Journal article
On the spatio-temporal representativeness of observations
- 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 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 6.8MB, Terms of use)
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- Publisher copy:
- 10.5194/acp-17-9761-2017
Authors
- Publisher:
- Copernicus Publications
- Journal:
- Atmospheric Chemistry and Physics More from this journal
- Volume:
- 17
- Issue:
- 16
- Pages:
- 9761-9780
- Publication date:
- 2017-08-21
- Acceptance date:
- 2017-07-19
- DOI:
- EISSN:
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1680-7324
- Language:
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English
- Keywords:
- Pubs id:
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1111208
- Local pid:
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pubs:1111208
- Deposit date:
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2020-10-29
Terms of use
- Copyright holder:
- Schutgens et al.
- Copyright date:
- 2017
- Rights statement:
- ©2017 Author(s). This work is distributed under the Creative Commons Attribution 3.0 License.
- Licence:
- CC Attribution (CC BY)
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