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 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)
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)
Since the launch of GOME in 1995, quite a number of physical ozone retrieval algorithms have been devised [1, 2], which rely on Differential Absorption Spectroscopy (DOAS) for determining the ozone slant column [3, 4]. These columns are then converted to total ozone values by means of an air mass factor (AMF) estimated from a combination of climatology and(More)
The recently published backpercolation algorithm for the training of neural networks will be compared with the backpropagation and quickpropagation algorithm by means of "artificial" classification problems (e.g. XOR, M-N-M decoder) and serveral others. Within all classification schemes the backpercolation algorithm is much more efficent and even successful(More)
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