Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 19
Articles | Volume 19, issue 19
https://doi.org/10.5194/gmd-19-9751-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-9751-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 19
Model description paper
 | 
12 Oct 2026
Model description paper |  | 12 Oct 2026

EnviFlux (v1.0): a simplified surface flux inversion tool based on four-dimensional variational data assimilation (4D-Var)

Ross N. Bannister

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Cited articles

Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019. a
An, X., Henne, S., Yao, B., Vollmer, M. K., Zhou, L., and Li, Y.: Estimating emissions of HCFC-22 and CFC-11 in China by atmospheric observations and inverse modeling, Sci. China Ser. C, 55, 2233–2241, 2012. a, b
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, https://doi.org/10.1002/qj.339, 2008a. a
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteor. Soc., 134, 1971–1996, 2008b. a, b
Bannister, R. N.: Issues in the assimilation of the sources of a trace gas by inverse modelling, Comput. Fluids, 46, 106–112, 2011. a
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EnviFlux is a low-cost data assimilation tool to estimate trace gas fluxes at the Earth's surface. Data assimilation ideas are often first studied with "toy models", but none exists for flux estimation, hence the purpose of EnviFlux.

EnviFlux is described, and then used to show how model error and bias can influence the inferred surface flux features. This is done in two scenarios, one with no prior knowledge of a source/sink pair, and another with prior knowledge in a more realistic situation.

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