Title :
Automatic selection by means of neural networks of GOME optimum spectral channels for the retrieval of ozone vertical profiles
Author_Institution :
Univ. Tor Vergata, Rome, Italy
Abstract :
A neural network procedure aiming at the retrieval of ozone concentration profiles from the radiance measurements provided by the instrument GOME on board of ESA satellite ERS-2 is described. The potentiality of neural networks has been exploited not only for the inversion purposes but also for sensitivity analysis. In fact, via electromagnetic modelling and pruning algorithms, a complete procedure has been designed to extract the GOME channels most crucial for the inversion. The effectiveness of the retrieval algorithm has been evaluated comparing its performance to that yielded by other instruments and inversion techniques.
Keywords :
atmospheric chemistry; atmospheric composition; atmospheric radiation; atmospheric spectra; atmospheric techniques; data acquisition; ozone; remote sensing; spectral analysis; ESA satellite ERS-2; GOME channels extraction; GOME optimum spectral channels; O3; automatic selection; electromagnetic modelling; inversion purposes; inversion techniques; neural networks; ozone concentration profiles; ozone vertical profiles retrieval; pruning algorithms; radiance measurements; retrieval algorithm; sensitivity analysis; Algorithm design and analysis; Atmosphere; Atmospheric measurements; Information retrieval; Instruments; Neural networks; Remote monitoring; Satellites; Sensitivity analysis; Testing;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1293772