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Technical note: Trend estimation from irregularly sampled, correlated data

Clarmann, T. von 1; Stiller, G. ORCID iD icon 1; Grabowski, U. 1; Eckert, E. 1; Orphal, J. 1
1 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Estimation of a trend of an atmospheric state variable is usually performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straightforward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate the error correlations. State-dependent amplitudes of superimposed periodic corrections result in a non-linear optimization problem which is solved iteratively.

Volltext §
DOI: 10.5445/IR/110080127
DOI: 10.5194/acp-10-6737-2010
Zitationen: 36
Web of Science
Zitationen: 33
Zitationen: 52
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2010
Sprache Englisch
Identifikator ISSN: 1680-7316
KITopen-ID: 110080127
HGF-Programm 12.04.01 (POF II, LK 01) Dynamik und Transporte
Erschienen in Atmospheric Chemistry and Physics
Verlag European Geosciences Union (EGU)
Band 10
Heft 14
Seiten 6737-6747
Nachgewiesen in Scopus
Web of Science
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