A. K. Kaifel

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[1] The inverse radiative transfer equation to retrieve atmospheric ozone distribution from the UV-visible satellite spectrometer Global Ozone Monitoring Experiment (GOME) has been modeled by means of a feed forward neural network. This Neural Network Ozone Retrieval System (NNORSY) was trained exclusively on a data set of GOME radiances collocated with(More)
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(More)
A new approach for retrieving total ozone from ERS2-GOME spectral data has been developed, which relies on feed-forward neural networks to perform the data inversion. Using selected GOME wavelength regions, instrument and geolocation data as an input, networks have been trained to determine atmospheric ozone in a one-step procedure. In order to train a(More)
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