OZONE FROM GOME DATA USING NEURAL NETWORK TECHNIQUE

Since the launch of GOME in 1995, quite a number of physical ozone retrieval algorithms have been devised [1, 2], which rely on Differential Absorption Spectroscopy (DOAS) for determining the ozone slant column [3, 4]. These columns are then converted to total ozone values by means of an air mass factor (AMF) estimated from a combination of climatology and satellite position relative to sun and earth. Although this method has proved to be reliable and yields good results in most geographical regions, recent research has shown it to be somewhat unstable in the case of large solar zenith angles [5, 6]. We therefore present the first results of a new alternative approach to ozone retrieval, which relies on neural networks to exploit the information contained in GOME spectra. As this method is very flexible, it can also be used for retrieving vertical ozone profiles. Current algorithms are commonly based on the work of Rodgers [7, 8], and retrieve the ozone by optimal estimation [see e. g. 9, 10]. This method is however computationally expensive, especially in the case of clouds, which have to be implemented in the forward model used. Furthermore, it requires the use and upkeep of a climatological a priori database with accurately known uncertainities, since these have a direct impact on the retrieved ozone profiles. A neural network, on the other hand, should be able to extract climatological and error information from its training data set, and thus does not require the explicit use of a priori ozone profiles.