Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

Abstract. In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. 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 CH₄ volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH₄-N₂O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.