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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Jul 2018

Research article | 12 Jul 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

On sampling bias adjustment for sparsely observing satellite instruments for the example of carbonyl sulfide (OCS)

Corinna Kloss1,2, Marc von Hobe1, Michael Höpfner3, Kaley A. Walker4, Martin Riese1, Jörn Ungermann1, Birgit Hassler5, Stefanie Kremser6, and Greg E. Bodeker6 Corinna Kloss et al.
  • 1Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research (IEK - 7), Jülich, Germany
  • 2Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC 2E), Université d'Orléans, CNRS, Orléans, France
  • 3Karlsruhe Institut of Technology, Institute of Meteorology and Climate research, Karlsruhe, Germany
  • 4University of Toronto, Department of Physics, Toronto, Ontario, Canada
  • 5Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 6Bodeker Scientific, Alexandra, New Zealand

Abstract. When computing climatological averages of atmospheric trace gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Complete homogeneous spatio-temporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbits and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform 15 Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional observations other than the satellite data product itself and is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite data. In a first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor, 20 which is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen spatio-temporal frame is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30° zones, the maximum spatio-temporal sampling bias adjustment was 11% for OCS mixing ratios at 16km and 5% for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and sign of the adjustments were in agreement with the adjustment of the ACE-FTS data.

Corinna Kloss et al.
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Status: final response (author comments only)
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Corinna Kloss et al.
Corinna Kloss et al.
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Publications Copernicus
Short summary
Are regional and seasonal averages from only a few satellite measurements, all aligned along a specific path, representative? Probably not. We present a method to adjust for the so-called "sampling bias" and investigate the influence on derived long term trends. The method is illustrated and validated for a long-lived trace gas (Carbonyl Sulfide), and it is shown that the influence of the sampling bias is too small to change scientific conclusions on long term trends.
Are regional and seasonal averages from only a few satellite measurements, all aligned along a...