Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity

Kiel, Matthäus; Eldering, A.; Roten, Dustin; Lin, John C.; Feng, Sha; Lei, Ruixue; Lauvaux, Thomas; Oda, Tomohiro; Roehl, Coleen M.; Blavier, Jean-François L.; Iraci, L. T.

doi:10.1016/j.rse.2021.112314

Cited by 76 publications

(77 citation statements)

References 46 publications

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“…A study used CO 2 observations at the TCCON Caltech site in Los Angeles to validate the CO 2 observed by OCO-3. The results of this study showed that the root mean square error (RMSE) between satellite data and ground-based data was 0.23 ppm, and the Pearson correlation coefficient was 0.99 [47]. In this study, the spatial matching setting is 0.1 • × 0.1 • , which is smaller than the spatial matching setting tested in our research, partly because the Caltech site is very close to the water area, which will affect the validation performance of satellite observations.…”

Section: Discussionmentioning

confidence: 84%

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

et al. 2021

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The Fourier Transform Spectrometer (FTS) at the Beijing Satellite Meteorological Ground Station observed XCO2 (the dry carbon dioxide column) from 2 March 2016 to 4 December 2018. The validation results of ground-based XCO2, as well as GOSAT, OCO-2, and TanSat XCO2, show that the best temporal matching setting for ground-based XCO2 and satellite XCO2 is ±1 h, and the best spatial matching setting for GOSAT is 0.5° × 0.5°. Consistent with OCO-2, the best spatial matching setting of TanSat is 5° × 5° or 6° × 6°. Among GOSAT, OCO-2, and TanSat, the satellite observation validation characteristics near 5° × 5° from the ground-based station are obviously different from other spatial matching grids, which may be due to the different observation characteristics of satellites near 5° × 5°. To study the influence of local CO2 sources on the characteristics of satellite observation validation, we classified the daily XCO2 observation sequence into concentrated, dispersive, increasing, and decreasing types, respectively, and then validated the satellite observations. The results showed that the concentrated and decreasing sub-datasets have better validation performance. Our results suggest that it is best to use concentrated and decreasing sub-datasets when using the Beijing Satellite Meteorological Ground Station XCO2 for satellite validation. The temporal matching setting should be ±1 h, and the spatial matching setting should consider the satellites observation characteristics of 5° × 5° distance from the ground-based station.

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Section: Discussionmentioning

confidence: 84%

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

et al. 2021

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“…The measured concentration enhancements are the net consequence of associated sources/sinks from source regions. That is, high atmospheric content of CO 2 or CO at the sounding location does not necessarily indicate a high emission rate at this location (Kiel et al, 2021). Our derived emissions and ERs, although reported for each sounding, indicate the overall emission and combustion efficiency over its source region.…”

Section: Intra-city Er Co Variations and Signal From Heavy Industrymentioning

confidence: 75%

“…Yet, no one has attempted to study the intra-urban gradient in combustion efficiency from space and connect such gradient to a specific combustion process. This is now possible by virtue of the Orbiting Carbon Observatory-3 (OCO-3) mounted on the International Space Station (ISS) that can sample a city-landscape during its Snapshot Area Mapping (SAM) mode (Eldering et al, 2019;Taylor et al, 2020;Kiel et al, 2021). In this effort to arrive at spatially-varying ERs from sensors with asynchronous orbits, we must account for several factors that have not been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO

Liu

Wennberg

et al. 2022

Preprint

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Abstract. Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at regional to city scale. Very few studies have focused on burning efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for deriving spatially-resolved ERs from asynchronous satellite measurements including 1) variations in meteorological conditions induced by different overpass times, 2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), and 3) interferences from the biosphere and biomass burning. In this study, we extended an established emission estimate approach to arrive at spatially-resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influence of the confounding factors listed above and further explain the intra-urban variations in ERs, we leveraged a Lagrangian atmospheric transport model and an urban land cover classification dataset and reported ERCO from the sounding level to the overpass- and city- levels. We found that the difference in the overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially-resolved ERCO. Specifically, a time difference of > 3 hours typically led to dramatic changes in the wind direction and shape of urban plumes and thereby making the calculation of accurate sounding-specific ERCO difficult. After removing those cases from consideration and applying a simple plume shift method when necessary, we discovered significant contrasts in combustion efficiencies between 1) two megacities versus two industry-oriented cities and 2) different regions within a city, based on six to seven nearly-coincident overpasses per city. Results suggest that the combustion efficiency for heavy industry in Los Angeles is slightly lower than its overall city-wide value (< 10 ppb-CO / ppm-CO2). In contrast, ERs related to the heavy industry in Shanghai are found to be much higher than Shanghai’s city-mean and more aligned with city-means of the two industry-oriented Chinese cities (approaching 20 ppb-CO / ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our first analysis provides guidance for estimating intra-city gradients in combustion efficiency from future missions, such as those that will map column CO2 and CO concentration simultaneously with high spatiotemporal resolutions.

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“…Optical remote sensing techniques sampling the total atmospheric column have been developed throughout the last two decades and have been found to be very useful for monitoring atmospheric GHGs (Wunch et al, 2011b). A series of state-of-the-art satellites with different spatiotemporal resolutions, including SCIAMACHY (Schneising et al, 2012;Dils et al, 2014;Houweling et al, 2014;Buchwitz et al, 2015;Heymann et al, 2015;Kulawik et al, 2016) and TROPOMI (Butz et al, 2012;Veefkind et al, 2012;Pandey et al, 2019;Wang et al, 2020a;Zhang et al, 2020b;Barre et al, 2021;Pandey et al, 2021;Park et al, 2021;Qu et al, 2021;Sha et al, 2021; by European Space Agency (ESA), GOSAT and GOSAT-2 by Japan (Butz et al, 2011;Morino et al, 2011;Cogan et al, 2012;Yoshida et al, 2013;Deng et al, 2014;Parker et al, 2020;Boesch et al, 2021), OCO-2 and OCO-3 by the United States (Thompson et al, 2012;Frankenberg et al, 2015;Eldering et al, 2017;Nassar et al, 2017;Patra et al, 2017;Wunch et al, 2017;Wang et al, 2020a;Zheng et al, 2020a;Zheng et al, 2020b;Hu and Shi, 2021;Kiel et al, 2021), TanSat (Liu et al, 2013;Liu et al, 2014;Yang et al, 2018a;Yang et al, 2018b, c;Zhang e...…”

Section: History Of Ghgs Monitoring Around the Globementioning

confidence: 99%

Monitoring greenhouse gases (GHGs) in China: status and perspective

Sun

Yin²,

Wang³

et al. 2022

Preprint

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Abstract. In order to establish a creditable greenhouse gases (GHGs) monitoring network to support the goals of carbon peak/neutrality, it is necessary to know what we have done and what we have to do in the future. In this study, we summarize an overview to the status and perspective of GHGs monitoring in China. With decades of effort, China has made a great breakthrough in GHGs monitoring capacity and steadily improved the performance of homemade GHGs monitoring instruments. However, most GHGs monitoring studies are hitherto research-oriented, temporal, sparse, and uncoordinated. It is suggested to take full advantages of various monitoring technologies, monitoring platforms, numerical simulations, and inventory compilation techniques to form a creditable GHGs stereoscopic monitoring and assessment system at an operational level. We envisage that this system can routinely quantify GHGs on national, provincial, regional and even individual scales with high spatiotemporal resolution and wide coverage to support low-carbon policy in China.

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Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity

Cited by 76 publications

References 46 publications

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO

Monitoring greenhouse gases (GHGs) in China: status and perspective

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Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity

Cited by 76 publications

References 46 publications

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

Study on the Ground-Based FTS Measurements at Beijing, China and the Colocation Sensitivity of Satellite Data

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO

Monitoring greenhouse gases (GHGs) in China: status and perspective

Contact Info

Product

Resources

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Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO