[1] The satellite instrument Global Ozone Monitoring Experiment (GOME), on board the ERS-2 mission of the European Space Agency, is measuring backscattered sunlight from the atmosphere in the range from 240 to 790 nm. This spectrum is used for deriving global, height-resolved information on the ozone distribution in the atmosphere. Contrary to total ozone column retrieval, the retrieval algorithm for ozone profiles requires absolutely calibrated reflectivity spectra. However, the in-flight calibration of the GOME reflectivity spectra needs to be corrected before the spectra can be used for profile retrieval. A general method for this calibration correction of satellite data and the profile retrieval method are described in this paper. The retrieved profiles from the recalibrated reflectivity spectra of GOME differ in the stratosphere by up to 50% from retrieved profiles without the correction. With the calibration correction, improved ozone profiles are retrieved for the complete range of 0-50 km. The GOME ozone profiles have been validated with ground and satellite measurements at a representative urban midlatitude and a rural tropical ground station.

SUMMARYNadir ozone profiles retrieved from the Global Ozone Monitoring Experiment (GOME) instrument are assimilated with a global three-dimensional (3D) atmospheric ozone model. The assimilation procedure is based on the Kalman filter equations, and is an extension of an existing assimilation procedure for total ozone columns. As a novelty, a 3D covariance model is developed using a single parametrization for correlations in all directions, instead of the usually applied separation in horizontal and vertical directions. The parametrization is anisotropic in all directions, accounting for the different correlation lengths of ozone with respect to altitude, latitude, and longitude. The assimilation procedure includes full use of the averaging kernel information provided with the GOME retrieval product. The averaging kernels account for the smaller sensitivity of the GOME instrument below the ozone maximum and the limited vertical resolution. A singular-value decomposition of the kernels is used to reduce the large data volume. A one-year dataset of GOME ozone profiles is assimilated for the year 2000. Independent data from ozonesondes are used to validate the results. A case-study shows that the assimilation of GOME profiles is able to improve the simulation of the vertical ozone distribution even in the case of strong vertical gradients.

[1] An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently, four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the optimal estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov regularization (Space Research Organization Netherlands), neural network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and data assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the Network for Detection of Stratospheric Change stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (Hawaii), Lauder (New Zealand), and Dumont d'Urville (Antarctic) for the years 1997-1999. In total, the comparison involves nearly 1000 ozone profiles and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15-48 km with a vertical resolution of 10 to 15 km, precision of 5-10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions.

Earthshine spectra measured by the nadir-viewing Global Ozone Monitoring Experiment (GOME) spectrometer aboard the second European Remote Sensing (ERS-2) Satellite in the range of 240-790 nm are widely used for the retrieval of concentrations and vertical profiles of atmospheric trace gases. For the near-real-time delivery of ozone columns and profiles at the Royal Netherlands Meterological Institute, a tailor-made wavelength calibration method was developed. The method use a high-resolution (0.01-nm) solar spectrum as the reference spectrum and applies both a shift and a squeeze to the wavelengths in selected windows to find the optimal wavelength grid per window. This method provides a calibration accuracy of 0.002 nm below and 0.001 nm above 290 nm. The new wavelength calibration method can be used on any wavelength window, for example, to improve the calibration of spectra from the GOME Data Processor. A software package, GomeCal, which performs this recalibration, along with an improved polarization and radiometric correction, has been made and has been released via the World Wide Web. The method can be used for any high-resolution (ir)radiance spectrometer, such as the satellite instruments SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), Ozone Monitoring Instrument, and GOME-2.

Global ozone profiles are derived from the ultraviolet and visible part of the spectra of the nadir‐viewing Global Ozone Monitoring Experiment (GOME), which is mounted on the polar orbiting second Earth Remote Sensing satellite (ERS‐2). These profiles need to be characterized, especially since the product includes a priori knowledge and so‐called averaging kernels. This additional information needs to be taken into account when comparing the profiles to correlative measurements. We perform an intercomparison between the ground‐based stratospheric lidar system in Lauder, New Zealand, and collocated GOME data. Here, the satellite profiles are retrieved with the algorithm of the Royal Netherlands Meteorological Institute (KNMI), which uses the optimal estimation method. In the comparison study significant differences are revealed which vary with season and altitude, indicating errors in the retrieval system. However, any quality assessment will just be one part of characterizing an ozone profile product that includes averaging kernels and a priori information. Data users need to be aware of the inherently complicated nature of such products that can only be fully understood when taking into account this additional information. In the second part of the study, the complex relation between the retrieved and the true profile is clarified using several interpretation tools. Applying these tools, we conclude that below 17‐km altitude the GOME profiles can only be used with appropriate use of averaging kernels (e.g., in data assimilation). Above 17 km up to 50 km the GOME spectra contain useful profile information, but the retrieved profiles have a moderate vertical resolution of about 11 km and contain a substantial fraction of a priori information of about 50%.