Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Volume 9, issue 1
Geosci. Model Dev., 9, 393–412, 2016
https://doi.org/10.5194/gmd-9-393-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Coupled chemistry–meteorology modelling: status and...

Geosci. Model Dev., 9, 393–412, 2016
https://doi.org/10.5194/gmd-9-393-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model experiment description paper 29 Jan 2016

Model experiment description paper | 29 Jan 2016

A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0)

J.-M. Haussaire and M. Bocquet

Related authors

On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020,https://doi.org/10.5194/gmd-13-1903-2020, 2020
Short summary
Diagnosing spatial error structures in CO2 mole fractions and XCO2 column mole fractions from atmospheric transport
Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez
Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019,https://doi.org/10.5194/acp-19-12007-2019, 2019
Short summary
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019,https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136,https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary
Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model
Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis
Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019,https://doi.org/10.5194/acp-19-5695-2019, 2019
Short summary

Related subject area

Numerical Methods
Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
Jouni Susiluoto, Alessio Spantini, Heikki Haario, Teemu Härkönen, and Youssef Marzouk
Geosci. Model Dev., 13, 3439–3463, https://doi.org/10.5194/gmd-13-3439-2020,https://doi.org/10.5194/gmd-13-3439-2020, 2020
Short summary
PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations
Olivier Pannekoucke and Ronan Fablet
Geosci. Model Dev., 13, 3373–3382, https://doi.org/10.5194/gmd-13-3373-2020,https://doi.org/10.5194/gmd-13-3373-2020, 2020
Short summary
Simple algorithms to compute meridional overturning and barotropic streamfunctions on unstructured meshes
Dmitry Sidorenko, Sergey Danilov, Nikolay Koldunov, Patrick Scholz, and Qiang Wang
Geosci. Model Dev., 13, 3337–3345, https://doi.org/10.5194/gmd-13-3337-2020,https://doi.org/10.5194/gmd-13-3337-2020, 2020
Short summary
Development of a two-way-coupled ocean–wave model: assessment on a global NEMO(v3.6)–WW3(v6.02) coupled configuration
Xavier Couvelard, Florian Lemarié, Guillaume Samson, Jean-Luc Redelsperger, Fabrice Ardhuin, Rachid Benshila, and Gurvan Madec
Geosci. Model Dev., 13, 3067–3090, https://doi.org/10.5194/gmd-13-3067-2020,https://doi.org/10.5194/gmd-13-3067-2020, 2020
Short summary
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
Rohitash Chandra, Danial Azam, Arpit Kapoor, and R. Dietmar Müller
Geosci. Model Dev., 13, 2959–2979, https://doi.org/10.5194/gmd-13-2959-2020,https://doi.org/10.5194/gmd-13-2959-2020, 2020
Short summary

Cited articles

Azzi, M., Johnson, G. M., and Cope, M.: An introduction to the generic reaction set photochemical smog mechanism, Proceedings of the Eleventh International Conference of the Clean Air Society of Australia and New Zealand, 5–10 July 1992, Brisbane, Qld., Australia, 451–462, 1992.
Bocquet, M.: Reconstruction of an atmospheric tracer source using the principle of maximum entropy, I: Theory, Q. J. Roy. Meteor. Soc., 131, 2191–2208, https://doi.org/10.1256/qj.04.67, 2005.
Bocquet, M.: Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18, 735–750, https://doi.org/10.5194/npg-18-735-2011, 2011.
Bocquet, M.: Parameter field estimation for atmospheric dispersion: Application to the Chernobyl accident using 4D-Var, Q. J. Roy. Meteor. Soc., 138, 664–681, https://doi.org/10.1002/qj.961, 2012.
Bocquet, M. and Sakov, P.: Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems, Nonlin. Processes Geophys., 19, 383–399, https://doi.org/10.5194/npg-19-383-2012, 2012.
Publications Copernicus
Download
Short summary
The focus is on the development of low-order models of atmospheric transport and chemistry and their use for data assimilation purposes. A new low-order coupled chemistry meteorology model is developed. It consists of the Lorenz40-variable model used as a wind field coupled with a simple ozone photochemistry module. Advanced ensemble variational methods are applied to this model to obtain insights on the use of data assimilation with coupled models, in an offline mode or in an online mode.
The focus is on the development of low-order models of atmospheric transport and chemistry and...
Citation