On the spatio-temporal representativeness of observations - Schutgens,N; Tsyro,S; Gryspeerdt,E; Goto,D; Weigum,N; Schulz,M; Stier,P (Atmospheric Chemistry and Physics Discussions, 2017)
The discontinuous spatio-temporal sampling ofobservations has an impact when using them to construct climatologiesor evaluate models. Here we provide estimates ofthis so-called representation error for a range of timescalesand length scales (semi-annually down to sub-daily, 300 to50 km) and show that even after substantial averaging of datasignificant representation errors may remain, larger than typicalmeasurement errors. Our study considers a variety ofobservations: ground-site or in situ remote sensing (PM2.5,black carbon mass or number concentrations), satellite remotesensing with imagers or lidar (extinction). We show thatobservational coverage (a measure of how dense the spatiotemporalsampling of the observations is) is not an effectivemetric to limit representation errors. Different strategiesto construct monthly gridded satellite L3 data are assessedand temporal averaging of spatially aggregated observations(super-observations) is found to be the best, although it stillallows for significant representation errors. However, temporalcollocation of data (possible when observations are comparedto model data or other observations), combined withtemporal averaging, can be very effective at reducing representationerrors. We also show that ground-based and wideswathimager satellite remote sensing data give rise to similarrepresentation errors, although their observational samplingis different. Finally, emission sources and orographycan lead to representation errors that are very hard to reduce,even with substantial temporal averaging.