Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 4, 143-146, 2004
© Author(s) 2004. This work is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
31 Jan 2004
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 network trained with the latitude, pressure, time of year, and \methane\ 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.

Citation: Lary, D. J., Müller, M. D., and Mussa, H. Y.: Using neural networks to describe tracer correlations, Atmos. Chem. Phys., 4, 143-146, doi:10.5194/acp-4-143-2004, 2004.
Search ACP
Final Revised Paper
Discussion Paper