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–Southern Oscillation (ENSO) on the wintertime Northern Hemisphere atmosphere. Unlike NLCCA, which considers multivariate predictors/predictands, nonlinear projection regresses a univariate predictor, for example, the ENSO climate index, onto predictands via a neural network. The model is thus less susceptible to overfitting on noisy problems, but is also unable to easily describe the influence of multiple climate predictors on the predictands. A nonlinear variant of PPA or redundancy analysis would fall between
–Southern Oscillation (ENSO) on the wintertime Northern Hemisphere atmosphere. Unlike NLCCA, which considers multivariate predictors/predictands, nonlinear projection regresses a univariate predictor, for example, the ENSO climate index, onto predictands via a neural network. The model is thus less susceptible to overfitting on noisy problems, but is also unable to easily describe the influence of multiple climate predictors on the predictands. A nonlinear variant of PPA or redundancy analysis would fall between
temperatures is proposed. The procedure is based on a weather classification consisting of two steps: principal component analysis and cluster analysis. At each time of observation(0700, 1400, and 2100 local time) the weather is characterized by temperature, relative humidity, wind speed,and cloudiness. The coet~cients of regression equations, enabling the missing temperatures to be determinedfrom the known temperatures at nearby stations, are computed within each weather class. The influence ofvarious
temperatures is proposed. The procedure is based on a weather classification consisting of two steps: principal component analysis and cluster analysis. At each time of observation(0700, 1400, and 2100 local time) the weather is characterized by temperature, relative humidity, wind speed,and cloudiness. The coet~cients of regression equations, enabling the missing temperatures to be determinedfrom the known temperatures at nearby stations, are computed within each weather class. The influence ofvarious
analyzed period, that is, without cross validation. The temporal structure of downscaled temperatures is characterized by their persistence (lag-1 autocorrelations); the spatial structure is examined by means of correlation maps and by dividing the domain into regions using principal component analysis. a. Persistence First let us examine the area-averaged persistence, shown in Table 7 together with deviations from its observed magnitudes. Among all the methods considered, the pointwise regression
analyzed period, that is, without cross validation. The temporal structure of downscaled temperatures is characterized by their persistence (lag-1 autocorrelations); the spatial structure is examined by means of correlation maps and by dividing the domain into regions using principal component analysis. a. Persistence First let us examine the area-averaged persistence, shown in Table 7 together with deviations from its observed magnitudes. Among all the methods considered, the pointwise regression
trend term, especially in the lower stratosphere. The paper is divided into three sections. In section 2 we describe the regression model and data processing. In section 3 we present the correction to the analysis of Crooks and Gray (2005) for the period 1979–2001 and then extend their regression analysis to 1978–2008. Section 4 gives a summary of the results and conclusions. 2. Data processing and regression model The solar signal is detected using a linear regression analysis of zonally
trend term, especially in the lower stratosphere. The paper is divided into three sections. In section 2 we describe the regression model and data processing. In section 3 we present the correction to the analysis of Crooks and Gray (2005) for the period 1979–2001 and then extend their regression analysis to 1978–2008. Section 4 gives a summary of the results and conclusions. 2. Data processing and regression model The solar signal is detected using a linear regression analysis of zonally
traditional EOF analysis, produces loading vectors that consist of a five-pentad series of spatial patterns associated with a single principal component time series. This allows us to potentially elucidate nascent teleconnection phases as they actually appear in the data, rather than their statistical representation in lead–lag regressions. While many teleconnection analyses are conducted at the 500-mb level, we use the 200-mb level since we are interested in the potential interaction between the tropics
traditional EOF analysis, produces loading vectors that consist of a five-pentad series of spatial patterns associated with a single principal component time series. This allows us to potentially elucidate nascent teleconnection phases as they actually appear in the data, rather than their statistical representation in lead–lag regressions. While many teleconnection analyses are conducted at the 500-mb level, we use the 200-mb level since we are interested in the potential interaction between the tropics
) extended Klein’s pointwise screening technique to examine the consistency of interdecadal trends in 700-hPa geopotential height and local SAT. They did so by analyzing residuals associated with a specification of monthly-mean SAT at a variety of sites throughout the United States, given the observed extratropical NH 700-hPa geopotential height field between 180° and 50°W. Wallace et al. (1995) used regression analysis to identify the spatial pattern in the NH SAT departure field (i.e., the SAT anomaly
) extended Klein’s pointwise screening technique to examine the consistency of interdecadal trends in 700-hPa geopotential height and local SAT. They did so by analyzing residuals associated with a specification of monthly-mean SAT at a variety of sites throughout the United States, given the observed extratropical NH 700-hPa geopotential height field between 180° and 50°W. Wallace et al. (1995) used regression analysis to identify the spatial pattern in the NH SAT departure field (i.e., the SAT anomaly
sources and analysis methods are described in section 2 . The low-level flow structure and moisture-flux characteristics associated with the climatological GPLLJ are discussed in section 3 . Section 4 presents the GPLLJ index while its regressions on dynamical and thermodynamical fields, including regional precipitation, are shown and discussed in section 5 . Recurrent variability of the GPLLJ and related hydroclimate footprints are presented in section 6 , while a brief summary of findings and
sources and analysis methods are described in section 2 . The low-level flow structure and moisture-flux characteristics associated with the climatological GPLLJ are discussed in section 3 . Section 4 presents the GPLLJ index while its regressions on dynamical and thermodynamical fields, including regional precipitation, are shown and discussed in section 5 . Recurrent variability of the GPLLJ and related hydroclimate footprints are presented in section 6 , while a brief summary of findings and
climate anomaly patterns by the use of correlation-based techniques such as EOFs or regression. Note that no correlation based analysis can duplicate the asymmetries between WEs and CEs shown in the composite framework. However, to discern how similar or different our composite results are from these analyses, particularly the seasonal-to-interannual mode of Zhang et al. (1997) and Garreaud and Battisti (1999) , we compute the 10° × 6° area-average regression/correlation patterns associated with
climate anomaly patterns by the use of correlation-based techniques such as EOFs or regression. Note that no correlation based analysis can duplicate the asymmetries between WEs and CEs shown in the composite framework. However, to discern how similar or different our composite results are from these analyses, particularly the seasonal-to-interannual mode of Zhang et al. (1997) and Garreaud and Battisti (1999) , we compute the 10° × 6° area-average regression/correlation patterns associated with
redundancy analysis (RDA; von Storch and Zwiers 1999 ) and partial least squares (PLS) regression ( Wold 1966 ), which maximize the explained variance of one variable by another. These approaches have also been applied for diagnosis (e.g., Bakalian et al. 2010 ) and prediction (e.g., Smoliak et al. 2010 ). Following a comparison study by Bretherton et al. (1992) , there has been a drive to improve theoretical understanding regarding the aspects and limitations of CCA and MCA. Both approaches (and
redundancy analysis (RDA; von Storch and Zwiers 1999 ) and partial least squares (PLS) regression ( Wold 1966 ), which maximize the explained variance of one variable by another. These approaches have also been applied for diagnosis (e.g., Bakalian et al. 2010 ) and prediction (e.g., Smoliak et al. 2010 ). Following a comparison study by Bretherton et al. (1992) , there has been a drive to improve theoretical understanding regarding the aspects and limitations of CCA and MCA. Both approaches (and
determined by canonical correlation analysis. Mon. Wea. Rev. , 115 , 1825 – 1850 . Barnston , A. G. , 1994 : Linear statistical short-term climate predictive skill in the Northern Hemisphere. J. Climate , 7 , 1513 – 1564 . Barnston , A. G. , and H. M. Van den Dool , 1993 : A degeneracy in cross-validated skill in regression-based forecasts. J. Climate , 6 , 963 – 977 . Barnston , A. G. , M. H. Glantz , and Y. He , 1999 : Predictive skill of statistical and dynamical climate
determined by canonical correlation analysis. Mon. Wea. Rev. , 115 , 1825 – 1850 . Barnston , A. G. , 1994 : Linear statistical short-term climate predictive skill in the Northern Hemisphere. J. Climate , 7 , 1513 – 1564 . Barnston , A. G. , and H. M. Van den Dool , 1993 : A degeneracy in cross-validated skill in regression-based forecasts. J. Climate , 6 , 963 – 977 . Barnston , A. G. , M. H. Glantz , and Y. He , 1999 : Predictive skill of statistical and dynamical climate
