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- Author or Editor: S. J. Mason x
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Abstract
Cluster analysis is a technique frequently used in climatology for grouping cases to define classes (synoptic types or climate regimes, for example), or for grouping stations or grid points to define regions. Cluster analysis is based on some form of distance matrix, and the most commonly used metric in the climatological field has been Euclidean distances. Arguments for the use of Euclidean distances are in some ways similar to arguments for using a covariance matrix in principal components analysis: the use of the metric is valid if all data are measured on the same scale. When using Euclidean distances for cluster analysis, however, the additional assumption is made that all the variables are uncorrelated, and this assumption is frequently ignored. Two possible methods of dealing with the correlation between the variables are considered: performing a principal components analysis before calculating Euclidean distances, and calculating Mahalanobis distances using the raw data. Under certain conditions calculating Mahalanobis distances is equivalent to calculating Euclidean distances from the principal components. It is suggested that when cluster analysis is used for defining regions, Mahalanobis distances are inappropriate, and that Euclidean distances should be calculated using the unstandardized principal component scores based on only the major principal components.
Abstract
Cluster analysis is a technique frequently used in climatology for grouping cases to define classes (synoptic types or climate regimes, for example), or for grouping stations or grid points to define regions. Cluster analysis is based on some form of distance matrix, and the most commonly used metric in the climatological field has been Euclidean distances. Arguments for the use of Euclidean distances are in some ways similar to arguments for using a covariance matrix in principal components analysis: the use of the metric is valid if all data are measured on the same scale. When using Euclidean distances for cluster analysis, however, the additional assumption is made that all the variables are uncorrelated, and this assumption is frequently ignored. Two possible methods of dealing with the correlation between the variables are considered: performing a principal components analysis before calculating Euclidean distances, and calculating Mahalanobis distances using the raw data. Under certain conditions calculating Mahalanobis distances is equivalent to calculating Euclidean distances from the principal components. It is suggested that when cluster analysis is used for defining regions, Mahalanobis distances are inappropriate, and that Euclidean distances should be calculated using the unstandardized principal component scores based on only the major principal components.
Abstract
Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur.
Abstract
Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur.
