Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5331-2021
https://doi.org/10.5194/gmd-14-5331-2021
Model description paper
 | 
26 Aug 2021
Model description paper |  | 26 Aug 2021

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

Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao

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Cited articles

Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. a
Ars, S., Broquet, G., Yver Kwok, C., Roustan, Y., Wu, L., Arzoumanian, E., and Bousquet, P.: Statistical atmospheric inversion of local gas emissions by coupling the tracer release technique and local-scale transport modelling: a test case with controlled methane emissions, Atmos. Meas. Tech., 10, 5017–5037, https://doi.org/10.5194/amt-10-5017-2017, 2017. a
Asefi‐Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., Huang, J., Elvidge, C., and Baugh, K.: A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results, J. Geophys. Res.-Atmos., 119, 10213–10231, https://doi.org/10.1002/2013JD021296, 2014. a
Babenhauserheide, A., Basu, S., Houweling, S., Peters, W., and Butz, A.: Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO2 surface flux inversions, Atmos. Chem. Phys., 15, 9747–9763, https://doi.org/10.5194/acp-15-9747-2015, 2015. a, b
Bagley, J. E., Jeong, S., Cui, X., Newman, S., Zhang, J., Priest, C., Campos-Pineda, M., Andrews, A. E., Bianco, L., Lloyd, M., Lareau, N., Clements, C., and Fischer, M. L.: Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions, J. Geophys. Res.-Atmos., 122, 1901–1918, https://doi.org/10.1002/2016JD025361, 2017. a
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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 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.