On the spatio-temporal representativeness of observations
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-08-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/17/9761/2017/acp-17-9761-2017.pdf |
| Summary: | 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 (PM<sub>2. 5</sub>,
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. |
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| ISSN: | 1680-7316 1680-7324 |