Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
21 Mar 2017
Review status
A revision of this discussion paper was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.
On the spatio-temporal representativeness of observations
Nick Schutgens1, Svetlana Tsyro2, Ed Gryspeerdt3,a, Daisuke Goto4, Natalie Weigum1, Michael Schulz2, and Philip Stier1 1Department of Physics, University of Oxford, Parks road, OX1 3PU, England
2Norwegian Meteorological Institute, P.O.Box 43 Blindern, Oslo, NO-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: Space and Atmospheric Physics Group, Imperial College London, London, SW7 2AJ, United Kingdom
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 time and length-scales (semi-annually down to sub-daily, 300 to 50 km) 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 remote sensing or in-situ (PM2.5, black carbon mass or number concentrations), satellite remote sensing with imagers or LIDARs (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 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. Temporal collocation of data (only possible in the context of evaluating model data with observations) can be very effective at reducing representation errors even when spatial sampling issues remain (e.g. when using ground-sites). 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.

Citation: Schutgens, N., Tsyro, S., Gryspeerdt, E., Goto, D., Weigum, N., Schulz, M., and Stier, P.: On the spatio-temporal representativeness of observations, Atmos. Chem. Phys. Discuss.,, in review, 2017.
Nick Schutgens et al.
Nick Schutgens et al.
Nick Schutgens et al.


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Short summary
Using high-resolution simulations, we estimate representativeness errors on time-scales 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 LIDARs, etc) and show how to reduce them. This study is relevant to the use of observations in constructing satellite L3 products, observational inter comparison and model evaluation.
Using high-resolution simulations, we estimate representativeness errors on time-scales of hours...