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Volume 13, issue 14
Atmos. Chem. Phys., 13, 7115-7132, 2013
https://doi.org/10.5194/acp-13-7115-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 7115-7132, 2013
https://doi.org/10.5194/acp-13-7115-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Jul 2013

Research article | 29 Jul 2013

Towards better error statistics for atmospheric inversions of methane surface fluxes

A. Berchet1, I. Pison1, F. Chevallier1, P. Bousquet1, S. Conil2, M. Geever3, T. Laurila4, J. Lavrič5, M. Lopez1, J. Moncrieff6, J. Necki7, M. Ramonet1, M. Schmidt1, M. Steinbacher8, and J. Tarniewicz1 A. Berchet et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, IPSL, Gif-sur-Yvette, France
  • 2Andra, DRD/OS, Observatoire Pérenne de l'Environnement, France
  • 3National University of Ireland, Galway, Ireland
  • 4FMI, Finnish Meteorological Institute, Helsinki, Finland
  • 5Max Planck Institute for Biogeochemistry, Jena, Germany
  • 6University of Edinburgh, Edinburgh, UK
  • 7AGH University of Science and Technology, Krakow, Poland
  • 8Empa, Laboratory for Air Pollution/Environmental Technology, Duebendorf, Switzerland

Abstract. We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.

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