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
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., 4, 143-146, doi:10.5194/acp-4-143-2004, 2004.