Harmonisation of the GOME, SCIAMACHY, and GOME-2 total ozone data records for a better understanding of long-term trends and their causes

This thesis addresses the issue of the accurate measurements of ozone distributions in the atmosphere obtained from different satellite borne atmospheric chemistry spectrometers which represent a major need and pre-requisite for determining whether the atmospheric burden of ozone depleting substances (ODS) are reduced in accordance to the Montreal Protocol, and valuable for long-term trend analysis to detect a subsequent ozone recovery. A consolidated and homogeneous long term dataset requires a careful analysis of the relevant parameters used in the retrievals, one important parameter is the absorption cross section. This work presents the procedures followed to correct the ozone cross section data of SCIAMACHY and GOME-2 spectrometers starting from original raw data (optical density spectra). Using the available versions of SCIAMACHY and GOME-2 FM cross sections in the retrieval of total ozone from each satellite leads to an overestimation in the total ozone by 3-5% and 8-9% compared to collocated GOME data, respectively. The quality of the revised temperature-dependent ozone absorption cross sections is investigated over GOME-2 and SCIAMACHY's entire spectral range. The revised data agree well within 3% with other published ozone cross sections and preserve the correct temperature dependence in the Hartley, Huggins, Chappuis and Wulf bands as displayed by the literature data. SCIAMACHY's total ozone columns retrieved using the revised cross section data are shown to be within 1% compared to the ozone amounts retrieved routinely from SCIAMACHY, which uses Bogumil et al. (2003) data but adjusted with a scaling factor of 5.3% and a wavelength shift of 0.08 nm. The total ozone column retrieved from the GOME-2/MetOp-A satellite using the new cross section data is within 1% compared to the ozone amounts retrieved from the standard retrieval performed for GOME-2. The study also presents a long term statistical trend analysis of total ozone datasets obtained from various