Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

O’Dell, C.; Eldering, A.; Wennberg, P. O.; Crisp, David; Gunson, M. R.; Fisher, B.; Frankenberg, Christian; Kiel, Matthäus; Lindqvist, Hannakaisa; Mandrake, Lukas; Merrelli, Aronne; Natraj, Vijay; Nelson, Robert R.; Osterman, G. B.; Payne, Vivienne H.; Taylor, Thomas E.; Wunch, Debra; Drouin, Brian J.; Oyafuso, Fabiano; Chang, Albert Y.; McDuffie, James; Smyth, Michael P.; Baker, D. F.; Basu, Sourish; Chevallier, F.; Crowell, Sean; Feng, Liang; Palmer, Paul I.; Dubey, Manvendra K.; García, Omaira; Griffith, David; Hase, Frank; Iraci, L. T.; Kivi, Rigel; Morino, Isamu; Notholt, Justus; Ohyama, Hirofumi; Petri, Christof; Roehl, Coleen M.; Sha, Mahesh Kumar; Strong, Kimberly; Sussmann, Ralf; Té, Yao; Uchino, Osamu; Velazco, Voltaire A.

doi:10.5194/amt-11-6539-2018

Cited by 241 publications

(336 citation statements)

References 112 publications

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“…Validation and bias correction have been important elements of the data processing strategy for OCO-2 as the OCO-2 XCO 2 retrieval algorithm continues to evolve [22]. The study by Wunch et al [18] provides a comprehensive validation of the Version 7 retrospective (V7r) XCO 2 product against the Total Carbon Column Observing Network (TCCON), and found that the absolute median difference is less than 0.4 ppm and the RMS difference less than 1.5 ppm.…”

Section: Oco-2 Co 2 Datamentioning

confidence: 99%

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

et al. 2019

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NASA's carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015)(2016)(2017)(2018) of global (60 • S-60 • N) XCO 2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO 2 dry air mole fraction, XCO 2 , are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO 2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO 2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO 2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO 2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO 2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO 2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO 2 signatures on global scale. all over the world and show levels that are about 100 ppm higher than they were in 1958, when they were about 315 ppm. However, the measurement networks are still denser in the Western world.One of the most important applications of CO 2 measurements is to infer the surface CO 2 fluxes, i.e., sources and sinks (or emissions into and removal from the atmosphere). A variety of methods are commonly used to derive CO 2 fluxes from spatially resolved estimates of column-averaged CO 2 dry air mole fraction, XCO 2 . These methods can be broadly classified as analytic mass balance, plume dispersion, or atmospheric inversion methods. All three approaches predict or optimize surface fluxes needed to reproduce the observed XCO 2 distribution in the presence of the time-varying wind field. Groun...

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Section: Oco-2 Co 2 Datamentioning

confidence: 99%

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

et al. 2019

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“…For the sensitivity to the variation in observation data, we use the three latest versions (v7, v8, and v9) of OCO-2 XCO 2 to form the observation vector y. Between the v7 and v8 data, there are a large number of changes in the OCO-2 XCO 2 retrieval algorithm related to spectroscopy, aerosol treatment, the surface model, and prior meteorology [29]. The differences between the v8 and v9 data are mainly from an update in surface pressure estimation [30].…”

Section: Results and Analysismentioning

confidence: 99%

“…While the plume was located largely north of the river (figure 2(d)), the WRF-Chem simulations put the plume south of the river (figures 2(c) and (e)). In comparison, Nassar et al [1] achieved an estimation of 29 wind direction adjustment to optimize the match between the Gaussian plume model and the plume detected by the OCO-2 data. We note that the simulated low wind speed (∼2 m s −1 ) in this case is more likely to be associated with variability in wind direction, as smaller scale features that are difficult to resolve exert greater influence in the absence of stronger winds.…”

Section: Results and Analysismentioning

confidence: 99%

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Estimating power plant CO₂ emission using OCO-2 XCO₂ and high resolution WRF-Chem simulations

Zheng

Nassar

Baxter

2019

Environ. Res. Lett.

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Anthropogenic CO 2 emission from fossil fuel combustion has major impacts on the global climate. The Orbiting Carbon Observatory 2 (OCO-2) observations have previously been used to estimate individual power plant emissions with a Gaussian plume model assuming constant wind fields. The present work assesses the feasibility of estimating power plant CO 2 emission using high resolution chemistry transport model simulations with OCO-2 observation data. In the new framework, 1.33 km Weather Research and Forecasting-Chem (WRF)-Chem simulation results are used to calculate the Jacobian matrix, which is then used with the OCO-2 XCO 2 data to obtain power plant daily mean emission rates, through a maximum likelihood estimation. We applied the framework to the seven OCO-2 observations of near mid-to-large coal burning power plants identified in Nassar et al (2017 Geophys. Res. Lett. 44, 10045-53). Our estimation results closely match the reported emission rates at the Westar power plant (Kansas, USA), with a reported value of 26.67 ktCO 2 /day, and our estimated value at 25.82-26.47 ktCO 2 /day using OCO-2 v8 data, and 22.09-22.80 ktCO 2 /day using v9 data. At Ghent, KY, USA, our estimations using three versions (v7, v8, and v9) range from 9.84-20.40 ktCO 2 /day, which are substantially lower than the reported value (29.17 ktCO 2 /day). We attribute this difference to diminished WRF-Chem wind field simulation accuracy. The results from the seven cases indicate that accurate estimation requires accurate meteorological simulations and high quality XCO 2 data. In addition, the strength and orientation (relative to the OCO-2 ground track) of the XCO 2 enhancement are important for accurate and reliable estimation. Compared with the Gaussian plume model based approach, the high resolution WRF-Chem simulation based approach provides a framework for addressing varying wind fields, and possible expansion to city level emission estimation.

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“…The O 2 A-band spectra have high sensitivity to changes in the light path. They are primarily used to determine the surface pressure, to screen for optically thick clouds and to quantify the total optical depth (AOD) and vertical distribution of aerosols for soundings that are sufficiently optically thin (AOD < 0.3) to yield accurate estimates of the column average CO 2 dry air mole fraction, XCO 2 (O'Dell et al, 2018).…”

Section: Oco-2 and Calipso Measurementsmentioning

confidence: 99%

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements

Zeng

Chen

Natraj

et al. 2020

Remote Sensing of Environment

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Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth's energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O 2 ) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O 2 A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale. Highlights (3-5 bullets; 85 characters including space)• A spectral sorting approach is proposed for constraining aerosol profile from space;• The approach is successfully applied to OCO-2 O 2 A-band measurements;• Retrieved profile parameters show good agreement with CALIPSO measurements;• The approach can be used to map the aerosol vertical distribution on a global scale.

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Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Cited by 241 publications

References 112 publications

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

Estimating power plant CO₂ emission using OCO-2 XCO₂ and high resolution WRF-Chem simulations

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements

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Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Cited by 241 publications

References 112 publications

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements

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Estimating power plant CO₂ emission using OCO-2 XCO₂ and high resolution WRF-Chem simulations