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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 16 | Copyright
Atmos. Chem. Phys., 17, 9761-9780, 2017
https://doi.org/10.5194/acp-17-9761-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Aug 2017

Research article | 21 Aug 2017

On the spatio-temporal representativeness of observations

Nick Schutgens1,a, Svetlana Tsyro2, Edward Gryspeerdt3,b, Daisuke Goto4, Natalie Weigum1, Michael Schulz2, and Philip Stier1 Nick Schutgens et al.
  • 1Department of Physics, University of Oxford, Parks road, OX1 3PU, UK
  • 2Norwegian Meteorological Institute, P.O. Box 43 Blindern, Oslo, 0312, Norway
  • 3Institute for Meteorology, Universität Leipzig, Stephanstr. 3, 04103 Leipzig, Germany
  • 4National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506, Japan
  • anow at: Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
  • bnow at: Space and Atmospheric Physics Group, Imperial College London, London, SW7 2AJ, UK

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

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We estimate representativeness errors in observations due to mismatching spatio-temporal sampling, on timescales of hours to a year and length scales of 50 to 200 km, for a variety of observing systems (in situ or remote sensing ground sites, satellites with imagers or lidar, etc.) and develop strategies to reduce them. This study is relevant to the use of observations in constructing satellite L3 products, observational intercomparison and model evaluation.
We estimate representativeness errors in observations due to mismatching spatio-temporal...
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