Dedicated neural networks algorithms for direct estimation of tropospheric ozone from satellite measurements

In this paper we report on the design of a Neural Networks algorithm to retrieve tropospheric ozone information from satellite data. Following a combined radiative transfer model-extended pruning sensitivity analysis for input wavelengths selection, we first made an inversion exercise based on a synthetically produced radiance-tropospheric ozone concentrations database. Starting from the encouraging obtained results, we tested the Net on ESA-ENVISAT SCIAMACHY Level lb data. A time series of Tropospheric Ozone Columns on some midlatitude sites has been retrieved from the satellite measurements and then compared with collocated and simultaneous ozonesondes reference columns. The inversion results are presented and critically discussed.