The study of the precipitation deserves great attention because being part of a recent past, they allow us to analyze in detail the variations which have occurred and their causes. In order to correctly study these variations we must have at our disposal some homogeneous series. A climatic series is considered homogeneous when its variations are only due to climatic events (Maugeri et al., 2006; Peterson et al., 1998). Unfortunately, most of the series present non climatic factors that may hide the real changes. The discontinuity can be due to a change in the location of the station, to a replacement of the instruments or to a variation in the surrounding environment. In this report, we have studied the daily pluviometric series of 21 meteorological stations in Piedmont. As a first step, we have done a historical research (concerning each station) which has allowed us to determine the variations due either to the location or to the replacement of the equipment. Subsequently we have reconstructed some monthly amounts for creating a serially complete (no missing data). We have chosen four different methods of spatial interpolation (Eischeid et al., 1995; Eischeid et al., 2000). These are defined as the 1) normal ratio method (NR), 2) simple inverse distance weighting (IDW), 3) multiple regression (MR) and 4) median of the previous three method (MED). Then we have applied the an implementation of well-known Standard Normal Homogeneity Test (SNHT) (Alexandersson et al., 1997) to the monthly series. This method, realised by the Climate Change Research Group (URV, Taragona, Spain), allows to estimate and individuate the gradual or sudden change of the average value of a particular series comparing it to the reference series which has been obtained by evaluating the result of the adjacent series and which is considered homogeneous. In this way we have got the homogeneous series on which trends have been computed and the non-parametric Mann-Kendall test has been used to understand the statistical meaning of the trend. Finally, to illustrate the trend of precipitation extreme values and in order to better understand the consequences of climate variations on our society, the indices proposed by “CCL/CLIVAR Working Group on Climate Change Detection” have been calculated over WMO 30-year periods (1951-80, 1961-90, 1971-2000) and over the whole period. Consecutively we have determined and quantify their correlation with large-scale atmospheric pattern and global indices such as the North Atlantic Oscillation (NAO).
Valuations on historical series of precipitation in Piedmont (NW Italy).
ACQUAOTTA, FIORELLA;FRATIANNI, SIMONA
2009-01-01
Abstract
The study of the precipitation deserves great attention because being part of a recent past, they allow us to analyze in detail the variations which have occurred and their causes. In order to correctly study these variations we must have at our disposal some homogeneous series. A climatic series is considered homogeneous when its variations are only due to climatic events (Maugeri et al., 2006; Peterson et al., 1998). Unfortunately, most of the series present non climatic factors that may hide the real changes. The discontinuity can be due to a change in the location of the station, to a replacement of the instruments or to a variation in the surrounding environment. In this report, we have studied the daily pluviometric series of 21 meteorological stations in Piedmont. As a first step, we have done a historical research (concerning each station) which has allowed us to determine the variations due either to the location or to the replacement of the equipment. Subsequently we have reconstructed some monthly amounts for creating a serially complete (no missing data). We have chosen four different methods of spatial interpolation (Eischeid et al., 1995; Eischeid et al., 2000). These are defined as the 1) normal ratio method (NR), 2) simple inverse distance weighting (IDW), 3) multiple regression (MR) and 4) median of the previous three method (MED). Then we have applied the an implementation of well-known Standard Normal Homogeneity Test (SNHT) (Alexandersson et al., 1997) to the monthly series. This method, realised by the Climate Change Research Group (URV, Taragona, Spain), allows to estimate and individuate the gradual or sudden change of the average value of a particular series comparing it to the reference series which has been obtained by evaluating the result of the adjacent series and which is considered homogeneous. In this way we have got the homogeneous series on which trends have been computed and the non-parametric Mann-Kendall test has been used to understand the statistical meaning of the trend. Finally, to illustrate the trend of precipitation extreme values and in order to better understand the consequences of climate variations on our society, the indices proposed by “CCL/CLIVAR Working Group on Climate Change Detection” have been calculated over WMO 30-year periods (1951-80, 1961-90, 1971-2000) and over the whole period. Consecutively we have determined and quantify their correlation with large-scale atmospheric pattern and global indices such as the North Atlantic Oscillation (NAO).
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