Articles | Volume 5, issue 9
Atmos. Meas. Tech., 5, 2169–2181, 2012

Special issue: GOME-2: calibration, algorithms, data products and...

Atmos. Meas. Tech., 5, 2169–2181, 2012

Research article 07 Sep 2012

Research article | 07 Sep 2012

Geophysical validation and long-term consistency between GOME-2/MetOp-A total ozone column and measurements from the sensors GOME/ERS-2, SCIAMACHY/ENVISAT and OMI/Aura

M. E. Koukouli1, D. S. Balis1, D. Loyola2, P. Valks2, W. Zimmer2, N. Hao2, J.-C. Lambert3, M. Van Roozendael3, C. Lerot3, and R. J. D. Spurr4 M. E. Koukouli et al.
  • 1Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 2Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Methodik der Fernerkundung (IMF), 82234 Oberpfaffenhofen, Germany
  • 3Belgian Institute for Space Aeronomy (BIRA-IASB), 3, Avenue Circulaire, 1180 Brussels, Belgium
  • 4RT-Solutions, Inc., Cambridge, MA, USA

Abstract. The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A [GOME-2] total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment [OMI] (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY [SCIAMACHY] (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors. In particular, on average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three datasets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both the GOME data processor [GDP] 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.