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| Titel |
Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites |
| VerfasserIn |
F. Madonna, M. Rosoldi, J. Güldner, A. Haefele, R. Kivi, M. P. Cadeddu, D. Sisterson, G. Pappalardo |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 11 ; Nr. 7, no. 11 (2014-11-19), S.3813-3823 |
| Datensatznummer |
250115953
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| Publikation (Nr.) |
 copernicus.org/amt-7-3813-2014.pdf |
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| Zusammenfassung |
| The potential for measurement redundancy to reduce uncertainty in atmospheric
variables has not been investigated comprehensively for climate observations.
We evaluated the usefulness of entropy and mutual correlation concepts, as
defined in information theory, for quantifying random uncertainty and
redundancy in time series of the integrated water vapour (IWV) and water vapour
mixing ratio profiles provided by five highly instrumented GRUAN (GCOS,
Global Climate Observing System, Reference Upper-Air Network) stations in
2010–2012. Results show that the random uncertainties on the IWV measured
with radiosondes, global positioning system, microwave and infrared
radiometers, and Raman lidar measurements differed by less than 8%.
Comparisons of time series of IWV content from ground-based remote sensing
instruments with in situ soundings showed that microwave radiometers have the
highest redundancy with the IWV time series measured by radiosondes and
therefore the highest potential to reduce the random uncertainty of the
radiosondes time series. Moreover, the random uncertainty of a time series
from one instrument can be reduced by ~ 60% by constraining the
measurements with those from another instrument. The best reduction of random
uncertainty is achieved by conditioning Raman lidar measurements with
microwave radiometer measurements. Specific instruments are recommended for
atmospheric water vapour measurements at GRUAN sites. This approach can be
applied to the study of redundant measurements for other climate variables. |
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