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| Titel |
Statistical modelling of collocation uncertainty in atmospheric thermodynamic profiles |
| VerfasserIn |
A. Fassò, R. Ignaccolo, F. Madonna, B. B. Demoz, M. Franco-Villoria |
| 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. 6 ; Nr. 7, no. 6 (2014-06-23), S.1803-1816 |
| Datensatznummer |
250115825
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| Publikation (Nr.) |
 copernicus.org/amt-7-1803-2014.pdf |
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| Zusammenfassung |
The quantification of measurement uncertainty of atmospheric parameters is a
key factor in assessing the uncertainty of global change estimates given by
numerical prediction models. One of the critical contributions to the
uncertainty budget is related to the collocation mismatch in space and time
among observations made at different locations. This is particularly
important for vertical atmospheric profiles obtained by radiosondes or lidar.
In this paper we propose a statistical modelling approach capable of
explaining the relationship between collocation uncertainty and a set of
environmental factors, height and distance between imperfectly collocated
trajectories. The new statistical approach is based on the heteroskedastic
functional regression (HFR) model which extends the standard functional
regression approach and allows a natural definition of uncertainty profiles.
Along this line, a five-fold decomposition of the total collocation
uncertainty is proposed, giving both a profile budget and an integrated
column budget.
HFR is a data-driven approach valid for any atmospheric parameter, which can
be assumed smooth. It is illustrated here by means of the collocation
uncertainty analysis of relative humidity from two stations involved in the
GCOS reference upper-air network (GRUAN). In this case, 85% of the total
collocation uncertainty is ascribed to reducible environmental error, 11%
to irreducible environmental error, 3.4% to adjustable bias, 0.1% to
sampling error and 0.2% to measurement error. |
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