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The Community Inversion Framework v1.0 : A unified system for atmospheric inversion studies

Berchet, Antoine ; Sollum, Espen ; Thompson, Rona L. ; Pison, Isabelle ; Thanwerdas, Joël ; Broquet, Grégoire ; Chevallier, Frédéric ; Aalto, Tuula ; Berchet, Adrien and Bergamaschi, Peter , et al. (2021) In Geoscientific Model Development 14(8). p.5331-5354
Abstract

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, 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... (More)

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, 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 and reactive species 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 the 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 and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Geoscientific Model Development
volume
14
issue
8
pages
24 pages
publisher
Copernicus GmbH
external identifiers
  • scopus:85114042252
ISSN
1991-959X
DOI
10.5194/gmd-14-5331-2021
language
English
LU publication?
yes
id
7a79c3ea-ff1e-44bb-b7cf-cd91c7b916bf
date added to LUP
2022-01-04 08:44:44
date last changed
2024-11-17 16:19:37
@article{7a79c3ea-ff1e-44bb-b7cf-cd91c7b916bf,
  abstract     = {{<p>Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, 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 and reactive species 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 the 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 and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.</p>}},
  author       = {{Berchet, Antoine and Sollum, Espen and Thompson, Rona L. and Pison, Isabelle and Thanwerdas, Joël and Broquet, Grégoire and Chevallier, Frédéric and Aalto, Tuula and Berchet, Adrien and Bergamaschi, Peter and Brunner, Dominik and Engelen, Richard and Fortems-Cheiney, Audrey and Gerbig, Christoph and Groot Zwaaftink, Christine D. and Haussaire, Jean Matthieu and Henne, Stephan and Houweling, Sander and Karstens, Ute and Kutsch, Werner L. and Luijkx, Ingrid T. and Monteil, Guillaume and Palmer, Paul I. and Van Peet, Jacob C.A. and Peters, Wouter and Peylin, Philippe and Potier, Elise and Rödenbeck, Christian and Saunois, Marielle and Scholze, Marko and Tsuruta, Aki and Zhao, Yuanhong}},
  issn         = {{1991-959X}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{5331--5354}},
  publisher    = {{Copernicus GmbH}},
  series       = {{Geoscientific Model Development}},
  title        = {{The Community Inversion Framework v1.0 : A unified system for atmospheric inversion studies}},
  url          = {{http://dx.doi.org/10.5194/gmd-14-5331-2021}},
  doi          = {{10.5194/gmd-14-5331-2021}},
  volume       = {{14}},
  year         = {{2021}},
}