RIARecom'd interpol. during acq or post-resamplingLaser mis-samplingLikely similar, potentially large diffsSect. 4.3Frequency shiftsChanges or large 0 offsetO & RIAInput spectral spacingImproper laser wavenumber, misalignment of laser or NIR beamShifts differ, effect similarSect. 4.2Solar gas stretchChanges or large 0 offsetO & RIAOSDSPoor spectral fits of solar lines; SE or res.Similar for same detector & res.Sect. 6Spectral fitting windowsWidth, locationsRIAInstrument resolution requires adaptationSame for similar res. (widths) & detector (locations)Gisi et al. (2012); Sect. 5.7 (H2O) Sect. 5.3 (discussion)Averaging kernelsUsed when comparing with a different instrument typeORodgers and Connor (2003) and prior info.Diff. sensitivity at atmos. layers from differing resolutionsb & VGSame for similar res., microwindows & VGSect. 5.1SZA artifactsMulti-day measurements in clean locationOEmpiricala (Wunch et al. 2011b)ILS, or SESee ILS entryFrey et al. (2015);Parker et al. (2015)Long-term artifactsPreferred co-location with accepted measurements (e.g., TCCON)OVarious (e.g., instrument settling, changing alignment, other)May widely differHerein – for extended InGaAs onlyRegion/zone dependenceCo-location with spatially distributed accepted measurementsO/MA priori insufficienciesLikely similarParker et al. (2015)Surface pressure effectsManually adjust pressure inputs.RIAAccurate barometer pres. calibr.Poor calculation of O2 column, directly or by poor fittingSimilar effects for similar resolutionsSect. 6pTz & H2O model profile sensitivityAdjust modeled meteorological profilesRIAImprove met. profilesNon-representative pTz+H2O profileSimilar effects for similar resolutionsSect. 6A prori VMR surface sensitivityAdjust a priori VMR near surfaceRIAImprove a priori profiles; reduce effect with AKsNon-representative VMR profile (e.g., polluted mixed layer)Similar effects for similar res. & true VMR profileParker et al. (2015)Opt. avg. timeAllan type plot; e.g., Chen et al. (2016)OEmpiricalSNR & true atmospheric variationDepends on SNR & locationChen et al. (2016) Sect. 5Resolution effectsTruncate high-resolution ifgRIAApply offsetInst. res.Similar for all solar-viewing insts.Gisi et al. (2012); Petri et al. (2012) Sects. 5.3–5.6Uncertainty budget for current fitting algorithmVarious, test on each new algorithm (Wunch et al. 2015)RIAInformativeVariousSimilar effects for similar resolutionsSect. 6
M denotes measurement (setups/adjustments required before acquisition), RIA denotes retrieval
input adjustment (post-data acquisition, pre-retrieval), O denotes observation post-retrieval (may require prior planning
of locations of measurements or longer term measurements), SE denotes spectroscopy errors, VG denotes viewing
geometry, res denotes resolution. a Though empirical corrections are occasionally accepted, it is always recommended to
correct the underlying problem(s) if possible. b XSM is Bruker™ code for interpolation during acquisition. c GGG can provide ifg resampling if two detectors are on instrument. Note that the preferred correction
is always of the root cause.
Time series comparison of EM27/SUN retrievals to retrievals from
the 0.5 cm-1 resolution IFS 125HR spectra.
Various user, instrumental, and measurement errors can reduce the accuracy
and precision of retrievals. GGG uses retrieved O2 column amount with
the average DMF of O2 (0.2095) to calculate the dry pressure column of
air. However, to calculate the O2 absorption coefficients, GGG takes
into account the surface pressure, which can lead to measurement
inaccuracies if the wrong surface pressure is used. Wunch et al. (2011b)
reported a 0.04 % XCO2 bias for a +1 hPa surface pressure offset in
the TCCON. Similarly, we find a 0.032 % XCO2 bias per +1 hPa
surface pressure offset, with a 0.004 % σ variation on average
throughout a day. Because the pressure offset affects O2 retrievals,
the other species are also affected (Table 4). XCO may be particularly
affected by a pressure bias because such a large fraction of the column CO
is near the surface.
Using the same July 2014 dataset used to test the sensitivity of the
retrievals to error in the pTz profile and surface pressure, we further
estimated the sensitivity to error in the temperature in the lower
atmosphere (surface – 700 hPa). GGG uses a single temperature profile per
day that represents the local-noon temperatures, and the surface
temperature is extracted from that profile. Such temperature error can arise
in particular at the beginning and end of the day when the temperature is
typically cooler than at noon. Here we derived the sensitivity of the
retrievals to a +10 K error in the lower atmosphere (Table 4). XCO
has a significantly larger bias than the other species, likely because water
absorption lines are the strongest spectral features in the CO retrieval
window and water absorption lines are highly sensitive to changes in
temperature. Water lines are also much stronger than N2O lines in the
N2O windows. These tests suggest that offsets under 1 hPa and 1 K would
cause small (∼ 0.1 ppm) biases on XCO2, but a 4 K
difference in near-surface (ground – 700 hPa) temperature could cause
∼ 0.4 ppm bias in XCO2 which is larger than our reported
1σ precision. For other studies using multiple spectrometers and
multiple meteorological measurements for Xgas retrievals, we recommend
cross-comparing meteorological measurements to eliminate bias – preferably
to a standard.
Finally, we perform a sensitivity study following the methodology of Wunch
et al. (2015). The magnitudes of the applied perturbations are in Table 5.
The results of this uncertainty budget study are presented for a day for
XCO2 and XCH4 in Fig. 11. We do not include a sum in quadrature
because we do not have an exhaustive list of sources of uncertainty. This
uncertainty budget indicates that the low-resolution instruments are
especially sensitive to biases in a priori pressures and a priori volume
mixing ratio (VMR) profiles. Some of these errors may partially account for
the unexplained long-term drifts we noted compared to TCCON that are
unrelated to signal (e.g., Fig. 8, October–November 2014). For example, surface
pressure and calculated Observer-Sun Doppler Stretch (OSDS) were correlated
with EM27/SUN to TCCON XCO2 differences in the long-term measurement.
However, there was no apparent trend in the spectral residuals from fitting
solar lines as the OSDS changed so these correlations may not indicate
cause.
Differences in Xgas between different instruments are due to a
combination of differences in resolution, and real instrumental
imperfections and instabilities. To attempt to distinguish between
resolution causing differences (e.g., by limitations in the forward model) or
instrumental issues, we repeat the test performed by Gisi et al. (2012, Fig. 11 therein) of truncating IFS 125HR interferograms for the full time series.
Results are shown in Fig. 12. Mean values for XCO2 are slightly lower
because of differences from retrievals on spectra of different resolutions,
as described in Sect. 5.3. When comparing 10 min averaged TCCON
data with lower resolution IFS 125HR retrievals we note monthly standard
deviations on order of 0.15 % for XCO2 and XCH4. This suggests
the standard deviations of comparing retrievals from the EM27/SUN with the
TCCON (Table 3) on these timescales are close to the current precision
limits for directly comparing XCO2 and XCH4 retrieved from spectra
of these different resolutions. Results in Fig. 12 are slightly more
scattered than in Fig. 8 and have different offsets. The data still show an
increase in XCO2 and XCH4 in October–November 2014 for reasons we do not
understand, and unfortunately we have no ILS characterizations over this
period.
Long-term drifts may or may not affect instruments employing the
standard InGaAs detector and may be eliminated by future retrieval updates.
They may also arise in part from how the comparison was made, e.g., the
assumptions to derive Eq. (A4) may not be valid for CH4 and N2O. As
a follow-up study, brief 5–6 day comparisons using a standard InGaAs
detector were made for the months of August, September, and November 2015.
Scaling factors varied from 0.99905 to 1.00001 for XCO2 and from
1.01228 to 1.00893 for XCH4, with larger day-to-day variability.
Long-term (1 year or more) comparisons of these instruments employing the
standard-InGaAs detector are needed before claims of long-term accuracy can
be made or the full magnitude of drift can be quantized. Errors that could
lead to drifts likely would be correlated amongst all EM27/SUN instruments,
so the comparison would need to be against a standard such as the TCCON.
Future studies may also benefit from comparing results using different
retrieval algorithms, as the magnitude of errors that may lead to drifts in
Xgas may vary among algorithms. Meanwhile, operators have already found
many purposeful ways to use these instruments that require only short-term
(about 1 month) precision without any assumptions about precision for longer time
periods (for example Hase et al.,
2015; Chen et al., 2016; Viatte et al., 2016). Studies using these
spectrometers independently longer term can also be performed depending on
the degree of precision required. Limits on precision described herein are
likely to only improve in future work.
Conclusions
Despite the challenge associated with the extended InGaAs detector and
mirror degradation, the EM27/SUN instruments perform well on short timescales with 1σ running 10 min precisions of 0.075 % for XCO2 and 0.057 %
for XCH4 retrieved from measurements using the standard InGaAs
detectors. These instruments perform well in terms of mobility and
stability, maintaining alignment despite frequent movement and jostling – an
ideal characteristic of mobile FTS instruments. Measurements from the
standard detector are precise enough to be used for campaigns of up to a few months and to provide useful supplementary Xgas measurements to
established networks like TCCON. However, we recommend regular – 6 months to
1 year depending on use – comparison with established measurements (e.g., a
TCCON site) to account for long-term drift. The frequency of comparison with
established measurements may need to be reevaluated when more long-term
comparison data become available. Simultaneous use of several EM27/SUN
instruments may also
help characterize drift. We also recommend
regular – about monthly depending on use – ILS characterization. Our
experience also suggests that use of the extended InGaAs detector without
limiting the spectral band-pass in the EM27/SUN is incompatible with
XCO2 and XCH4 retrievals that are precise long-term.
In general, we recommend all new ground-based, solar-viewing, remote sensing
FTS instruments to undergo some or all tests listed in Table 6 to evaluate
their performance. We also recommend comparisons of retrieval outputs to
those of existing instrumentation (e.g., TCCON or NDACC-IRWG). These tests
assume that one of the three widely used and accepted retrieval algorithms
(GGG, PROFFIT, and SFIT), known to provide accurate spectral
fitting, is used. New retrieval algorithms should be subjected to additional
comparisons with currently accepted algorithms. Some of the results of these
tests will be similar across all instruments of a given type, and so do not
need to be repeated if they have been performed on another instrument
elsewhere.
Assumptions and limitations in the AK correction
To derive Eq. (5), we begin with Eq. (22) in Rodgers and Connor (2003):
c^i=ca+∑khkai,k⋅xt,k-xa,k+ϵi.
To include the pressure-weighting function h (Connor et al.,
2008), we have used summation notation. The “hat” represents a retrieved value,
c represents a column (scalar) value, and ϵ is the error.
Subscript i is for a particular instrument, subscript a represents the a
priori, subscript k is for a particular atmospheric layer, and subscript
t represents the true atmosphere. The vectors a and
x represent the column averaging kernel and atmospheric VMR
profile respectively. This equation is derived from Eq. (1) in Rodgers and
Connor (2003) using a Taylor series expansion about the a priori profile, and
assuming linearity about it.
To compare retrievals from remote sounding instruments, a comparison profile
(also called the comparison ensemble mean, denoted xc) is
used. Here, we have used the daily a priori profiles, which were the same
for all instruments, as the comparison profiles. We note, however, that the
comparison profiles should describe the real atmosphere as far as possible
(Rodgers, 2000). Though the a priori profile has a drawdown in CO2 from the
biosphere near the surface, the real atmosphere in Pasadena is polluted near
the surface. Thus this choice of comparison profiles is not ideal in our
situation.
If we ignore retrieval error Eq. (A1), and further assume that
xt=xa except at the surface, it can be rewritten
as
1ai,sc^i-ca=hsxt,s-xa,s,
where the subscript s represents a surface value. If we are comparing
measurements from two different instruments, i=1 and
i=2, in the same location, xt,s and hs are the same.
Because the a priori profiles are also the same,
1a1,sc^1-ca=1a2,sc^2-ca,
which can be rewritten as
c^1=a1,sa2,sc^2-ca+ca.
Even in the absence of error, retrievals from instruments with different
averaging kernels will still differ.
We adjust the EM27/SUN XCO2 and XCH4 retrievals using
Eq. (A4) before comparison with the TCCON, which adjusts XCO2 by up to
∼ 1.2 ppm and XCH4 by up to ∼ 8 ppb.
Future work could improve on this methodology using a better comparison
ensemble or more representative a priori profiles for retrievals from
measurements in Pasadena. This correction is not applied to XH2O
because the AKs vary more among spectra because of larger variations in
absorption strengths. It is also not applied to XCO and XN2O
because using xt=xa is too poor of an assumption
and makes the comparison worse between the TCCON and EM27/SUN retrievals in
terms of R2.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Frank Hase and Michael Gisi for helpful discussions on ghost
reduction, detector nonlinearity, and ILS measurements. We further thank
Michael Gisi and Bruker Optics™ for loaning us a standard InGaAs
detector for testing and for instructions on realigning the EM27/SUN. We
thank Dietrich Feist for discussions on mirror degradation. We also thank
Nicholas Jones, David Giffith, Frank Hase, and Sabrina Arnold for sharing
their experience with mirror degradation. This work is supported in part by
the W. M. Keck Institute for Space Studies. Jacob Hedelius was also
partially supported by a Caltech Chemistry and Chemical Engineering
Division Fellowship funded by the Dow Chemical Graduate Fellowship,
and expresses thanks to them. The authors gratefully acknowledge funding from the NASA Carbon
Cycle Science program (grant number NNX14AI60G) and the Jet Propulsion Laboratory. Manvendra K. Dubey acknowledges
funding from the NASA-CMS program for field observations and from the
LANL-LDRD for the acquisition of the LANL EM27/SUN. Jia Chen, Taylor Jones, Jonathan E. Franklin, and
Steven C. Wofsy acknowledge funding provided by NSF MRI Award 1337512.
The authors thank the referees for their comments.
Edited by: F. Hase
Reviewed by: M. K. Sha and one anonymous referee
ReferencesBennett, J. M. and Ashley, E. J.: Infrared Reflectance and Emittance of
Silver and Gold Evaporated in Ultrahigh Vacuum, Appl. Opt., 4, 221,
10.1364/AO.4.000221, 1965.Chen, J., Samra, J., Gottlieb, E., Budney, J., Daube, C., Daube, B., Hase,
F., Gerbig, C., Chance, K., and Wofsy, S.: Boston Column Network: Compact
Solar-Tracking Spectrometers and Differential Column Measurements, in:
American Geophysical Union Fall Meeting, San Francisco, California,
15–19 December, Abstract ID: A53L-3381, 10.13140/RG.2.1.2284.1361, 2014.Chen, J., Viatte, C., Hedelius, J. K., Jones, T., Franklin, J. E., Parker,
H., Gottlieb, E. W., Wennberg, P. O., Dubey, M. K., and Wofsy, S. C.:
Differential column measurements using compact solar-tracking spectrometers,
Atmos. Chem. Phys., 16, 8479–8498, 10.5194/acp-16-8479-2016, 2016.Connor, B., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.:
Orbiting Carbon Observatory: Inverse method and prospective error analysis,
J. Geophys. Res., 113, D05305, 10.1029/2006JD008336, 2008.Dohe, S., Sherlock, V., Hase, F., Gisi, M., Robinson, J., Sepúlveda, E.,
Schneider, M., and Blumenstock, T.: A method to correct sampling ghosts in
historic near-infrared Fourier transform spectrometer (FTS) measurements,
Atmos. Meas. Tech., 6, 1981–1992, 10.5194/amt-6-1981-2013, 2013.Feist, D. G., Arnold, S. G., Hase, F., and Ponge, D.: Rugged optical mirrors
for Fourier transform spectrometers operated in harsh environments, Atmos.
Meas. Tech., 9, 2381–2391, 10.5194/amt-9-2381-2016, 2016.Frey, M., Hase, F., Blumenstock, T., Groß, J., Kiel, M., Mengistu Tsidu,
G., Schäfer, K., Sha, M. K., and Orphal, J.: Calibration and instrumental
line shape characterization of a set of portable FTIR spectrometers for
detecting greenhouse gas emissions, Atmos. Meas. Tech., 8, 3047–3057,
10.5194/amt-8-3047-2015, 2015.Gisi, M.: EM27/SUN, in: Annual Joint NDACC-IRWG & TCCON Meeting, Bad
Sulza, Germany, May 12–14, available at:
http://www.acom.ucar.edu/irwg/IRWG_2014_presentations/Wednesday_PM/Gisi_Bruker_EN27.pdf
(last access: 4 May 2016), 2014.Gisi, M., Hase, F., Dohe, S., Blumenstock, T., Simon, A., and Keens, A.:
XCO2-measurements with a tabletop FTS using solar absorption spectroscopy,
Atmos. Meas. Tech., 5, 2969–2980, 10.5194/amt-5-2969-2012, 2012.Hale, G. E.: The Astrophysical Observatory of the California Institute of
Technology, Astrophys. J., 82, 111–139, 10.1086/143663, 1935.Hase, F.: Improved instrumental line shape monitoring for the ground-based,
high-resolution FTIR spectrometers of the Network for the Detection of
Atmospheric Composition Change, Atmos. Meas. Tech., 5, 603–610,
10.5194/amt-5-603-2012, 2012.Hase, F., Blumenstock, T., and Paton-Walsh, C.: Analysis of the instrumental
line shape of high-resolution fourier transform IR spectrometers with gas
cell measurements and new retrieval software., Appl. Opt., 38, 3417–3422,
10.1364/AO.38.003417, 1999.Hase, F., Hannigan, J., Coffey, M. T., Goldman, A., Hopfner, M., Jones, N.
B., Risland, C. P., and Wood, S. W.: Intercomparison of retrieval codes used
for the analysis of high-resolution, ground-based FTIR measurements, J.
Quant. Spectrosc. Ra., 87, 25–52, 10.1016/j.jqsrt.2003.12.008, 2004.Hase, F., Frey, M., Blumenstock, T., Groß, J., Kiel, M., Kohlhepp, R.,
Mengistu Tsidu, G., Schäfer, K., Sha, M. K., and Orphal, J.: Application
of portable FTIR spectrometers for detecting greenhouse gas emissions of the
major city Berlin, Atmos. Meas. Tech., 8, 3059–3068,
10.5194/amt-8-3059-2015, 2015.Hase, F., Frey, M., Kiel, M., Blumenstock, T., Harig, R., Keens, A., and
Orphal, J.: Addition of a channel for XCO observations to a portable FTIR
spectrometer for greenhouse gas measurements, Atmos. Meas. Tech., 9,
2303–2313, 10.5194/amt-9-2303-2016, 2016.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. Opt., 41), 6968–6979, 10.1364/AO.41.006968, 2002.Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, S., 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, Bull. Am. Meteorol. Soc., 77, 437–471,
10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.Keppel-Aleks, G., Toon, G. C., Wennberg, P. O., and Deutscher, N. M.:
Reducing the impact of source brightness fluctuations on spectra obtained by
Fourier-transform spectrometry., Appl. Opt., 46, 4774–4779,
10.1364/AO.46.004774, 2007.Klappenbach, F., Bertleff, M., Kostinek, J., Hase, F., Blumenstock, T.,
Agusti-Panareda, A., Razinger, M., and Butz, A.: Accurate mobile remote
sensing of XCO2 and XCH4 latitudinal transects from aboard a research
vessel, Atmos. Meas. Tech., 8, 5023–5038, 10.5194/amt-8-5023-2015, 2015.Kort, E. A., Angevine, W. M., Duren, R., and Miller, C. E.: Surface
observations for monitoring urban fossil fuel CO2 emissions: Minimum
site location requirements for the Los Angeles megacity, J. Geophys. Res.
Atmos., 118, 1–8, 10.1002/jgrd.50135, 2013.Learner, R. C. M., Thorne, A. P., and Brault, J. W.: Ghosts and artifacts in
Fourier-transform spectrometry, Appl. Opt., 35, 2947–2954,
10.1364/AO.35.002947, 1996.Lindenmaier, R., Dubey, M. K., Henderson, B. G., Butterfield, Z. T., Herman,
J. R., Rohn, T., and Lee, S.-H. Multiscale observations of CO2,
13CO2, and pollutants at Four Corners for emission verification and
attribution. Proceedings of the National Academy of Sciences of the United
States of America, 111, 8386-8391, 10.1073/pnas.1321883111, 2014.McKain, K., Wofsy, S. C., Nehrkorn, T., Eluszkiewicz, J., Ehleringer, J. R.,
and Stephens, B. B.: Assessment of ground-based atmospheric observations for
verification of greenhouse gas emissions from an urban region, Proc. Natl.
Acad. Sci. USA, 109, 8423–8428, 10.1073/pnas.1116645109, 2012.Messerschmidt, J., Macatangay, R., Notholt, J., Petri, C., Warneke, T., and
Weinzierl, C.: Side by side measurements of CO2 by ground-based Fourier
transform spectrometry (FTS), Tellus, Ser. B Chem. Phys. Meteorol., 62,
749–758, 10.1111/j.1600-0889.2010.00491.x, 2010.Parker, H. A., Hedelius, J., Viatte, C., Wunch, D., Wennberg, P. O., Chen,
J., Wofsy, S., Jones, T., Franklin, J., Dubey, M. K., Roehl, C. M., Podolske,
J. R., Hillyard, P. W., and Iraci, L. T.: Compact Solar Spectrometer Column
CO2, and CH4 Observations: Performance Evaluation at Multiple North
American TCCON Sites, in: American Geophysical Union Fall Meeting, San
Francisco, California, 14–18 December, Abstract ID: GC11B-1030,
2015.Petri, C., Warneke, T., Jones, N., Ridder, T., Messerschmidt, J., Weinzierl,
T., Geibel, M., and Notholt, J.: Remote sensing of CO2 and CH4 using
solar absorption spectrometry with a low resolution spectrometer, Atmos.
Meas. Tech., 5, 1627–1635, 10.5194/amt-5-1627-2012, 2012.Pougatchev, N. S., Connor, B. J., and Rinsland, C. P.: Infrared measurements
of the ozone vertical distribution above Kitt Peak, J. Geophys. Res., 100,
16689–16697, 10.1029/95JD01296, 1995.Rodgers, C.: Inverse methods for atmospheric sounding: Theory and practice,
World Scientific, Singapore, 256 pp., 10.1142/3171, 2000.Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments, J. Geophys. Res., 108, 4116, 10.1029/2002JD002299, 2003.Saad, K. M., Wunch, D., Toon, G. C., Bernath, P., Boone, C., Connor, B.,
Deutscher, N. M., Griffith, D. W. T., Kivi, R., Notholt, J., Roehl, C.,
Schneider, M., Sherlock, V., and Wennberg, P. O.: Derivation of tropospheric
methane from TCCON CH4 and HF total column observations, Atmos. Meas.
Tech., 7, 2907–2918, 10.5194/amt-7-2907-2014, 2014.Shiomi, K., Kuze, A., Kawakami, S., Kataoka, F., Hedelius, J., Viatte, C.,
Wennberg, P., Wunch, D., Roehl, C., Leifer, I., Tanaka, T., Iraci, L.,
Bruegge, C., and Schwander, F.: GOSAT CO2 and CH4 validation
activity with a portable FTS at Pasadena, Chino, and Railroad Valley, in:
American Geophysical Union Fall Meeting, San Francisco, California,
14–18 December, Abstract ID: A41I-0162, 2015.Thompson, A. and Chen, H. M.: Beamcon III, a linearity measurement instrument
for optical detectors, J. Res. Natl. Inst. Stand. Technol., 99, 751–755,
10.6028/jres.099.067, 1994.Toon, G. C.: The JPL MkIV interferometer, Opt. Photonics News, 2, 19,
10.1364/OPN.2.10.000019, 1991.Viatte, C., Lauvaux, T., Hedelius, J. K., Parker, H., Chen, J., Jones, T.,
Franklin, J. E., Deng, A. J., Gaudet, B., Verhulst, K., Duren, R., Wunch, D.,
Roehl, C., Dubey, M. K., Wofsy, S., and Wennberg, P. O.: Methane emissions
from dairies in the Los Angeles Basin, Atmos. Chem. Phys. Discuss.,
10.5194/acp-2016-281, in review, 2016.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, 1–11, 10.1029/2006JD007154, 2006.Wennberg, P. O., Wunch, D., Roehl, C., Blavier, J.-F., Toon, G. C., and
Allen, N.: TCCON data from California Institute of Technology, Pasadena,
California, USA, Release GGG2014R1.,
10.14291/tccon.ggg2014.pasadena01.R1/1182415, 2014.Wunch, D., Taylor, J. R., Fu, D., Bernath, P., Drummond, J. R., Midwinter,
C., Strong, K., and Walker, K. A.: Simultaneous ground-based observations of
O3, HCl, N2O, and CH4 over Toronto, Canada by three Fourier
transform spectrometers with different resolutions, Atmos. Chem. Phys., 7,
1275–1292, 10.5194/acp-7-1275-2007, 2007.Wunch, D., Wennberg, P. O., Toon, G. C., Keppel-Aleks, G., and Yavin, Y. G.:
Emissions of greenhouse gases from a North American megacity, Geophys. Res.
Lett., 36, L15810, 10.1029/2009GL039825, 2009.Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B.,
Fischer, M. L., Uchino, O., Abshire, J. B., Bernath, P., Biraud, S. C.,
Blavier, J.-F. L., Boone, C., Bowman, K. P., Browell, E. V., Campos, T.,
Connor, B. J., Daube, B. C., Deutscher, N. M., Diao, M., Elkins, J. W.,
Gerbig, C., Gottlieb, E., Griffith, D. W. T., Hurst, D. F., Jiménez, R.,
Keppel-Aleks, G., Kort, E. A., Macatangay, R., Machida, T., Matsueda, H.,
Moore, F., Morino, I., Park, S., Robinson, J., Roehl, C. M., Sawa, Y.,
Sherlock, V., Sweeney, C., Tanaka, T., and Zondlo, M. A.: Calibration of the
Total Carbon Column Observing Network using aircraft profile data, Atmos.
Meas. Tech., 3, 1351–1362, 10.5194/amt-3-1351-2010, 2010.Wunch, D., Wennberg, P. O., Toon, G. C., Connor, B. J., Fisher, B., Osterman,
G. B., Frankenberg, C., Mandrake, L., O'Dell, C., Ahonen, P., Biraud, S. C.,
Castano, R., Cressie, N., Crisp, D., Deutscher, N. M., Eldering, A., Fisher,
M. L., Griffith, D. W. T., Gunson, M., Heikkinen, P., Keppel-Aleks, G.,
Kyrö, E., Lindenmaier, R., Macatangay, R., Mendonca, J., Messerschmidt,
J., Miller, C. E., Morino, I., Notholt, J., Oyafuso, F. A., Rettinger, M.,
Robinson, J., Roehl, C. M., Salawitch, R. J., Sherlock, V., Strong, K.,
Sussmann, R., Tanaka, T., Thompson, D. R., Uchino, O., Warneke, T., and
Wofsy, S. C.: A method for evaluating bias in global measurements of CO2
total columns from space, Atmos. Chem. Phys., 11, 12317–12337,
10.5194/acp-11-12317-2011, 2011a.Wunch, D., Toon, G. C., Blavier, J.-F. L., 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. Trans. R. Soc. A, 369,
2087–2112, 10.1098/rsta.2010.0240, 2011b.
Wunch, D., Toon, G. C., Sherlock, V., Deutscher, N. M., Liu, C., Feist, D.
G., and Wennberg, P. O.: The Total Carbon Column Observing Network's GGG2014
Data Version, 43, 10.14291/tccon.ggg2014.documentation.R0/1221662, 2015.Wunch, D., Toon, G. C., Hedelius, J., Vizenor, N., Roehl, C. M., Saad, K. M.,
Blavier, J.-F. L., Blake, D. R., and Wennberg, P. O.: Quantifying the Loss of
Processed Natural Gas Within California's South Coast Air Basin Using
Long-term Measurements of Ethane and Methane, Atmos. Chem. Phys. Discuss.,
10.5194/acp-2016-359, in review, 2016.