New techniques for detection and adjustment of shifts in daily precipitation data series
نویسندگان
چکیده
This study integrates a Box-Cox power transformation procedure into a common trend two-phase regression model based test (the PMFred algorithm) for detecting changepoints, to make the test applicable to non-Gaussian data series, such as non-zero daily precipitation amounts or wind speeds. The detection power aspects of the transformed method (transPMFred) are assessed by a simulation study, which shows that this new algorithm is much better than the corresponding untransformed method for non-Gaussian data; the transformation procedure can increase the hit rate by up to about 70%. Examples of application of this new transPMFred algorithm to detect shifts in real daily precipitation series are provided using non-zero daily precipitation series recorded at a few stations across Canada that represent very different precipitation regimes. The detected changepoints are in good agreement with documented times of changes in for all the example series. This study clarifies that it is essential for homogenization of daily precipitation data series to test the non-zero precipitation amount series and the frequency series of precipitation occurrence (or non-occurrence), separately. The new transPMFred can be used to test the series of non-zero daily precipitation (which are non-Gaussian and positive), while the existing PMFred algorithm can be used to test the frequency series. A software package for using the transPMFred algorithm to detect shifts in non-zero daily precipitation amounts has been developed and made available online free of charge, along with a quantile matching (QM) algorithm for adjusting shifts in non-zero daily precipitation series, which is applicable to all positive data. In addition, a similar QM algorithm has also been developed for adjusting Gaussian data such as temperatures. It is noticed that frequency discontinuities are often inevitable due to changes in the measuring precision of precipitation, and that they could complicate the detection of shifts in non-zero daily precipitation data series and void any attempt to homogenize the
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