Submitted as: model description paper | 18 Dec 2020
Review status: this preprint is currently under review for the journal GMD.
The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies
Antoine Berchet1,Espen Sollum2,Rona L. Thompson2,Isabelle Pison1,Joël Thanwerdas1,Grégoire Broquet1,Frédéric Chevallier1,Tuula Aalto3,Peter Bergamaschi4,Dominik Brunner5,Richard Engelen6,Audrey Fortems-Cheiney1,Christoph Gerbig7,Christine Groot Zwaaftink2,Jean-Matthieu Haussaire5,Stephan Henne5,Sander Houweling8,Ute Karstens9,Werner L. Kutsch10,Ingrid T. Luijkx11,Guillaume Monteil9,Paul I. Palmer12,Jacob C. A. van Peet8,Wouter Peters11,13,Philippe Peylin1,Elise Potier1,Christian Rödenbeck7,Marielle Saunois1,Marko Scholze9,Aki Tsuruta3,and Yuanhong Zhao1Antoine Berchet et al.Antoine Berchet1,Espen Sollum2,Rona L. Thompson2,Isabelle Pison1,Joël Thanwerdas1,Grégoire Broquet1,Frédéric Chevallier1,Tuula Aalto3,Peter Bergamaschi4,Dominik Brunner5,Richard Engelen6,Audrey Fortems-Cheiney1,Christoph Gerbig7,Christine Groot Zwaaftink2,Jean-Matthieu Haussaire5,Stephan Henne5,Sander Houweling8,Ute Karstens9,Werner L. Kutsch10,Ingrid T. Luijkx11,Guillaume Monteil9,Paul I. Palmer12,Jacob C. A. van Peet8,Wouter Peters11,13,Philippe Peylin1,Elise Potier1,Christian Rödenbeck7,Marielle Saunois1,Marko Scholze9,Aki Tsuruta3,and Yuanhong Zhao1
Received: 02 Dec 2020 – Accepted for review: 16 Dec 2020 – Discussion started: 18 Dec 2020
Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.
The Community Inversion Framework: codes and documentationAntoine Berchet, Espen Sollum, Isabelle Pison, Rona L. Thompson, Joël Thanwerdas, Audrey Fortems-Cheiney, Jacob C. A. van Peet, Elise Potier, Frédéric Chevallier, and Grégoire Broquet https://doi.org/10.5281/zenodo.4322372
Antoine Berchet et al.
Viewed
Total article views: 540 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
392
143
5
540
30
5
3
HTML: 392
PDF: 143
XML: 5
Total: 540
Supplement: 30
BibTeX: 5
EndNote: 3
Views and downloads (calculated since 18 Dec 2020)
Cumulative views and downloads
(calculated since 18 Dec 2020)
Viewed (geographical distribution)
Total article views: 438 (including HTML, PDF, and XML)
Thereof 433 with geography defined
and 5 with unknown origin.
We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
We present here a Community Inversion Framework (CIF) to help rationalize development efforts...