Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year 4.890
  • CiteScore value: 4.49 CiteScore 4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 51 Scimago H index 51
Volume 10, issue 3 | Copyright
Geosci. Model Dev., 10, 1199-1208, 2017
https://doi.org/10.5194/gmd-10-1199-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 17 Mar 2017

Methods for assessment of models | 17 Mar 2017

An alternative way to evaluate chemistry-transport model variability

Laurent Menut1, Sylvain Mailler1, Bertrand Bessagnet2, Guillaume Siour3, Augustin Colette2, Florian Couvidat2, and Frédérik Meleux2 Laurent Menut et al.
  • 1Laboratoire de Météorologie Dynamique, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Route de Saclay, 91128 Palaiseau, France
  • 2INERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550 Verneuil-en-Halatte, France
  • 3Laboratoire Inter-Universitaire des Systèmes Atmosphériques, UMR CNRS 7583, Université Paris Est Créteil et Université Paris Diderot, Institut Pierre Simon Laplace, Créteil, France

Abstract. A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

Download & links
Publications Copernicus
Download
Short summary
A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted.
A simple and complementary model evaluation technique for regional chemistry transport is...
Citation
Share