A novel approach to retrieving total ozone columns from the ERS2 GOME ͑Global Ozone Monitoring Experiment͒ spectral data has been developed. With selected GOME wavelength regions, from clear and cloudy pixels alike plus orbital and instrument data as input, a feed-forward neural network was trained to determine total ozone in a one-step inverse retrieval procedure. To achieve this training, ground-based total ozone measurements from the World Ozone and Ultraviolet Data Center ͑WOUDC͒ for the

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... rs 1996 -2000, supplemented with Dobson-corrected Total Ozone Mapping Spectrometer ͑TOMS͒ data to provide global coverage, were collocated with GOME ground pixels into a training data set. Validation of the neural-network-retrieved ozone values relative to independent ground stations yielded a rms error of better than 11 Dobson units. Comparisons performed on the basis of operationally available TOMS and GOME level-3 maps exhibit good agreement in general, with a latitude-dependent offset. © 2002 Optical Society of America OCIS codes: 010.4950, 280.0280. M. D. Mü ller ͑martin.mueller@zsw-bw.de͒ and A. Kaifel are with the Center for Solar Energy and Hydrogen Research, Stuttgart 70565, Germany. M. Weber and J. P. Burrows are with the Institute