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| Titel |
Automated quality control methods for sensor data: a novel observatory approach |
| VerfasserIn |
J. R. Taylor, H. L. Loescher |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 7 ; Nr. 10, no. 7 (2013-07-24), S.4957-4971 |
| Datensatznummer |
250018355
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| Publikation (Nr.) |
 copernicus.org/bg-10-4957-2013.pdf |
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| Zusammenfassung |
| National and international networks and observatories of terrestrial-based
sensors are emerging rapidly. As such, there is demand for a standardized
approach to data quality control, as well as interoperability of data among
sensor networks. The National Ecological Observatory Network (NEON) has begun
constructing their first terrestrial observing sites, with 60 locations
expected to be distributed across the US by 2017. This will result in over
14 000 automated sensors recording more than > 100 Tb of data per year.
These data are then used to create other datasets and subsequent
"higher-level" data products. In anticipation of this challenge, an overall
data quality assurance plan has been developed and the first suite of data
quality control measures defined. This data-driven approach focuses on
automated methods for defining a suite of plausibility test parameter
thresholds. Specifically, these plausibility tests scrutinize the data range
and variance of each measurement type by employing a suite of binary checks.
The statistical basis for each of these tests is developed, and the methods
for calculating test parameter thresholds are explored here. While these
tests have been used elsewhere, we apply them in a novel approach by
calculating their relevant test parameter thresholds. Finally, implementing
automated quality control is demonstrated with preliminary data from a NEON
prototype site. |
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