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Dataset

 

University of Leicester GOSAT Proxy XCH4 v9.0

Update Frequency: Not Planned
Latest Data Update: 2021-06-02
Status: Ongoing
Online Status: ONLINE
Publication State: Citable
Publication Date: 2020-05-07
DOI Publication Date: 2020-05-07
Download Stats: last 12 months
Dataset Size: 4.53K Files | 18GB

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.

Citable as:  Parker, R.; Boesch, H. (2020): University of Leicester GOSAT Proxy XCH4 v9.0. Centre for Environmental Data Analysis, 07 May 2020. doi:10.5285/18ef8247f52a4cb6a14013f8235cc1eb. https://dx.doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb
Abbreviation: Not defined
Keywords: GOSAT, CH4, Methane

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
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: 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
File Format:
CF compliant NetCDF

Citations: 12

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.

Chen, Z., Jacob, D. J., Gautam, R., Omara, M., Stavins, R. N., Stowe, R. C., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Pendergrass, D. C., & Hancock, S. (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(10), 5945–5967. https://doi.org/10.5194/acp-23-5945-2023
Chen, Z., Jacob, D. J., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Penn, E., & Yu, X. (2022). Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations. Atmospheric Chemistry and Physics, 22(16), 10809–10826. 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
Lu, X., Jacob, D. J., Wang, H., Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Shen, L., Qu, Z., Sulprizio, M. P., Nesser, H., Bloom, A. A., Ma, S., Worden, J. R., Fan, S., Parker, R. J., Boesch, H., Gautam, R., Gordon, D., Moran, M. D., … Andrews, A. (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 CHlt;subgt;4lt;/subgt; ObsPack) and satellite (GOSAT) atmospheric observations. Atmospheric Chemistry and Physics, 22(1), 395–418. https://doi.org/10.5194/acp-22-395-2022
Lu, X., Jacob, D. J., Zhang, Y., Maasakkers, J. D., Sulprizio, M. P., Shen, L., Qu, Z., Scarpelli, T. R., Nesser, H., Yantosca, R. M., Sheng, J., Andrews, A., Parker, R. J., Boesch, H., Bloom, A. A., & Ma, S. (2021). Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CHlt;subgt;4lt;/subgt; ObsPack) and satellite (GOSAT) observations. Atmospheric Chemistry and Physics, 21(6), 4637–4657. https://doi.org/10.5194/acp-21-4637-2021
Parker, R. J., Webb, A., Boesch, H., Somkuti, P., Barrio Guillo, R., Di Noia, A., Kalaitzi, N., Anand, J. S., Bergamaschi, P., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., … Wunch, D. (2020). A decade of GOSAT Proxy satellite CHlt;subgt;4lt;/subgt; observations. Earth System Science Data, 12(4), 3383–3412. 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(22), 5669–5691. https://doi.org/10.5194/bg-17-5669-2020
Parker, R. J., Wilson, C., Comyn-Platt, E., Hayman, G., Marthews, T. R., Bloom, A. A., Lunt, M. F., Gedney, N., Dadson, S. J., McNorton, J., Humpage, N., Boesch, H., Chipperfield, M. P., Palmer, P. I., & Yamazaki, D. (2022). Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations. Biogeosciences, 19(24), 5779–5805. https://doi.org/10.5194/bg-19-5779-2022
Yuzhong Zhang, Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Jinfeng Chang, A. Anthony Bloom, Ma, S., Worden, J., Parker, R. J., Boesch, H. (2021). Dataset for "Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations" (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4052518
Yuzhong Zhang, Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Jinfeng Chang, A. Anthony Bloom, Ma, S., Worden, J., Parker, R. J., Boesch, H. (2021). Dataset for "Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations" (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4052517
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., & Boesch, H. (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
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., & Boesch, H. (2021). Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations. Atmospheric Chemistry and Physics, 21(5), 3643–3666. https://doi.org/10.5194/acp-21-3643-2021

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Instrument/Platform pairings

TANSO Deployed on: GOSAT

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
Coverage
Temporal Range
Start time:
2009-04-23T00:00:00
End time:
2021-12-31T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°