On the spatio-temporal representativeness of observations
- 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 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 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.