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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 3, issue 6
Atmos. Meas. Tech., 3, 1555–1568, 2010
https://doi.org/10.5194/amt-3-1555-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 3, 1555–1568, 2010
https://doi.org/10.5194/amt-3-1555-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 Nov 2010

12 Nov 2010

Improving ozone profile retrieval from spaceborne UV backscatter spectrometers using convergence behaviour diagnostics

B. Mijling, O. N. E. Tuinder, R. F. van Oss, and R. J. van der A B. Mijling et al.
  • Royal Netherlands Meteorological Institute (KNMI), Postbus 201, 3730-AE De Bilt, The Netherlands

Abstract. The Ozone Profile Algorithm (OPERA), developed at KNMI, retrieves the vertical ozone distribution from nadir spectral satellite measurements of back scattered sunlight in the ultraviolet and visible wavelength range. To produce consistent global datasets the algorithm needs to have good global performance, while short computation time facilitates the use of the algorithm in near real time applications.

To test the global performance of the algorithm we look at the convergence behaviour as diagnostic tool of the ozone profile retrievals from the GOME instrument (on board ERS-2) for February and October 1998. In this way, we uncover different classes of retrieval problems, related to the South Atlantic Anomaly, low cloud fractions over deserts, desert dust outflow over the ocean, and the intertropical convergence zone. The influence of the first guess and the external input data including the ozone cross-sections and the ozone climatologies on the retrieval performance is also investigated. By using a priori ozone profiles which are selected on the expected total ozone column, retrieval problems due to anomalous ozone distributions (such as in the ozone hole) can be avoided.

By applying the algorithm adaptations the convergence statistics improve considerably, not only increasing the number of successful retrievals, but also reducing the average computation time, due to less iteration steps per retrieval. For February 1998, non-convergence was brought down from 10.7% to 2.1%, while the mean number of iteration steps (which dominates the computational time) dropped 26% from 5.11 to 3.79.

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