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| Titel |
Techniques for analyses of trends in GRUAN data |
| VerfasserIn |
G. E. Bodeker, S. Kremser |
| 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 ; 8, no. 4 ; Nr. 8, no. 4 (2015-04-08), S.1673-1684 |
| Datensatznummer |
250116293
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| Publikation (Nr.) |
 copernicus.org/amt-8-1673-2015.pdf |
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| Zusammenfassung |
| The Global Climate Observing System (GCOS) Reference Upper Air Network
(GRUAN) provides reference quality RS92 radiosonde measurements of
temperature, pressure and humidity. A key attribute of reference quality
measurements, and hence GRUAN data, is that each datum has a well
characterized and traceable estimate of the measurement uncertainty. The
long-term homogeneity of the measurement records, and their well
characterized uncertainties, make these data suitable for reliably detecting
changes in global and regional climate on decadal time scales. Considerable
effort is invested in GRUAN operations to (i) describe and analyse all
sources of measurement uncertainty to the extent possible, (ii) quantify and
synthesize the contribution of each source of uncertainty to the total
measurement uncertainty, and (iii) verify that the evaluated net uncertainty
is within the required target uncertainty. However, if the climate science
community is not sufficiently well informed on how to capitalize on this
added value, the significant investment in estimating meaningful measurement
uncertainties is largely wasted. This paper presents and discusses the
techniques that will need to be employed to reliably quantify long-term
trends in GRUAN data records. A pedagogical approach is taken whereby
numerical recipes for key parts of the trend analysis process are explored.
The paper discusses the construction of linear least squares regression
models for trend analysis, boot-strapping approaches to determine
uncertainties in trends, dealing with the combined effects of autocorrelation
in the data and measurement uncertainties in calculating the uncertainty on
trends, best practice for determining seasonality in trends, how to deal with
co-linear basis functions, and interpreting derived trends. Synthetic data
sets are used to demonstrate these concepts which are then applied to a first
analysis of temperature trends in RS92 radiosonde upper air soundings at the
GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E). |
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