On the spatio-temporal representativeness of observations
File(s)
Published version
Author(s)
Type
Journal Article
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 spatiotemporal
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 wideswath
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.
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 spatiotemporal
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 wideswath
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.
Date Issued
2017-08-21
Date Acceptance
2017-07-19
Citation
Atmospheric Chemistry and Physics Discussions, 2017, 17 (16), pp.9761-9780
ISSN
1680-7367
Publisher
European Geosciences Union
Start Page
9761
End Page
9780
Journal / Book Title
Atmospheric Chemistry and Physics Discussions
Volume
17
Issue
16
Subjects
Meteorology & Atmospheric Sciences
Publication Status
Published