Spectral radiative-transfer modeling with minimized computation time by use of a neural-network technique.

A new approach based on a neural-network technique for reduction in the computation time of radiative-transfer models is presented. This approach gives high spectral resolution without significant loss of accuracy. A rigorous radiative-transfer model is used to calculate radiation values at a few selected wavelengths, and a neural-network algorithm replenishes them to a complete spectrum with radiation values at a high spectral resolution.

Neural network scheme for the retrieval of total ozone from Global Ozone Monitoring Experiment data.

A novel approach to retrieving total ozone columns from the ERS2 GOME (Global Ozone Monitoring Experiment) 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 (WOUDC) for the years 1996-2000, supplemented with Dobson-corrected Total Ozone Mapping Spectrometer (TOMS) data to provide global coverage, were collocated with GOME ground pixels into a training data set.