Yakov Livschitz

Ten years of GOME/ERS-2 total ozone data- : The new GOME data processor (GDP) version 4: 1. Algorithm description

The Global Ozone Monitoring Instrument (GOME) was launched on European Space Agencys ERS-2 platform in April 1995. The GOME data processor (GDP) operational retrieval algorithm has generated total ozone columns since July 1995. In 2004 the GDP system was given a major upgrade to version 4.0, a new validation was performed, and the 10-year GOME level 1 data record was reprocessed. In two papers, we describe the GDP 4.0 retrieval algorithm and present an error budget and sensitivity analysis (paper 1) and validation of the GDP total ozone product and the overall accuracy of the entire GOME ozone record (paper 2). GDP 4.0 uses an optimized differential optical absorption spectroscopy (DOAS) algorithm, with air mass factor (AMF) conversions calculated using the radiative transfer code linearized discrete ordinate radiative transfer (LIDORT). AMF computation is based on the TOMS version 8 ozone profile climatology, classified by total column, and AMFs are adjusted iteratively to reflect the DOAS slant column result. GDP 4.0 has improved wavelength calibration and reference spectra and includes a new molecular Ring correction to deal with distortion of ozone absorption features due to inelastic rotational Raman scattering effects. Preprocessing for cloud parameter estimation in GDP 4.0 is done using two new cloud correction algorithms: OCRA and ROCINN. For clear and cloudy scenes the precision of the ozone column product is better than 2.4 and 3.3%, respectively, for solar zenith angles up to 80°. Comparisons with ground-based data are generally at the 1-1.5% level or better for all regions outside the poles.

Cloud Properties Derived From GOME/ERS-2 Backscatter Data for Trace Gas Retrieval

We focus on the retrieval of cloud properties appropriate for trace gas retrieval from sun-normalized ultraviolet/visible backscatter spectra obtained from the Global Ozone Monitoring Experiment (GOME) onboard the European Space Agencys European Remote Sensing 2 Satellite (ERS-2). Retrieved quantities are the fractional cloud coverage of the GOME footprint, the cloud-top albedo, and the cloud-top height. A data fusion technique is applied to calculate the fractional cloud cover of GOME footprints from GOMEs polarization measurement devices. Furthermore, cloud-top albedo and cloud-top height are retrieved simultaneously from GOME measurements around the oxygen A-band by a neural network approach. We compare our results with corresponding results from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer onboard the first European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) METEOSAT Second Generation 1 geostationary spacecraft. Our analysis revealed that GOME-derived basic cloud properties are of remarkably high quality. GOME slightly underestimates the cloud coverage of footprints, which was expected since GOME is mainly sensitive to optically thick water clouds. GOME measurements are limited to the ultraviolet and visible part of the solar spectrum, which hampers the detection of optically thin clouds. For both the cloud-top height and the cloud-top albedo, we found a small bias relative to SEVIRI results. The overall uncertainty of retrieved total ozone columns with respect to cloud parameters is about 1%-2%. Our approach is applied to the operational processing of GOME/ERS-2 and will be applied to GOME-2/METOP (launched in 2006) in the framework of EUMETSATs Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF).