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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 1
Atmos. Chem. Phys., 4, 143–146, 2004
https://doi.org/10.5194/acp-4-143-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Atmos. Chem. Phys., 4, 143–146, 2004
https://doi.org/10.5194/acp-4-143-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Jan 2004

31 Jan 2004

Using neural networks to describe tracer correlations

D. J. Lary1,2,3, M. D. Müller1,4, and H. Y. Mussa3 D. J. Lary et al.
  • 1Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, USA
  • 2GEST at the University of Maryland Baltimore County, MD, USA
  • 3Unilever Cambridge Centre, Department of Chemistry, University of Cambridge, UK
  • 4National Research Council, Washington DC, USA

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

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