We present the first quantitative intercomparison between two open-path
dual-comb spectroscopy (DCS) instruments which were operated across adjacent
2 km open-air paths over a 2-week period. We used DCS to measure the
atmospheric absorption spectrum in the near infrared from 6023 to
6376 cm
Quantitative determination of greenhouse gas fluxes over a variety of
temporal and spatial scales is necessary for characterizing source strength
and intermittency and for future emissions monitoring, reporting, and
verification. To this end, techniques exist to measure greenhouse gas
concentrations on a variety of length scales, each of which has advantages
and disadvantages. Point sensors provide valuable information about local
sources, but their use for continuous regional measurements on sampling
towers is complicated by local wind patterns, local sources, and mixing
within the planetary boundary layer (PBL), especially at night (Lauvaux et
al., 2008, 2012; Ciais et al., 2010). Similarly, total-column measurements
are particularly useful for sub-continental to global-scale measurements;
however they are sensitive to atmospheric transport errors within the PBL
(Lauvaux and Davis, 2014), are affected by clouds and aerosols, are primarily
limited to daytime measurements, and lack either the revisit rates or
mobility for regional flux measurements. Horizontal integrated path
measurements are complementary to point sensors and satellites: they cover
spatial scales from one to tens of kilometers, provide measurements on
second-to-minute timescales with portable instruments, and are thus
appropriate for regional studies. Active-source open-path sensors such as
open-path Fourier transform spectroscopy (FTS), differential
optical absorption spectroscopy (DOAS), differential lidar (DIAL), or tunable
diode laser absorption spectroscopy (TDLAS) are often used for these
measurements and can retrieve path-averaged concentrations but typically with
10 % or greater uncertainties (EPA, 2017, and references therein).
Recently, open-path dual-comb spectroscopy (DCS) has emerged as a new
technique that could potentially provide precise, accurate, continuous
regional measurements of the mole fractions of CO
Here we demonstrate that open-path DCS can indeed yield dry mole fractions
over open-air paths with a high level of intercomparability, over long
periods of time, and with sufficient precision to track variations in the
ambient levels from local sources and sinks. Two completely independent
open-path DCS instruments are operated over neighboring open-air paths during
a 2-week measurement campaign. Although both DCS instruments use fully
stabilized frequency combs, they are portable (Truong et al., 2016) and are
operated nearly continuously during both day and night through laboratory
temperature variations from 17 to 25 We use dry mole fraction
for carbon dioxide and methane, denoted respectively as XCO
Similar intercomparison measurements between conventional active open-path sensors are rare but have shown agreement of typically 1–20 % (Thoma et al., 2005; Hak et al., 2005; Smith et al., 2011; Shao et al., 2013; Conde et al., 2014; Reiche et al., 2014; Thalman et al., 2015). Here, we find agreement between two DCS instruments that is an order of magnitude better and is comparable to that achieved with highly calibrated, state-of-the-art solar-looking FTS systems that retrieve vertical column measurements (Messerschmidt et al., 2011; Frey et al., 2015; Hedelius et al., 2016); however, open-path DCS does not require instrument-specific calibrations (e.g., of the instrument line shape) and provides a very different capability by retrieving the dry mole fractions across regional, kilometer-scale paths over day and night on a mobile platform. Moreover, as the agreement between open-path DCS instruments is below the level of natural background fluctuations, future measurements can facilitate accurate inverse modeling to identify sources and sinks of carbon emission over regions. As an initial demonstration, we discuss the observed diurnal variations from this 2-week measurement campaign in the final section of the paper.
A frequency comb is a laser pulsed at a very precise repetition rate of
Setup for the open-path dual-comb spectrometer (DCS) comparison at
the NIST Boulder, CO campus. The main components for DCS A and DCS B are
housed in a rooftop laboratory, including the frequency combs, telescope,
receiver, and processor. For each DCS, the combined comb light is launched
from a telescope; travels
A DCS system can be thought of as a high-resolution Fourier transform
spectrometer but has a number of attributes that distinguish it from conventional
horizontal open-path FTS systems and other open-path instruments, which could lead to
higher-performance atmospheric trace gas monitoring. A compact, mobile DCS
system such as this one has no moving parts, dense point spacing (200 MHz or
0.0067 cm
Figure 2 provides an overview of our experiment. Two DCS instruments measured
the atmospheric absorption across a 2 km round-trip open path that extended
from the top of a building at the National Institute of Standards and
Technology (NIST) Boulder campus to a pair of retroreflectors located on a
nearby hill. Both DCS instruments were based on a similar overall design and
used self-referenced, stabilized frequency combs (Sinclair et al., 2015), but
one was built by a team at NIST and the other by a team at the University of
Colorado; they are hereafter referred to as DCS A and DCS B, respectively. As
outlined below, the two instruments differed in their exact design and
physical parameters. Nevertheless, no instrument-specific calibration or bias
offset was applied to either system. The acquired atmospheric absorption
spectra were fit to retrieve the column density of CO
Specifications of the two DCS systems. HCC: hollow corner cube.
Figure 3a shows a simplified schematic of both DCS setups. Briefly, each DCS
system used two mutually coherent self-referenced erbium-doped fiber
frequency combs based on the design of Sinclair et al. (2015) with nominal
repetitions rates
Raw spectra from DCS A (blue) and DCS B (red).
The combined light from both combs is transmitted via single-mode fiber to a
telescope, where it is launched to a retroreflector. The returning signal is
collected onto an amplified, 100 MHz bandwidth InGaAs photodetector and
digitized at a sampling rate
The exact optical layout of DCS A is given in Truong et al. (2016). While following the same basic design, DCS B differs in several technical details. These include a slightly different output spectrum; slightly different comb tooth spacings and offset frequency; minor differences in the reference cw laser and its locking scheme; and different amplifier design, launched and received powers, and telescope design. Some of these differences are laid out in Fig. 3, Table 1, and Sect. 2.4 below.
We have found that the use of stabilized, phase-coherent frequency combs is a
necessary but not sufficient prerequisite to reaching sub-percent agreement
in retrieved gas concentrations. It is critical that the spectrally filtered
comb output does not include stray unfiltered light. Similarly, any stray
reflections from the telescope that can “short-circuit” the atmospheric
path must be avoided. As with FTS systems, nonlinearities are problematic. In the optical domain,
nonlinearities can arise when the combs are combined in fiber with high
optical power. These are minimized for DCS A by filtering the light, which
decreases the peak power, before combining the combs. For DCS B the combs do
not have booster amplifiers and thus have significantly lower power.
Nonlinearities in the photodetection can also occur (Zolot et al., 2013); in
laboratory tests with a CO reference cell, we verified no bias in retrieved
concentration as a function of received power up to 300
Concentration retrievals from DCS A (blue dots) and DCS B (red
lines) for HDO (ppm), H
The two telescope systems are shown in Fig. 3a. Due to the large spectral
bandwidth, reflective optics are preferred to minimize chromatic dispersion.
For DCS A, the launch–receive system was based on a bi-directional off-axis
parabolic telescope with a 3 in. aperture, while for DCS B it was based on a
6 in. aperture Ritchey–Chrétien (RC) telescope with the light launched
separately from behind the secondary mirror. In both cases, the launched beam
diameter was
Figure 3b shows the return power for both systems as a function of time. For
reference, the minimum return power required to obtain useful spectra was
The acquired transmission spectra are the product
Time series of concentration differences, where difference is defined as DCS A minus DCS B.
Figure 4a shows the overall raw DCS transmission spectra from the two
instruments averaged for a 3 h period. They differ significantly because of
the different comb intensity profiles,
Figure 4d shows the residuals after fitting the absorption lines in the DCS A spectrum to HITRAN 2008 and removing the etalon. The higher signal-to-noise ratio (SNR) of the DCS A yields an even lower broadband noise than the difference spectrum, but there are clear residuals near spectral lines attributable to incorrect line shapes/parameters in the HITRAN 2008 database. Nevertheless, the overall magnitude of the residuals is very small in comparison to the spectral absorption.
List of systematic uncertainties. See discussion in Sect. 3.2 and
3.4 for more details. Upper half of table: instrument-specific systematics.
Lower half of the table: model-dependent systematics common to both
instruments. The final row is the estimated added uncertainty from the water
correction, which is dominated by the nominal
From the fitted concentrations, we retrieve the mole fractions as outlined in
Sect. 2.5. The retrieved time series for XCO
Table 2 summarizes the systematic uncertainties of the DCS systems in terms of instrument-specific systematics in the top of the table and model-dependent uncertainties common to both instruments in the bottom part of the table. We discuss the instrument-specific uncertainties below and the model-dependent uncertainties in Sect. 3.5 in the context of the comparison with the point sensor.
To explore the source of the small systematic offsets between the DCS
retrievals, we have performed a number of control comparisons. In the
processing, we have varied the initial concentration guess in the fit with
negligible effect. We have also varied the polynomial baseline fit by
adjusting the window size from 100 to 150 GHz and polynomial order from
seventh to ninth order and again found negligible variations of 0.02 %
for CO
Statistical distributions of the differences between DCS A and DCS B
for dry CO
Figure 8 shows the precision versus averaging time (determined using the
modified Allan deviation) based on the scatter across a 6 h period over
which the CO
The precision at 30 s and 5 min averaging time is given at the bottom of
Table 1. DCS A has superior CO
A commercial cavity ring-down point sensor, Picarro Model 1301 The
use of trade names is necessary to specify the experimental results and does
not imply endorsement by the National Institute of Standards and Technology.
Precision (Allan deviation) versus averaging time,
Comparison between the open-path DCS A data (blue) and the point
CRDS data (gold) for H
The short-duration spikes are present in the CRDS time series and presumably
arise from the very different spatial sampling of the two instruments. The
DCS system measures the integrated column over 1 km (one way), while
the CRDS is a point sensor and therefore much more sensitive to local
sources. For example, a 1 m
The long-term overall offset between the CRDS and DCS data is a consequence
of their very different calibrations. The CRDS is tied to the WMO scale for
CO
Diurnal cycles for wind speed, wind direction, XCH
In contrast, the DCS has no instrument-specific calibration but relies
completely on a fit to a spectral database to extract the gas concentrations
from the measured absorbance across a wide range of ambient pressures and
temperatures. Here, we use HITRAN 2008, which has
This basic discrepancy between retrievals based on line shape parameters from
a spectral database and manometric and gravimetric
calibrations (WMO standard) is not unique to DCS. Several studies have
calibrated the Total Carbon Column Observing Network (TCCON) retrievals
against WMO-based instruments (Wunch et al., 2010; Messerschmidt et al.,
2011; Geibel et al., 2012; Tanaka et al., 2012). Although TCCON is not a
solely HITRAN-based analysis (Wunch et al., 2011), a correction factor of
0.9898 for CO
An accurate path-averaged air temperature is also important to avoid
systematic offsets. Unlike vertical total-column measurements through the
entire atmosphere, kilometer-scale open horizontal paths should have
relatively low temperature inhomogeneities of around a few degrees Celsius,
and thus the use of a single “path-averaged” temperature in the fit is
sufficient for accurate retrievals. We verified this through a sensitivity
study comparing retrievals for simulated spectra with temperature gradients
up to 10
Finally, the calculation of the dry mole fraction requires an accurate removal of the water concentration. We do retrieve the water concentration with a high precision from the fits. As shown in Table 2, the dominant uncertainty in the water concentration is again the line strengths from the spectral database.
The 2 weeks of open-path data are analyzed for diurnal cycles, as shown in
Fig. 10, with the intent of an
initial understanding of CO
As expected, the median of the diurnal cycle for CO
Methane has a significantly weaker diurnal cycle than carbon dioxide, which is consistent with a species that lacks significant diurnally varying local sources. Rather, its concentration follows expected variations in the boundary layer height; the concentration increases overnight into the early morning as the boundary layer collapses, and then it decreases during the late morning through afternoon as the boundary layer rises again. The largest likely methane source near Boulder is local oil/gas fields, but these typically lie to the northeast, while the wind directions are generally out of the west to southeast. It is also possible that the methane comes from leaking natural gas infrastructure within the city.
Here we provide the first quantitative comparison of open-path dual-comb
spectroscopy instruments. The dual-comb spectrometers were based on fully
phase-coherent and stabilized fiber frequency combs and operated nearly
continuously over a 2-week period. We performed these measurements over
adjacent 2 km round-trip paths to measure concentrations of dry CO
As per NIST regulations, all data are archived at NIST and available upon request.
As described in Sect. 2.5, we extract the path-averaged temperature directly
from a fit to the 30013
Figure A1 compares this fitted path-averaged temperature from DCS A to three
point sensors, two of which are located on the rooftop near the telescope
launch point, and one is located
The fitted path-averaged temperature over 2 weeks at 5 min
intervals (red) compared to the measured air temperature from a rooftop
anemometer located near the telescope (blue), a second thermistor temperature
sensor also located on the roof but 100 m away (black), and a third rooftop
temperature
other but do not agree with the fitted path-averaged
temperature. Moreover, that disagreement has a distinct diurnal character,
supporting the argument it arises from a real temperature gradient. In
contrast, the path-averaged temperature does often agree well with the
temperature measured by the third temperature point sensor located at an
altitude similar to or higher than the open path on the NCAR Mesa building
(
The authors declare that they have no conflict of interest.
We thank Tim Newburger and Kathryn McKain for assistance with the CRDS calibrations, Terry Bullet and Jon Kofler for assistance in setting up the CRDS sampling, Ryan Thalman for detailed long-path instrument correlation data, and Adam J. Fleischer and Anna Karion for assistance with the manuscript. This work was funded by the Defense Advanced Research Program Agency DSO SCOUT program, ARPA-E MONITOR program under award number DE-AR0000539, and James Whetstone and the NIST Greenhouse Gas and Climate Science Initiative. Eleanor M. Waxman and Kevin C. Cossel are supported by National Research Council postdoctoral fellowships. Edited by: Huilin Chen Reviewed by: two anonymous referees