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Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data

Journal Article


Abstract


  • Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in

    earth science. Satellite observations can provide information on the atmospheric state at fine spatial

    and temporal resolution while providing substantial coverage across the globe. For example, this

    capability can greatly enhance the understanding of the space-time variation of the greenhouse gas,

    carbon dioxide (CO2), since ground-based measurements are limited. NASA's Orbiting Carbon

    Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the

    mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric

    CO2. The retrieval is an inverse problem and consists of a physical forward model for the transfer

    of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and

    the surface. The model and other algorithm inputs introduce key sources of uncertainty into the

    retrieval problem. This article develops a computationally efficient surrogate model that is embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution of the retrieval error.

Authors


  •   Hobbs, Jonathan (external author)
  •   Braverman, Amy (external author)
  •   Cressie, Noel A.
  •   Granat, Robert (external author)
  •   Gunson, M (external author)

Publication Date


  • 2017

Citation


  • Hobbs, J., Braverman, A., Cressie, N., Granat, R. & Gunson, M. (2017). Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data. SIAM/ASA Journal on Uncertainty Quantification, 5 (1), 956-985.

Number Of Pages


  • 29

Start Page


  • 956

End Page


  • 985

Volume


  • 5

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in

    earth science. Satellite observations can provide information on the atmospheric state at fine spatial

    and temporal resolution while providing substantial coverage across the globe. For example, this

    capability can greatly enhance the understanding of the space-time variation of the greenhouse gas,

    carbon dioxide (CO2), since ground-based measurements are limited. NASA's Orbiting Carbon

    Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the

    mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric

    CO2. The retrieval is an inverse problem and consists of a physical forward model for the transfer

    of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and

    the surface. The model and other algorithm inputs introduce key sources of uncertainty into the

    retrieval problem. This article develops a computationally efficient surrogate model that is embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution of the retrieval error.

Authors


  •   Hobbs, Jonathan (external author)
  •   Braverman, Amy (external author)
  •   Cressie, Noel A.
  •   Granat, Robert (external author)
  •   Gunson, M (external author)

Publication Date


  • 2017

Citation


  • Hobbs, J., Braverman, A., Cressie, N., Granat, R. & Gunson, M. (2017). Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data. SIAM/ASA Journal on Uncertainty Quantification, 5 (1), 956-985.

Number Of Pages


  • 29

Start Page


  • 956

End Page


  • 985

Volume


  • 5

Issue


  • 1

Place Of Publication


  • United States