Physics – Geophysics
Scientific paper
Jun 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005eostr..86..226m&link_type=abstract
EOS Transactions, AGU, Volume 86, Issue 24, p. 226-232
Physics
Geophysics
7
Mathematical Geophysics: Time Series Analysis (1872, 4277, 4475), Global Change: Climate Dynamics (0429, 3309), Global Change: Sea Level Change (1222, 1225, 4556)
Scientific paper
Geophysical studies are plagued by short and noisy time series. These time series are typically nonstationary, contain various long-period quasi-periodic components, and have rather low signal-to-noise ratios and/or poor spatial sampling. Classic examples of these time series are tide gauge records, which are influenced by ocean and atmospheric circulation patterns, twentieth-century warming, and other long-term variability. Remarkable progress recently has been made in the statistical analysis of time series. Ghil et al. [2002] presented a general review of several advanced statistical methods with a solid theoretical foundation. This present article highlights several new approaches that are easy to use and that may be of general interest. Extracting trends from data is a key element of many geophysical studies; however, when the best fit is clearly not linear, it can be difficult to evaluate appropriate errors for the trend. Here, a method is suggested of finding a data-adaptive nonlinear trend and its error at any point along the trend. The method has significant advantages over, e.g., low-pass filtering or fitting by polynomial functions in that as the fit is data adaptive, no preconceived functions are forced on the data; the errors associated with the trend are then usually much smaller than individual measurement errors.
Grinsted Aslak
Jevrejeva Svetlana
Moore Carrick J.
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