Using neural networks for GOME ozone retrieval - an overview

Martin Mueller

Atmospheric Chemistry & Dynamics Branch
NASA Goddard Space Flight Center

Abstract:
Neural network provide an interesting alternative to classical retrieval methods, because they are fast and accurate, and thoroughly exploit nonlinear correlations in the data. In a way they combine 4-dimensional statistical and physical information in a similar way assimilation does. Their application will be described for the case of ozone profile retrieval from the Global Ozone Monitoring Experiment (GOME), a UV spectrometer flying on ERS-2. The Neural Network Ozone Retrieval System (NNORSY) was trained on a data set of GOME radiances collocated with ozone measurements from ozonesondes, Halogen Occultation Experiment, Stratospheric Aerosol and Gas Experiment II, and Polar Ozone and Aerosol Measurement III. I will discuss results obtained from analyzing data from 1996 to 2001, error estimates gained from the neural network, sensitivity studies and possible combination with classical methods.