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Statistical downscaling model based on canonical correlation analysis for winter extreme precipitation events in the Emilia‐Romagna region
Author(s) -
Busuioc A.,
Tomozeiu R.,
Cacciamani C.
Publication year - 2007
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1547
Subject(s) - downscaling , climatology , environmental science , precipitation , percentile , canonical correlation , correlation coefficient , mathematics , atmospheric sciences , statistics , meteorology , geography , geology
Optimum statistical downscaling models for three winter precipitation indices in the Emilia‐Romagna region, especially related to extreme events, were investigated. For this purpose, the indices referring to the number of events exceeding the long‐term 90 percentile of rainy days, simple daily intensity and maximum number of consecutive dry days were calculated as spatial averages over homogeneous sub‐regions identified by the cluster analysis. The statistical downscaling model (SDM) based on the canonical correlation analysis (CCA) was used as downscaling procedure. The CCA was also used to understand the large‐/regional‐scale mechanisms controlling precipitation variability across the analysed area, especially with respect to extreme events. The dynamic (mean sea‐level pressure‐SLP) and thermodynamic (potential instability‐δ Q and specific humidity‐SH) variables were considered as predictors (either individually or together). The large‐scale SLP can be considered a good predictor for all sub‐regions in the dry index case and for two sub‐regions in the case of the other two indices, showing the importance of dynamical forcing in these cases. Potential instability is the best predictor for the highest mountain region in the case of heavy rainfall frequency, when it can be considered as a single predictor. The combination of dynamic and thermodynamic predictors improves the SDM's skill for all sub‐regions in the dry index case and for some sub‐regions in the simple daily intensity index case. The selected SDMs are stable in time only in terms of correlation coefficient for all sub‐regions for which they are skilful and only for some sub‐regions in terms of explained variance. The reasons are linked to the changes in the atmospheric circulation patterns influencing the local rainfall variability in Emilia‐Romagna as well as the differences in temporal variability over some sub‐regions and sub‐intervals. It was concluded that the average skill over an ensemble of the most skilful and stable SDMs for each region/sub‐interval gives more consistent results. Copyright © 2007 Royal Meteorological Society