Articles | Volume 10, issue 23
Atmos. Chem. Phys., 10, 11839–11849, 2010
https://doi.org/10.5194/acp-10-11839-2010

Special issue: GOMOS (Global Ozone Monitoring by Occultation of Stars): data...

Atmos. Chem. Phys., 10, 11839–11849, 2010
https://doi.org/10.5194/acp-10-11839-2010

  13 Dec 2010

13 Dec 2010

Mid-latitude ozone monitoring with the GOMOS-ENVISAT experiment version 5: the noise issue

P. Keckhut1, A. Hauchecorne1, L. Blanot2, K. Hocke3,4, S. Godin-Beekmann1, J.-L. Bertaux1, G. Barrot2, E. Kyrölä5, J. A. E. van Gijsel6, and A. Pazmino1 P. Keckhut et al.
  • 1LATMOS-IPSL, CNRS/INSU, UMR 8190, UVSQ, UPMC, Guyancourt, France
  • 2ACRI-ST, Sophia Antipolis, Sophia Antipolis, France
  • 3Institute of Applied Physics, University of Bern, Bern, Switzerland
  • 4Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 5FMI, Helsinki, Finland
  • 6RIVM, Bilthoven, The Netherlands

Abstract. The GOMOS ozone profiles have been analysed to evaluate the GOMOS ability to capture the long-term ozone evolution at mid-latitudes during the expected recovery phase of the ozone layer. Version 5 of the operational GOMOS ozone data has been compared with data from two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Comparisons between ground-based and GOMOS data confirm the occurrence of spurious retrievals mainly occurring since 2006. Using a selected set of data it is shown that some bad retrievals are induced by the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not only induce a continuous bias, but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analyses it is recommended filtering the data according to background light conditions and star temperature (spectrum shape). The new method of the dark charge estimate proposed to be implemented in the version 6 of the ESA algorithm seems to significantly reduce the occurrence of such effects and should allow to monitor stratospheric ozone using GOMOS data with greater confidence.

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