In order to better manage anthropogenic CO2 emissions, improved methods of quantifying emissions are needed at all spatial scales from the national level down to the facility level. Although the ...Orbiting Carbon Observatory 2 (OCO‐2) satellite was not designed for monitoring power plant emissions, we show that in some cases, CO2 observations from OCO‐2 can be used to quantify daily CO2 emissions from individual middle‐ to large‐sized coal power plants by fitting the data to plume model simulations. Emission estimates for U.S. power plants are within 1–17% of reported daily emission values, enabling application of the approach to international sites that lack detailed emission information. This affirms that a constellation of future CO2 imaging satellites, optimized for point sources, could monitor emissions from individual power plants to support the implementation of climate policies.
Plain Language Summary
Burning coal for electricity generation accounts for more than 40% of humanity's current global CO2 emissions. To better manage CO2 emissions, improved methods of quantifying emissions are needed at all spatial scales. Although the Orbiting Carbon Observatory 2 (OCO‐2) satellite was not designed for monitoring power plant emissions, we show that in select cases, CO2 observations from OCO‐2 can be used to quantify daily CO2 emissions from individual middle‐ to large‐sized coal power plants by fitting the data to a simple model. Demonstrating the method on U.S. power plants with reliable reported emission data enabled application of the approach to international sites that have less or lower quality information available on emissions. Space agencies around the world are currently exploring how to design satellite missions to help address climate change and support Monitoring, Reporting and Verification (MRV) of CO2 emissions for climate agreements. This work affirms that a constellation of CO2 imaging satellites, with a design optimized for point sources, could monitor CO2 emissions from individual fossil fuel burning power plants to support that objective.
Key Points
The combustion of coal for electricity generation accounts for more than 40% of global anthropogenic CO2 emissions
Orbiting Carbon Observatory 2 observations can be used to quantify CO2 emissions from individual coal power plants, in selected cases
This work suggests that a future constellation of CO2 imaging satellites could monitor fossil fuel power plant CO2 emissions to support climate policy
The 2015-2016 El Niño led to historically high temperatures and low precipitation over the tropics, while the growth rate of atmospheric carbon dioxide (CO
) was the largest on record. Here we ...quantify the response of tropical net biosphere exchange, gross primary production, biomass burning, and respiration to these climate anomalies by assimilating column CO
, solar-induced chlorophyll fluorescence, and carbon monoxide observations from multiple satellites. Relative to the 2011 La Niña, the pantropical biosphere released 2.5 ± 0.34 gigatons more carbon into the atmosphere in 2015, consisting of approximately even contributions from three tropical continents but dominated by diverse carbon exchange processes. The heterogeneity of the carbon-exchange processes indicated here challenges previous studies that suggested that a single dominant process determines carbon cycle interannual variability.
The Total Carbon Column Observing Network Wunch, Debra; Toon, Geoffrey C.; Blavier, Jean-François L. ...
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences,
05/2011, Letnik:
369, Številka:
1943
Journal Article
Recenzirano
Odprti dostop
A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO 2 , CO, CH 4 , N 2 O and other molecules that absorb in the ...near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO 2 ). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.
The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial ...resolution and sampling. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH4 data that show the good agreement of the updated TROPOMI CH4 with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause.
The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the ...molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO2 distribution could be misinterpreted as evidence for CO2 fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO2. Each of these soundings has an unprecedented combination of spatial resolution (< 3 km2/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.
In a 3.5-year long study, the long-term
performance of a mobile, solar absorption Bruker EM27/SUN spectrometer, used
for greenhouse gas observations, is checked with respect to a co-located
reference ...Bruker IFS 125HR spectrometer, which is part of the Total Carbon
Column Observing Network (TCCON). We find that the EM27/SUN is stable on
timescales of several years; the drift per year between the EM27/SUN and the
official TCCON product is 0.02 ppmv for XCO2 and 0.9 ppbv for
XCH4, which is within the 1σ precision of the comparison,
0.6 ppmv for XCO2 and 4.3 ppbv for XCH4. The bias between
the two data sets is 3.9 ppmv for XCO2 and 13.0 ppbv for
XCH4. In order to avoid sensitivity-dependent artifacts, the EM27/SUN
is also compared to a truncated IFS 125HR data set derived from
full-resolution TCCON interferograms. The drift is 0.02 ppmv for
XCO2 and 0.2 ppbv for XCH4 per year, with 1σ
precisions of 0.4 ppmv for XCO2 and 1.4 ppbv for XCH4,
respectively. The bias between the two data sets is 0.6 ppmv for
XCO2 and 0.5 ppbv for XCH4. With the presented long-term
stability, the EM27/SUN qualifies as an useful supplement to the existing
TCCON network in remote areas. To achieve consistent performance, such an
extension requires careful testing of any spectrometers involved by
application of common quality assurance measures. One major aim of the
COllaborative Carbon Column Observing Network (COCCON) infrastructure is to
provide these services to all EM27/SUN operators. In the framework of COCCON
development, the performance of an ensemble of 30 EM27/SUN spectrometers was
tested and found to be very uniform, enhanced by the centralized inspection
performed at the Karlsruhe Institute of Technology prior to deployment.
Taking into account measured instrumental line shape parameters for each
spectrometer, the resulting average bias across the ensemble with respect to
the reference EM27/SUN used in the long-term study in XCO2 is
0.20 ppmv, while it is 0.8 ppbv for XCH4. The average standard
deviation of the ensemble is 0.13 ppmv for XCO2 and 0.6 ppbv for
XCH4. In addition to the robust metric based on absolute differences,
we calculate the standard deviation among the empirical calibration factors.
The resulting 2σ uncertainty is 0.6 ppmv for XCO2 and
2.2 ppbv for XCH4. As indicated by the executed long-term study on
one device presented here, the remaining empirical calibration factor deduced
for each individual instrument can be assumed constant over time. Therefore
the application of these empirical factors is expected to further improve the
EM27/SUN network conformity beyond the scatter among the empirical
calibration factors reported above.
Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and
methane (CH4) is the second most important greenhouse gas contributing to human-induced
climate change. ...Detailed and continuous observations of these gases are necessary to better assess
their impact on climate and atmospheric pollution. While surface and airborne measurements are able
to accurately determine atmospheric abundances on local scales, global coverage can only be
achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which
was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer
measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath
on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the
shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These
characteristics enable the determination of both gases with an unprecedented level of detail on a
global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4
are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm
spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm
Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm
is intended to be used with the operational algorithms for mutual verification and to provide new
geophysical insights. We introduce the algorithm in detail, including expected error characteristics
based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration
procedure applied in the post-processing of the XCH4 data. The quality of the results based
on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer
(FTS) measurements providing realistic error estimates of the satellite data: the XCO data
set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of
1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb
(0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and
XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a
high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based
on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison
to the operational products and examples of the detection of emission sources in a single satellite
overpass, such as CO emissions from the steel industry and CH4 emissions from the
energy sector, which potentially allows for the advance of emission monitoring and air quality assessments
to an entirely new level.
We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based ...spectrometers (EM27/SUN), in situ isotopic 13∕12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s−1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5 ppt s−1 at Chino.
On 13 October 2017, the Tropospheric Monitoring Instrument
(TROPOMI) was launched on the Copernicus Sentinel-5
Precursor satellite in a sun-synchronous orbit. One of the
mission's operational data ...products is the total column
concentration of carbon monoxide (CO), which was released
to the public in July 2018. The current TROPOMI CO
processing uses the HITRAN 2008 spectroscopic data with
updated water vapor spectroscopy and produces a CO data
product compliant with the mission requirement of 10 %
precision and 15 % accuracy for single soundings.
Comparison with ground-based CO observations of the Total
Carbon Column Observing Network (TCCON) show systematic
differences of about 6.2 ppb and single-orbit
observations are superimposed by a significant striping
pattern along the flight path exceeding 5 ppb. In this
study, we discuss possible improvements of the CO data
product. We found that the molecular spectroscopic data
used in the retrieval plays a key role for the data
quality where the use of the Scientific Exploitation of
Operational Missions – Improved Atmospheric Spectroscopy
Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases
reduce the bias between TROPOMI and TCCON due to improved
CH4 spectroscopy. SEOM-IAS achieves the best
spectral fit quality (root-mean-square, rms,
differences between the simulated and measured spectrum)
of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to
3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the
bias even further below 1 ppb. HITRAN 2012 shows the
worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections
and furthermore introduces an artificial bias of about
-1.5×1017 molec cm−2 between TROPOMI CO and
the CAMS-IFS model in the Tropics caused by the H2O
spectroscopic data. Moreover, analyzing 1 year of
TROPOMI CO observations, we identified increased striping
patterns by about 16 % percent from November 2017 to
November 2018. For that, we defined a measure γ,
quantifying the relative pixel-to-pixel variation in CO in the
cross-track and along-track directions.
To mitigate this effect, we discuss two
destriping methods applied to the CO data a posteriori.
A destriping mask calculated per orbit by median filtering
of the data in the cross-track direction significantly
reduced the stripe pattern from γ=2.1 to γ=1.6.
However,
the destriping can be further improved, achieving γ=1.2 by
deploying a Fourier analysis and filtering of the
data, which not only corrects for stripe patterns in the
cross-track direction but also accounts for the
variability of stripes along the flight path.
Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high ...latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.
PDF
