Atmospheric Chemistry and Physics Discussions www.atmos-chem-phys-discuss.net 1680-7367 1680-7375 3 6 2003 10.5194/acpd-3-5711-2003 http://www.atmos-chem-phys-discuss.net/3/5711/2003/ http://www.atmos-chem-phys-discuss.net/3/5711/2003/acpd-3-5711-2003.html http://www.atmos-chem-phys-discuss.net/3/5711/2003/acpd-3-5711-2003.pdf 5711 5724 2003-11-13 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 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