Atmospheric Chemistry and Physics www.atmos-chem-phys.net 1680-7316 1680-7324 4 1 2004 10.5194/acp-4-143-2004 http://www.atmos-chem-phys.net/4/143/2004/ http://www.atmos-chem-phys.net/4/143/2004/acp-4-143-2004.html http://www.atmos-chem-phys.net/4/143/2004/acp-4-143-2004.pdf 143 146 2004-01-31 Using neural networks to describe tracer correlations D. J. Lary M. D. Müller H. Y. Mussa Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, USA GEST at the University of Maryland Baltimore County, MD, USA Unilever Cambridge Centre, Department of Chemistry, University of Cambridge, UK National Research Council, Washington DC, USA 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_4  (but not N₂O) from 1991 till the present. The neural network Fortran code used is available for download.