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OCO-2 XCO 2 retrievals using the FOCAL algorithm M. Buchwitz, M. Reuter, S. Noël, H. Bovensmann, J. P. Burrows, Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany www.iup.uni-bremen.de/carbon_ghg Contact: [email protected] [1] Bovensmann, H., et al., A remote sensing technique for global monitoring of power plant CO 2 emissions from space and related applications, Atmos. Meas. Tech., 3, 781-811, 2010. [2] Broquet, G., et al., The potential of satellite spectro-imagery for monitoring CO 2 emissions from large cities, Atmos. Meas. Tech., 11, 681-708, 2018. [3] Buchwitz, M., et al., Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO 2 and CH 4 retrieval errors by error parameterization, Atmos. Meas. Tech., 6, 3477-3500, 2013. [4] Buchwitz, M., et al., Copernicus Climate Change Service (C3S) Global Satellite Observations of Atmospheric Carbon Dioxide and Methane, Adv. Astronaut. Sci. Technol., 2018. [5] Buchwitz, M., et al., Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016, Atmos. Chem. Phys., 18, 17355-17370, 2018. [6] Reuter, M., et al., Decreasing emissions of NOx relative to CO 2 in East Asia inferred from satellite observations, Nature Geoscience, pp.4, 2014. [7] Reuter, M., et al., How much CO 2 is taken up by the European terrestrial biosphere?, Bull. Amer. Meteor. Soc., 665-671, 2017. [8] Reuter, M., et al., A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO 2 Retrieval Setup, Remote Sens., 9, 1159, 2017. [9] Reuter, M., et al., A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO 2 Retrievals from OCO-2, Remote Sens., 9, 1102, 2017. [10] Reuter, M., et al., Towards monitoring localized CO 2 emissions from space: co-located regional CO 2 and NO 2 enhancements observed by the OCO-2 and S5P satellites, Atmos. Chem. Phys. (submitted), 2019. [11] Pillai, D., et al., Tracking city CO 2 emissions from space using a high resolution inverse modeling approach: A case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591-9610, 2016. [12] Pinty B., et al., An Operational Anthropogenic COEmissions Monitoring & Verification Support capacity - Baseline Requirements, Model Components and Functional Architecture, doi: 10.2760/39384, European Commission Joint Research Centre, EUR 28736 EN, 2017. Abstract Satellite observations of the column-averaged dry-air mole fraction of CO 2 , denoted XCO 2 , combined with inverse modelling, permits to obtain information on natural and anthropogenic CO 2 sources and sinks (e.g., [1-7, 10-12]). The planned future European Copernicus anthropogenic CO 2 Monitoring (CO2M) mission, a Copernicus Sentinel High-Priority Candidate Mission (HPCM), will likely be implemented as a constellation of satellites with small ground pixels (approx. 2x2 km 2 ) and wide swath (> 200 km) as needed for XCO 2 imaging [1-3, 10-12]. In addition to XCO 2 , CO2M will also provide a number of other parameters, which are highly beneficial to meet the CO2M objectives, such as methane (XCH 4 ) and NO 2 columns. CO2M will cover similar spectral bands as NASA’s OCO-2 satellite mission however with a much wider swath resulting in about one order of magnitude more observations per satellite compared to OCO-2. Using co-funding from different sources including CHE and VERIFY the University of Bremen started working on the development of a new very fast but still very accurate algorithm (FOCAL) for the retrieval of XCO 2 from OCO-2 [8, 9] and to generate XCO 2 data products for CHE and VERIFY. Here we present an initial global XCO 2 data set covering the years 2015-2016 including comparisons with TCCON ground-based observations, the CAMS model and OCO-2 XCO 2 data products from NASA generated using NASA‘s ACOS algorithm. We also present first results from using OCO-2 XCO 2 in combination with Sentinel-5-Precursor (S5P) NO 2 to obtain information on anthropogenic CO 2 emission sources [10]. Selected references FOCALv08 XCO 2 Large-scale pattern (5 o x5 o ): Comparison with CAMS: Systematic and random errors: Comparison with TCCON: Comparisons with initial & latest NASA products: OCO-2 XCO 2 is sparse due to narrow swath, small ground pixels and strict quality filtering: Anthropogenic CO 2 emissions We use OCO-2 XCO 2 in combination with S5P NO 2 to identify XCO 2 enhancements from localized CO 2 emission sources and we quantify the CO 2 cross-sectional flux w.r.t. OCO- 2‘s orbit and compare with emission inventories [10]. Here 2 examples: Medupi and Matimba power plants in South Africa on July 11, 2018. Bhagdad on July 31, 2018. OCO-2 FOCAL v08 Latest OCO-2 NASA v9 (Lite) Acknowledgement: This research received funding from the European Commission via H2020 projects CHE & VERIFY, coordinated by ECMWF & LSCE/CEA, respectively.
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OCO-2 XCO2 retrievals using the FOCAL algorithm · very accurate algorithm (FOCAL) for the retrieval of XCO. 2. from OCO-2 [8, 9] and to generate XCO. 2. data products for CHE and

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Page 1: OCO-2 XCO2 retrievals using the FOCAL algorithm · very accurate algorithm (FOCAL) for the retrieval of XCO. 2. from OCO-2 [8, 9] and to generate XCO. 2. data products for CHE and

OCO-2 XCO2 retrievals using the FOCAL algorithmM. Buchwitz, M. Reuter, S. Noël, H. Bovensmann, J. P. Burrows, Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany

www.iup.uni-bremen.de/carbon_ghg

Contact: [email protected]

[1] Bovensmann, H., et al., A remote sensing technique for global monitoring of power plant CO2 emissions from space and relatedapplications, Atmos. Meas. Tech., 3, 781-811, 2010.

[2] Broquet, G., et al., The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities, Atmos. Meas. Tech., 11,681-708, 2018.

[3] Buchwitz, M., et al., Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by errorparameterization, Atmos. Meas. Tech., 6, 3477-3500, 2013.

[4] Buchwitz, M., et al., Copernicus Climate Change Service (C3S) Global Satellite Observations of Atmospheric Carbon Dioxide andMethane, Adv. Astronaut. Sci. Technol., 2018.

[5] Buchwitz, M., et al., Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observationsduring 2003-2016, Atmos. Chem. Phys., 18, 17355-17370, 2018.

[6] Reuter, M., et al., Decreasing emissions of NOx relative to CO2 in East Asia inferred from satellite observations, Nature Geoscience,pp.4, 2014.

[7] Reuter, M., et al., How much CO2 is taken up by the European terrestrial biosphere?, Bull. Amer. Meteor. Soc., 665-671, 2017.

[8] Reuter, M., et al., A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1:Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sens., 9, 1159, 2017.

[9] Reuter, M., et al., A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2:Application to XCO2 Retrievals from OCO-2, Remote Sens., 9, 1102, 2017.

[10] Reuter, M., et al., Towards monitoring localized CO2 emissions from space: co-located regional CO2 and NO2 enhancementsobserved by the OCO-2 and S5P satellites, Atmos. Chem. Phys. (submitted), 2019.

[11] Pillai, D., et al., Tracking city CO2 emissions from space using a high resolution inverse modeling approach: A case study for Berlin,Germany, Atmos. Chem. Phys., 16, 9591-9610, 2016.

[12] Pinty B., et al., An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support capacity - Baseline Requirements,Model Components and Functional Architecture, doi: 10.2760/39384, European Commission Joint Research Centre, EUR 28736 EN, 2017.

AbstractSatellite observations of the column-averaged dry-air mole fraction of CO2, denoted XCO2,combined with inverse modelling, permits to obtain information on natural andanthropogenic CO2 sources and sinks (e.g., [1-7, 10-12]).

The planned future European Copernicus anthropogenic CO2 Monitoring (CO2M) mission,a Copernicus Sentinel High-Priority Candidate Mission (HPCM), will likely be implementedas a constellation of satellites with small ground pixels (approx. 2x2 km2) and wide swath (>200 km) as needed for XCO2 imaging [1-3, 10-12]. In addition to XCO2, CO2M will alsoprovide a number of other parameters, which are highly beneficial to meet the CO2Mobjectives, such as methane (XCH4) and NO2 columns.

CO2M will cover similar spectral bands as NASA’s OCO-2 satellite mission however with amuch wider swath resulting in about one order of magnitude more observations per satellitecompared to OCO-2. Using co-funding from different sources including CHE and VERIFYthe University of Bremen started working on the development of a new very fast but stillvery accurate algorithm (FOCAL) for the retrieval of XCO2 from OCO-2 [8, 9] and togenerate XCO2 data products for CHE and VERIFY.

Here we present an initial global XCO2 data set covering the years 2015-2016 includingcomparisons with TCCON ground-based observations, the CAMS model and OCO-2 XCO2data products from NASA generated using NASA‘s ACOS algorithm. We also present firstresults from using OCO-2 XCO2 in combination with Sentinel-5-Precursor (S5P) NO2 toobtain information on anthropogenic CO2 emission sources [10].

Selected references

FOCALv08 XCO2

Large-scale pattern (5ox5o): Comparison with CAMS:

Systematic and random errors: Comparison with TCCON:

Comparisons with initial & latest NASA products:

OCO-2 XCO2 is sparse due to narrow swath, smallground pixels and strict quality filtering:

Anthropogenic CO2 emissionsWe use OCO-2 XCO2 in combination with S5P NO2 to identify XCO2 enhancements fromlocalized CO2 emission sources and we quantify the CO2 cross-sectional flux w.r.t. OCO-2‘s orbit and compare with emission inventories [10]. Here 2 examples:

Medupi and Matimba power plants in South Africa on July 11, 2018.

Bhagdad on July 31, 2018.

OCO-2 FOCAL v08 Latest OCO-2 NASA v9 (Lite)

Acknowledgement: This research receivedfunding from the European Commission via H2020 projects CHE & VERIFY, coordinated by ECMWF & LSCE/CEA, respectively.