Atmos. Chem. Phys. Discuss., 3, 5711-5724, 2003
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Using neural networks to describe tracer correlations
D. J. Lary1,2,3, M .D. Müller1,4, and H. Y. Mussa3
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 5 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 co-efficient 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 10 dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4 (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download

Citation: Lary, D. J., Müller, M .D., and Mussa, H. Y.: Using neural networks to describe tracer correlations, Atmos. Chem. Phys. Discuss., 3, 5711-5724, doi:10.5194/acpd-3-5711-2003, 2003.
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