Dataset
University of Leicester GOSAT Proxy XCH4 v9.0
Abstract
The University of Leicester GOSAT Proxy XCH4 v9.0 data set contains column-averaged dry-air mole fraction of methane (XCH4) generated from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using the University of Leicester Full-Physics retrieval scheme (UoL-FP) using the Proxy retrieval approach.
This data is an NCEO funded update/extension to the European Space Agency Climate Change Initiative (CCI) CH4_GOS_OCPR V7.0. and the Copernicus Climate Change Service (C3S) CH_4 v7.2 data sets. It's a full reprocessing, based on different underlying L1B radiance data with additional changes. The latest version of the GOSAT Level 1B files (version 210.210) was acquired directly from the National Institute for Environmental Studies (NIES) GOSAT Data Archive Service (GDAS) Data Server and are processed with the Leicester Retrieval Preparation Toolset to extract the measured radiances along with all required sounding-specific ancillary information such as the measurement time, location and geometry. These measured radiances have the recommended radiometric calibration and degradation corrections applied as per Yoshida et al., 2013 with an estimate of the spectral noise derived from the standard deviation of the out-of-band signal. The spectral data were then inputted into the UoL-FP retrieval algorithm where the Proxy retrieval approach is used to obtain the column-averaged dry-air mole fraction of methane (XCH4). Post-filtering and bias correction against the Total Carbon Column Observing Network is then performed. See process information and documentation for further details.
Details
Previous Info: |
No news update for this record
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Previously used record identifiers: |
No related previous identifiers.
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Access rules: |
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s): http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
Data lineage: |
Data were produced by the University of Leicester project team and delivered to Centre for Environmental Data Analysis (CEDA) for archival and publication. The data was produce under NCEO grant nceo020005 |
Data Quality: |
The data has been fully validated by the University of Leicester project team
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File Format: |
CF compliant NetCDF
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Related Documents
Citations: 14
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
Balasus, N., Jacob, D.J., Lorente, A., Maasakkers, J.D., Parker, R.J., Boesch, H., Chen, Z., Kelp, M.M., Nesser, H. & Varon, D.J. (2023) A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases. Atmospheric Measurement Techniques 16, 3787–3807. https://doi.org/10.5194/amt-16-3787-2023 https://doi.org/10.5194/amt-16-3787-2023 |
Chen, Z., Jacob, D.J., Gautam, R., et al. (2023) Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action. Atmospheric Chemistry and Physics 23, 5945–5967. https://doi.org/10.5194/acp-23-5945-2023 https://doi.org/10.5194/acp-23-5945-2023 |
Chen, Z., Jacob, D.J., Nesser, H., et al. (2022) Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations. Atmospheric Chemistry and Physics 22, 10809–10826. https://doi.org/10.5194/acp-22-10809-2022 https://doi.org/10.5194/acp-22-10809-2022 |
Höglund-Isaksson, L. (2021) Comment on acp-2021-671. https://doi.org/10.5194/acp-2021-671-rc1 https://doi.org/10.5194/acp-2021-671-rc1 |
Lu, X., Jacob, D.J., Wang, H., et al. (2022) Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) atmospheric observations. Atmospheric Chemistry and Physics 22, 395–418. https://doi.org/10.5194/acp-22-395-2022 https://doi.org/10.5194/acp-22-395-2022 |
Lu, X., Jacob, D.J., Zhang, Y., et al. (2021) Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) observations. Atmospheric Chemistry and Physics 21, 4637–4657. https://doi.org/10.5194/acp-21-4637-2021 https://doi.org/10.5194/acp-21-4637-2021 |
Parker, R.J., Webb, A., Boesch, H., et al. (2020) A decade of GOSAT Proxy satellite CH<sub>4</sub> observations. Earth System Science Data 12, 3383–3412. https://doi.org/10.5194/essd-12-3383-2020 https://doi.org/10.5194/essd-12-3383-2020 |
Parker, R.J., Wilson, C., Bloom, A.A., Comyn-Platt, E., Hayman, G., McNorton, J., Boesch, H. & Chipperfield, M.P. (2020) Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations. Biogeosciences 17, 5669–5691. https://doi.org/10.5194/bg-17-5669-2020 https://doi.org/10.5194/bg-17-5669-2020 |
Parker, R.J., Wilson, C., Comyn-Platt, E., et al. (2022) Evaluation of wetland CH4in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations. Biogeosciences 19, 5779–5805. https://doi.org/10.5194/bg-19-5779-2022 https://doi.org/10.5194/bg-19-5779-2022 |
Qu, Z., Jacob, D.J., Bloom, A.A., Worden, J.R., Parker, R.J. & Boesch, H. (2024) Inverse modeling of 2010–2022 satellite observations shows that inundation of the wet tropics drove the 2020–2022 methane surge. Proceedings of the National Academy of Sciences 121. https://doi.org/10.1073/pnas.2402730121 https://doi.org/10.1073/pnas.2402730121 |
Yuzhong Zhang, Jacob, D.J., Lu, X., et al. (2021) Dataset for ‘Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations’. https://doi.org/10.5281/ZENODO.4052517 https://doi.org/10.5281/zenodo.4052517 |
Yuzhong Zhang, Jacob, D.J., Lu, X., et al. (2021) Dataset for ‘Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations’. https://doi.org/10.5281/ZENODO.4052518 https://doi.org/10.5281/zenodo.4052518 |
Zhang, Y., Jacob, D.J., Lu, X., et al. (2020) Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations. https://doi.org/10.5194/acp-2020-964 https://doi.org/10.5194/acp-2020-964 |
Zhang, Y., Jacob, D.J., Lu, X., et al. (2021) Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations. Atmospheric Chemistry and Physics 21, 3643–3666. https://doi.org/10.5194/acp-21-3643-2021 https://doi.org/10.5194/acp-21-3643-2021 |
Process overview
Mobile platform operations
Mobile Platform Operation 1 | Mobile Platform Operation for GOSAT |
Computation Element: 1
Title | UoL_FP: University of Leicester Full-Physics retrieval algorithm for retrieval of XCH_4 from GOSAT data |
Abstract | The GOSAT spectral data were inputted into the UoL-FP retrieval algorithm where the Proxy retrieval approach was used to obtain the column-averaged dry-air mole fraction of methane (XCH4). See the linked documentation for further information |
Input Description | None |
Output Description | None |
Software Reference | None |
Output Description | None |
- units: K
- long_name: air_temperature_apriori
- var_id: air_temperature_apriori
- units: 1e-9
- var_id: ch4_profile_apriori
- long_name: ch4_profile_apriori
- units: 1e-6
- var_id: co2_profile_apriori
- long_name: co2_profile_apriori
- units: 1
- long_name: exposure_id
- var_id: exposure_id
- units: 1
- long_name: gain
- var_id: gain
- units: 1e-6
- long_name: h2o_profile_apriori
- var_id: h2o_profile_apriori
- units: 1e-6
- long_name: model_xco2
- var_id: model_xco2
- units: 1e-6
- long_name: model_xco2_median_diff
- var_id: model_xco2_median_diff
- units: 1e-6
- long_name: model_xco2_range
- var_id: model_xco2_range
- units: hPa
- var_id: pressure_levels
- long_name: pressure_levels
- units: 1
- var_id: pressure_weight
- long_name: pressure_weight
- units: 1e-9
- long_name: raw_xch4
- var_id: raw_xch4
- units: 1e-9
- long_name: raw_xch4_error
- var_id: raw_xch4_error
- units: 1e-6
- long_name: raw_xco2
- var_id: raw_xco2
- units: 1e-6
- long_name: raw_xco2_error
- var_id: raw_xco2_error
- units: 1
- long_name: retr_flag
- var_id: retr_flag
- units: degree
- standard_name: sensor_zenith_angle
- var_id: sensor_zenith_angle
- long_name: sensor_zenith_angle
- units: degree
- var_id: solar_zenith_angle
- standard_name: solar_zenith_angle
- long_name: solar_zenith_angle
- units: hPa
- long_name: surface_air_pressure_apriori
- var_id: surface_air_pressure_apriori
- units: hPa
- long_name: surface_air_pressure_apriori_std
- var_id: surface_air_pressure_apriori_std
- units: m
- standard_name: surface_altitude
- var_id: surface_altitude
- long_name: surface_altitude
- units: m
- long_name: surface_altitude_stdev
- var_id: surface_altitude_stdev
- units: 1e-9
- var_id: xch4
- long_name: xch4
- standard_name: dry_atmosphere_mole_fraction_of_methane
- units: 1
- var_id: xch4_averaging_kernel
- long_name: xch4_averaging_kernel
- units: 1
- var_id: xch4_quality_flag
- long_name: xch4_quality_flag
- units: 1e-9
- var_id: xch4_uncertainty
- long_name: xch4_uncertainty
- units: 1
- var_id: xco2_averaging_kernel
- long_name: xco2_averaging_kernel
Co-ordinate Variables
- units: degrees_north
- standard_name: latitude
- var_id: latitude
- long_name: latitude
- units: degrees_east
- standard_name: longitude
- var_id: longitude
- long_name: longitude
- long_name: time
- standard_name: time
- var_id: time
Temporal Range
2009-04-23T00:00:00
2021-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |