On the spatio-temporal representativeness of observations

Journal article

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.

Authors: Schutgens, N; Tsyro, S; Gryspeerdt, E; Goto, D; Weigum, N

• Publication status: Published
• Peer review status: Not peer reviewed
• Publisher: European Geosciences Union (EGU)
• Journal: Atmospheric Chemistry and Physics Discussions
• Volume: 17
• Issue: 16
• Pages: 9761–9780
• Publication date: 2017-03-21
• Acceptance date: 2017-03-14
• DOI: 10.5194/acp-2017-149
• ISSN: 1680-7375
• Language: English