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Detection and correction of artificial shifts in climate series
Author(s) -
Caussinus Henri,
Mestre Olivier
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2004.05155.x
Subject(s) - series (stratigraphy) , spurious relationship , outlier , reliability (semiconductor) , computer science , data mining , econometrics , statistics , machine learning , artificial intelligence , mathematics , geology , paleontology , power (physics) , physics , quantum mechanics
Summary. Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather‐stations by means of a suitable two‐factor model allows us to check the reliability of the series. An adapted penalized log‐likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.