Imperial College London

DrEdwardGryspeerdt

Faculty of Natural SciencesThe Grantham Institute for Climate Change

Royal Society University Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 7900e.gryspeerdt Website

 
 
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Location

 

708Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Schutgens:2017:10.5194/acp-2017-149,
author = {Schutgens, N and Tsyro, S and Gryspeerdt, E and Goto, D and Weigum, N and Schulz, M and Stier, P},
doi = {10.5194/acp-2017-149},
journal = {Atmospheric Chemistry and Physics Discussions},
pages = {9761--9780},
title = {On the spatio-temporal representativeness of observations},
url = {http://dx.doi.org/10.5194/acp-2017-149},
volume = {17},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Schutgens,N
AU - Tsyro,S
AU - Gryspeerdt,E
AU - Goto,D
AU - Weigum,N
AU - Schulz,M
AU - Stier,P
DO - 10.5194/acp-2017-149
EP - 9780
PY - 2017///
SN - 1680-7367
SP - 9761
TI - On the spatio-temporal representativeness of observations
T2 - Atmospheric Chemistry and Physics Discussions
UR - http://dx.doi.org/10.5194/acp-2017-149
UR - http://hdl.handle.net/10044/1/49775
VL - 17
ER -