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The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2​​​​​​​) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1 %–0.5 % in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 snapshot area mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contains artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated Level 1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g., MicroCarb, GeoCarb, GOSAT-GW, CO2M).

We characterize major anthropogenic point sources in the South African Highveld region using Orbiting Carbon Observatory-3 (OCO-3) Snapshot Area Map (SAM) carbon dioxide (CO2) and Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) observations. Altogether we analyze six OCO-3 SAMs. We estimate the emissions of six power stations (Kendal, Kriel, Matla, Majuba, Tutuka and Grootvlei) and the largest single emitter of greenhouse gas in the world, Secunda CTL synthetic fuel plant. We apply the cross-sectional flux method for the emission estimation, and we extend the method to fit several plumes at the same time. Overall, the satellite-based emission estimates are in good agreement (within the uncertainties) as compared to emission inventories, even for the cases where several plumes are mixed. We also discuss the advantages and challenges of the current measurement systems for greenhouse gas emission monitoring and reporting, and the applicability of different emission estimation approaches to future satellite missions such as the Copernicus CO2 Monitoring Mission (CO2M) and the Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW), including the joint analysis of CO2 and NO2 observations.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm has been applied to multi-year records of observations from NASA's Orbiting Carbon Observatory -2 and -3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling, but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 ppm and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements show root mean squared errors (RMSE) of approximately 0.8 ppm and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 ppm and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of errors in small areas, as well as biases across land-ocean crossings, also show encouraging results, for each sensor and in their agreement. Taken together, our results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1–0.5 % in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 Snapshot Area Mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contain artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated L1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias, and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g. MicroCARB, GeoCarb, GOSAT-GW, CO2M).

Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allowing for a large number of co-located observations. Several different OCO-2 retrieval algorithm modifications are tested, with the most successful being a single-band Cox–Munk-only model. Using this, we find excellent agreement between the two sensors, with OCO-2 having a small mean bias against AMSR2 of −0.22 m s−1, an RMSD of 0.75 m s−1, and a correlation coefficient of 0.94. Although OCO-2 is restricted to clear-sky measurements, potential benefits of its higher spatial resolution relative to microwave instruments include the study of coastal wind processes, which may be able to inform certain economic sectors.

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 the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO2 emissions. 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 influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ERCO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ERCO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ERCO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, 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 nearly coincident overpasses per city. Results suggest that the ERCO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value ( ppb-CO/ppm-CO2). In contrast, the ERCO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO/ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO2 and CO concentrations simultaneously with high spatiotemporal resolutions.

International audience; Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO$_2$ emissions (larger than 1.0 ktCO$_2$ h$^{-1}$ per 10$^{-2}$ square degree gridcell) over the globe with a simple plume cross-sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO-2) to the average morning and afternoon (with OCO-3). We see consistent variations of the median emissions, indicating that the retrieval-inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorableobserving conditions, when the future satellite CO$_2$ imagers provide an order of magnitude more data

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO

The Orbiting Carbon Observatory 2 (OCO-2) satellite has been provided information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top-down atmospheric inversion methods. Column average CO2 dry air mole fraction retrievals has been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of ten atmospheric inversions all characterized by different transport models, data assimilation algorithm and prior fluxes using first OCO-2 v7 in 2015-2016 and then OCO-2 version 9 land observations for the longer period 2015- 2018. Inversions assimilating in situ (IS) measurements have been also used to provide a baseline against which to compare the satellite-driven results. The times series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiments using either OCO-2 or IS data. We then evaluate the inversion ensemble based on dataset from TCCON, aircraft, and in-situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions and the estimation of carbon emissions over tropical Africa require further analysis.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

International audience; NASA's Orbiting Carbon Observatory-3 (OCO-3) was designed to support the quantification and monitoring of anthropogenic CO 2 emissions. Its Snapshot Area Map (SAM) and target mode measurements provide an innovative dataset for carbon studies on sub-city scales. Unlike any other current space-based instrument, OCO-3 has the ability to scan large contiguous areas of emission hot spots like cities, power plants, and volcanoes. These measurements result in dense, fine-scale spatial maps of column averaged dry-air mole fractions of carbon dioxide (X CO 2). For the first time, we present and analyze X CO 2 distributions over the Los Angeles megacity (LA) derived from OCO-3 SAM and target mode observations. Urban X CO 2 enhancements range from 0 − 6 ppm (median enhancements ≃ 2 ppm) relative to a clean background and show excellent agreement with nearby ground-based TCCON measurements of X CO 2. OCO-3's dense observations reveal intra-urban variations of X CO 2 over the city that have never been observed from space before. The spatial variations are mainly driven by the complex fossil fuel emission patterns and meteorological conditions in the LA Basin and are in good agreement with those from co-located TROPOMI measurements of co-emitted NO 2. Differences between measured and simulated X CO 2 enhancements from two models (WRF-Chem and X-STILT) are typically below 1 ppm with larger differences for some sub regions. Both models capture the observed intra-urban X CO 2 gradients. Further, OCO-3's multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. OCO-3's frequent target and SAM mode observations will pave the way to constrain urban emissions at finer, sub-city scales.

High-resolution bottom-up estimation provides a detailed guide for city greenhouse gas mitigation options, offering details that can increase the economic efficiency of emissions reduction options and synergize with other urban policy priorities at the human scale. As a critical constraint to urban atmospheric CO2 inversion studies, bottom-up spatiotemporally explicit emissions data products are also necessary to construct comprehensive urban CO2 emission information systems useful for trend detection and emissions verification. The “Hestia Project” is an effort to provide bottom-up granular fossil fuel (FFCO2) emissions for the urban domain with building/street and hourly space–time resolution. Here, we report on the latest urban area for which a Hestia estimate has been completed – the Los Angeles megacity, encompassing five counties: Los Angeles County, Orange County, Riverside County, San Bernardino County and Ventura County. We provide a complete description of the methods used to build the Hestia FFCO2 emissions data product for the years 2010–2015. We find that the LA Basin emits 48.06 (±5.3) MtC yr−1, dominated by the on-road sector. Because of the uneven spatial distribution of emissions, 10 % of the largest-emitting grid cells account for 93.6 %, 73.4 %, 66.2 %, and 45.3 % of the industrial, commercial, on-road, and residential sector emissions, respectively. Hestia FFCO2 emissions are 10.7 % larger than the inventory estimate generated by the local metropolitan planning agency, a difference that is driven by the industrial and electricity production sectors. The detail of the Hestia-LA FFCO2 emissions data product offers the potential for highly targeted, efficient urban greenhouse gas emissions mitigation policy. The Hestia-LA v2.5 emissions data product can be downloaded from the National Institute of Standards and Technology repository (https://doi.org/10.18434/T4/1502503, Gurney et al., 2019).

Isoprene is the atmosphere’s most important non-methane organic compound, with key impacts on atmospheric oxidation, ozone, and organic aerosols. In-situ isoprene measurements are sparse, and satellite-based constraints have employed an indirect approach using its oxidation product formaldehyde, which is affected by non-isoprene sources plus uncertainty and spatial smearing in the isoprene-formaldehyde relationship. Direct global isoprene measurements are therefore needed to better understand its sources, sinks, and atmospheric impacts. Here we show that the isoprene spectral signatures are detectable from space using the satellite-borne Cross-track Infrared Sounder (CrIS), develop a full-physics retrieval methodology for quantifying isoprene abundances from these spectral features, and apply the algorithm to CrIS measurements over Amazonia. The results are consistent with model output and in-situ data, and establish the feasibility of direct global space-based isoprene measurements. Finally, we demonstrate the potential for combining space-based measurements of isoprene and formaldehyde to constrain atmospheric oxidation over isoprene source regions., Isoprene is a key component of the atmosphere, with impacts on oxidation, ozone and organic aerosols, but in-situ measurements are limited. Here, the authors present a full-physics measurement framework based on satellite data that enables the direct observation of atmospheric isoprene from space.

All measurements of X CO 2 from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X CO 2 retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X CO 2 . For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec ) introduce a bias in X CO 2 in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X CO 2 data.

The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52∘, with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

The GOSAT TANSO-FTS sensor has been collecting high spectral resolution measurements of reflected solar radiation in the Oxygen A-band (0.76 microns) and two shortwave-infrared carbon dioxide (CO2) absorption bands (1.6 and 2.0 microns) since April, 2009. The measured radiances allow for estimates of the total column carbon dioxide (XCO2) via retrieval inversion. An eleven year long record of XCO2 retrieved via NASA’s Atmospheric Carbon Observations from Space (ACOS) build 9 software suite is analyzed and discussed. The v9 XCO2 data has been publicly available on the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) since the spring of 2020.The ACOS GOSAT v9 XCO2 is evaluated against CO2 flux inversion models, observations from the Total Carbon Column Observation Network (TCCON), as well as against collocated measurements from NASA’s OCO-2 satellite. The results indicate a product that agrees with OCO-2 and models within approximately 0.25 ppm with less than 1 ppm standard deviation (σ). Agreement with TCCON is within approximately 0.1 ppm with approximately 1 ppm σ for daily overpass mean aggregated data. The ACOS GOSAT v9 XCO2 product will allow CO2 flux inversion modelers and terrestrial ecologists to address questions about long term (decadal) carbon cycle dynamics related to net and gross carbon fluxes.

In 2020, many countries implemented lockdowns to control the spread of the novel coronavirus disease (COVID-19), leading to reported decreases in anthropogenic CO2 emissions based on bottom-up estimates. Some studies reported that the resulting atmospheric CO2 changes were below the detection limit of current observing systems on the ground or in space. We quantify CO2 emissions from Europe’s largest fossil fuel burning power plant before and during lockdown using space-based CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions. The results show clear emission reductions of >20% in April 2020, demonstrating the ability of space-based CO2 observations to quantify emission reductions at the facility level. This research reinforces the value of space-based CO2 data for verifying future CO2 emission reductions expected from climate change mitigation policies and the importance of monitoring emissions at sub-national scales.

While initial plans for measuring carbon dioxide from space hoped for 1-2 ppm levels of accuracy (bias) and precision in the CO2 column mean dry air mole fraction (XCO2), in the past few years it has become clear that accuracies better than 0.5 ppm are required for most current science applications. These include measuring continental (1000+ km) and regional scale (100s of km) surface fluxes of CO2 at monthly-average timescales. Considering the 400+ ppm background, this translates to an accuracy of roughly 0.1%, an incredibly challenging target to hit. Improvements in both instrument calibration and retrieval algorithms have led to significant improvements in satellite XCO2 accuracies over the past decade. The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, including post-retrieval filtering and bias correction, has demonstrated unprecedented accuracy with our latest algorithm version as applied to the Orbiting Carbon Observatory-2 (OCO-2) satellite sensor. This presentation will discuss the performance of the v10 XCO2 product by comparisons to TCCON and models, and showcase its performance with some recent examples, from the potential to infer large-scale fluxes to its performance on individual power plants. The v10 product yields better agreement with TCCON over land and ocean, plus reduced biases over tropical oceans and desert areas as compared to a median of multiple global carbon inversion models, allowing better accuracy and faith in inferred regional-scale fluxes. More specifically, OCO-2 has single sounding precision of ~0.8 ppm over land and ~0.5 ppm over water, and RMS biases of 0.5-0.7 ppm over both land and water. Given the six-year and growing length of the OCO-2 data record, this also enables new studies on carbon interannual variability, while at the same time allowing identification of more subtle and temporally-dependent errors. Finally, we will discuss the prospects of future improvements in the next planned version (v11), and the long-term prospects of greenhouse gas retrievals in the coming years.

An increasingly-large number of cities have designed ambitious climate mitigation plans to contribute to national GHG emission reduction objectives, typically starting with city-scale accountability of their direct and indirect fossil fuel emissions (Self Reported Inventories). Several concepts of spaceborne instruments providing high resolution 2D view of CO2 total column concentrations (XCO2) have been developed to monitor the CO2 anthropogenic emissions. Those images target mainly the CO2 atmospheric plumes from cities and large power plants, expecting that their study may quantify the emissions of those sources. However, there is still a need to develop and asses estimation methods which could process a large number of images in a robust way for such quantifications.In this study, we evaluate the ability to quantify CO2 urban emissions from XCO2 2D images by conducting sensitivity experiments with synthetic images over the Paris area during the winter 2019/2020. Synthetic data were simulated using state-of-the-art mesoscale model simulations at 1km resolution coupled to a high-resolution inventory, all validated against in situ CO2 tower measurements. We compared multiple direct flux calculation methods as described in various studies including Source Pixel, Integrated Mass Enhancement and Cross-sectional methods [Varon et al.,2018], further examined with various configurations, in addition to several formulations of Gaussian plume inversion techniques. These methods are computationally affordable compared to mesoscale inversions based on Eulerian or Lagrangian models, hence able to process rapidly a large amount of data over various cities in the future.We quantified the uncertainties and accuracy for these methods using different combinations of assumptions to i) identify the plume from the city, ii) to determine the corresponding background concentrations from natural and anthropogenic sources outside the city, and iii) to estimate the effective wind speed and direction of the plume. From this large ensemble of approaches and configurations, we identified the most robust methods and parametrizations with their corresponding precisions under various meteorological conditions and specifications of the XCO2 images (esp. spatial resolution and measurement errors).Starting with ideal cases without measurement noise and with perfectly known transport, we further increase the complexity of the experiments towards more realistic conditions in order to quantify the impact of the various sources of uncertainties (i.e. measurement errors, uncertainties in background conditions, uncertain plume detection, transport uncertainties). We show that most methods have to be adapted to handle the spatial extent of the targeted sources and that their performance are good in near steady state conditions. The source pixel method seems to be the less suited for extended source estimation. However, the final uncertainty is mainly driven by the pre-processing steps (background, plume limits and effective wind estimations).

The OCO-3 instrument was launched on May 4, 2019 from Kennedy Space Center to the International Space Station. Since August 2019, the instrument has taken measurements of reflected sunlight in three near-infrared bands from which column averaged dry-air mole fractions of carbon dioxide (XCO2) are derived. The instrument was specifically designed to measure anthropogenic emissions and its snapshot area map (SAM) and target (TG) observational modes allow to scan large contiguous areas (up to 80×80 km2) on a single overpass over emission hotspots like cities, power plants, or volcanoes. These measurements result in fine-scale spatial maps of XCO2 unlike what can be done with any other current space-based instrument. Here, we present and analyze XCO2 distributions over the Los Angeles (LA) megacity derived from multiple OCO-3 TG and SAM mode observations using the vEarly data product. We find that urban XCO2 values are elevated by 2-6 ppm relative to a clean background. The dense, high resolution OCO-3 observations reveal fine-scale, intra-urban variations of XCO2 over the LA megacity that have not been observed from space before. We further analyze the intra-urban characteristics and compare the XCO2 enhancements observed by OCO-3 with simulated values from two models that can resolve XCO2 variations across the city: an Eulerian (WRF-Chem) and a Lagrangian approach (X-STILT). We show that the observed variations are mainly driven by the complex and highly variable meteorological condition in the LA Basin. Median XCO2 differences between model and observation are typically below 1.3 ppm over the entirety of the LA megacity with slightly larger differences for some sub regions. Further, we find that OCO-3’s multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. In the future, these observations will help to better constrain urban emissions at finer spatiotemporal scales.

The Orbiting Carbon Observatory 3 (OCO-3) was installed on the International Space Station (ISS) in May 2019 and began routine operations in August 2019 to continue global CO2 and solar-induced chlorophyll fluorescence (SIF) observations using the flight spare instrument from OCO-2. The first version of the data, called vEarly, was released in early 2020, and an update, v10, is being prepared.The growing OCO-3 dataset includes the standard ocean and land measurements, as well as a large set of validation measurements over TCCON stations and a new locally focused measurement. The new Snapshot Area Map (SAM) mode, where 80km by 80km areas are sampled with 2km by 2km footprints in 2 minutes is measurement approach unique to OCO-3. This is a new observation mode made possible by the agile pointing mirror assembly of OCO-3. Data has been collected over hundreds of cities, volcanos, over areas of interest to the terrestrial carbon community, and in coordination with field campaigns.The cross comparison of OCO-3 and OCO-2 data, for radiances, XCO2, and SIF is underway to gain insights into data quality and to create and OCO-3 dataset that can be used seamlessly with OCO-2 measurements. We will discuss these intercomparisons, highlighting a few examples, such as the OCO-2 and OCO-3 target and SAM measurements in Los Angeles that were collected on the same day. Highlights from validation activities and global XCO2 data characteristics will be presented, as well as details of the SAM collection statistics and most sampled regions. The value of the OCO-3 dataset for characterization of diurnal patterns will also be shared.Highlights of the key scientific findings from the mission to date will be included. Finally, looking forward, I will also discuss the mission status, including the expectations for the remaining mission life and progress on developing an improved data version to be released in late spring/early summer 2021.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheri...

The third major reprocessing campaign for the Orbiting Carbon Observatory 2 mission is underway. With each release, several aspects of instrument calibration were improved. An onboard lamp tracks relative gain degradation, but ages too quickly to track absolute scaling. The most significant changes between builds were evolving assumptions about the stability of the solar calibrator. For the newest build, the trend in lunar measurements over a 4.5-year time series was applied. Another significant change was to expand a list of detector outliers via review of dark, lamp, and science measurements. More subtle adjustments to stray light and spectral dispersion were also performed. The techniques developed for OCO-2 are being applied to the Orbiting Carbon Observatory 3, which completed in-orbit checkout in August 2019. OCO-3 does not have a solar calibrator, and will need to constrain lamp aging via secondary lamps and comparison to other satellites.

With three imaging grating spectrometers, the Orbiting Carbon Observatory-2 (OCO-2) measures high spectral resolution spectra ( &lambda, / &Delta, &lambda, &asymp, 19,000) of reflected solar radiation within the molecular oxygen (O 2 ) A-band at 0.765 &mu, m and two carbon dioxide (CO 2 ) bands at 1.61 and 2.06 &mu, m. OCO-2 uses onboard lamps with a reflective diffuser, solar observations through a transmissive diffuser, lunar measurements, and surface targets for radiometric calibration and validation. Separating calibrator aging from instrument degradation poses a challenge to OCO-2. Here we present a methodology for trending the OCO-2 Build 8R radiometric calibration using OCO-2 nadir observations over eight desert sites and nearly simultaneous observations from Moderate Resolution Imaging Spectroradiometer (MODIS) with sensor viewing zenith angles of 15 &plusmn, 0.5 ∘ . For the O 2 A-band, this methodology is able to quantify a drift of &minus, 0.8 &plusmn, 0.1% per year and capture a small error in correcting the aging of the solar calibrator. For the other two OCO-2 bands, no measurable changes were seen, indicating less than 0.1% and less than 0.3% per year drift in the radiometric calibration of Band 2 and Band 3, respectively.

The Orbiting Carbon Observatory 3 (OCO-3) was installed on the International Space Station (ISS) in May 2019 and will continue the observation of global CO2 and solar-induced chlorophyll fluorescence (SIF) observations using the flight spare instrument from OCO-2. This talk will focus on the science data products, early operations, abd a few highlights from early mission data.The low-inclination ISS orbit lets OCO-3 sample the tropics and sub-tropics across the full range of daylight hours with dense observations at northern and southern mid-latitudes (+/- 52º). The combination of these dense CO2 and SIF measurements provides continuity of data for global flux estimates as well as a unique opportunity to address key deficiencies in our understanding of the global carbon cycle. The instrument utilizes an agile, 2-axis pointing mechanism (PMA), providing the capability to look towards the bright reflection from the ocean and validation targets. The PMA also allows for the collection of dense datasets over 80km by 80km areas called snapshot area maps (SAMs).The in-orbit check out of the instrument was conducted through July 2019. In this phase the engineering team verified the performance of all systems, the calibration team began collecting the needed calibration data, and the mission operations team verified the performance of all measurement modes and the mission operations planning tools. Since August 2019, OCO-3 has been collecting routine nadir, glint, target, and SAM data.Target mode observations over surface-based Total Carbon Column Observing Network (TCCON) sites help to identify and minimize potential instrument biases in the OCO-3 data. Other validation activities include direct comparisons to XCO2 estimates from OCO-2 and comparisons to predictions from near-real-time models. These comparisons will be discussed and early results will be presented. In addition, several hundred SAMs have been collected over (mega-)cities, powerplants, volcanos, and other terrestrial carbon focus areas. The steadily growing number of SAM observations provides a unique dataset for scientific studies on local scales. We discuss the potential of these observations, alone and in conjunction with simultaneous observations from other space-based sensors, to yield greater insights into carbon cycle science.

The Orbiting Carbon Observatory-3 (OCO-3) was successfully launched on May 4, 2019 from Kennedy Space Center via a Space-X Falcon 9. One week later, the instrument was installed as an external payload on the International Space Station (ISS). OCO-3 extends NASA’s study of carbon and measures the dry-air mole fraction of column carbon dioxide (XCO2) in the Earth’s atmosphere from space.These space-based measurements are compared to ground-based observations from the Total Carbon Column Observing Network (TCCON). TCCON is a global network of high-resolution ground-based Fourier Transform Spectrometers that records spectra of the sun in the near-infrared spectral region. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2 are retrieved. TCCON data are tied to the WMO scale and serve as the link between calibrated surface in situ measurements and OCO-3 measurements.OCO-3’s agile 2-D pointing mirror assembly (PMA) allows the instrument to stare at a TCCON station as it passes overhead - providing information about the quality, biases, and errors in the OCO-3 data. Here, we show early comparisons between the OCO-3 XCO2 dataset collected during target mode observations and coincident TCCON measurements and discuss site-dependent biases and its potential origins.

The NASA Orbiting Carbon Observatory-3 (OCO-3) was launched on May 4, 2019 to the International Space Station and has been taking measurements since August. OCO-3, like its predecessor OCO-2, makes hyperspectral measurements of reflected sunlight in three near-infrared bands. However, one of the unique features of OCO-3 is its ability to scan large contiguous areas on the order of 80 km by 80 km using a pointing mirror assembly. This capability, known as snapshot area mapping (SAM) mode, is being used to look at cities, forests, volcanos, and multiple other areas that are of interest to the carbon dioxide (CO2) and solar-induced chlorophyll fluorescence (SIF) scientific communities. For example, OCO-3 can measure column-mean CO2 (XCO2) over the entire Los Angeles, CA basin during the span of only two minutes. With several hundred SAMs having been collected so far and upwards of 25 possible per day, there is a wealth of data to investigate for scientific features and for any potential instrument biases. Additionally, this type of dense sampling will be a proof-of-concept for multiple future wide-swath CO2 missions. Here, we present several OCO-3 SAM mode measurements and discuss interesting features, XCO2 results, and future mission plans.

We quantify hourly on-road fossil fuel carbon dioxide (FFCO2) emissions at the road segment level for the Los Angeles (LA) megacity based on observed traffic data, and characterize emission patterns across space and time. This on-road FFCO2 emissions dataset for LA (from Hestia version 1.0), based on actual traffic volume, provides emissions per vehicle kilometer travelled (VKT)—an important metric for greenhouse gas (GHG) reductions. We further identify emissions hotpots that can help state and local policy makers plan the most effective GHG reduction strategies. On-road vehicle traffic accounts for half of the FFCO2 emissions in LA, of which 41% is from arterials (intermediate road type). Arterials also have the largest C emissions intensity—FFCO2 per VKT—possibly from high traffic congestion and fleet composition. Non-interstate emissions hotspots (> 419 tC lane-km-1) are equally dominated by arterials and collectors (the lowest road type) in terms of FFCO2 emissions though collectors have a higher VKT. These hotspots occur in densely populated areas and developed landuse classes, largely in LA (67%) and Orange (18%) counties, and provide specific targets for emissions reduction efforts. The estimated uncertainties for interstate, arterial and collector emissions per road length are ± 2.1, ± 0.5 and ± 18.0%, respectively. Our overall estimates compare reasonably well with other products, DARTE and FIVE but with substantial differences in spatial distribution. The method for developing this dataset is easily replicable in other urban landscapes, and represents a powerful tool for carbon cycle science and regional policy makers.

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

We present the current development of the Carbon Balance Observatory (CARBO). CARBO is a wide-swath mapping, low Earth orbit (LEO) new generation of instruments that expands on the ground-breaking CO2 and Solar Induced Fluorescence (SIF) measurements pioneered by the Orbiting Carbon Observatory (OCO-2/3) by adding CH4 and CO detection. The instrument’s spatial coverage is delivered at 2 km by 2 km resolution with a field-of-view of 10° to 15° from LEO for a ~200 km wide swath. It achieves roughly 20x better spatial coverage than the OCO-2 instrument, and 3x better Solar Induced Chlorophyll Fluorescence (SIF) detection sensitivity, in a smaller package. CARBO will measure CO2 at