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

P. Sellitto, A. Burini, F. Del Frate, D. Solimini, S. Casadio

Abstract

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.

Index Terms - atmospheric composition atmospheric techniques geophysical signal processing inverse problems neural nets ozone time series troposphere ESA-ENVISAT SCIAMACHY Level 1b data.

This paper appears in: Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Publication Date: 23-28 July 2007
On page(s): 1685-1688
ISBN: 978-1-4244-1211-2
INSPEC Accession Number: 9890474
Digital Object Identifier: 10.1109/IGARSS.2007.4423141

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