Tropospheric Ozone Column Retrieval From ESA-Envisat SCIAMACHY Nadir UV/VIS Radiance Measurements by Means of a Neural Network Algorithm

Abstract

Spaceborne measurements may significantly support monitoring the concentration of atmospheric constituents affecting air quality, such as ozone. However, retrieving tropospheric ozone concentration information from nadir satellite data is an arduous task, given the weak sensitivity of the earth's radiance to ozone variations in the lower part of the atmosphere. We propose a new methodology, based on neural networks (NN), for retrieving the tropospheric ozone column from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) nadir UV/VIS measurements. The design of the NN algorithm is based on an analysis of the information content of measurements in both UV and VIS bands, carried out by a combined radiative transfer model and NN extended pruning procedure. The NN was trained and tested with simulated data and with matching World Ozone and Ultraviolet radiation Data Centre ozonesonde data sets and validated by independent data taken over two test sites. A significant improvement of the retrieval capabilities is observed when VIS wavelengths are included into the input vector. Finally, an example of tropospheric ozone map generated automatically by the methodology at a continental scale is provided and critically discussed.