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DESIGN OF NEURAL NETWORKS ALGORITHMS FOR THE RETRIEVAL OF TROPOSPHERIC OZONE FROM SATELLITE DATA

This paper reports on the development of Neural Networks algorithms for tropospheric ozone retrieval from ESA-Envisat SCIAMACHY measurements. We present and discuss some retrieval experiments from UV/VIS simulated data, based upon a combined sensitivity analysis performed with the aid of the UVSPEC radiative transfer model and a neural Extended Pruning procedure. In particular, the role of UV and VIS information budget is here exploited and critically discussed.