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historical data. Instead of using past-performance information, this study proposes a new, innovative, and dynamical performance-ranking method using only the current forecast information (therefore, it is computationally simple and cheap) to predict the relative performance of each ensemble member case by case (i.e., flow dependent). To our knowledge all of the previously mentioned statistical methods have been used only to postprocess or calibrate raw forecasts and so far no statistical method has been
historical data. Instead of using past-performance information, this study proposes a new, innovative, and dynamical performance-ranking method using only the current forecast information (therefore, it is computationally simple and cheap) to predict the relative performance of each ensemble member case by case (i.e., flow dependent). To our knowledge all of the previously mentioned statistical methods have been used only to postprocess or calibrate raw forecasts and so far no statistical method has been
1. Introduction Horton et al. (2001) use an empirical ranking method for climatological data probably introduced by Beard (1943) , derived in a similar form by Chegodaev (1953) , and extensively used by Jenkinson in Volume II of a Natural Environment Research Council report ( NERC 1975a , b ). The method converts ranked values of a physical variable (e.g., temperature) into corresponding cumulative probabilities defined over a specific climatological period. Horton et al. (2001) used this
1. Introduction Horton et al. (2001) use an empirical ranking method for climatological data probably introduced by Beard (1943) , derived in a similar form by Chegodaev (1953) , and extensively used by Jenkinson in Volume II of a Natural Environment Research Council report ( NERC 1975a , b ). The method converts ranked values of a physical variable (e.g., temperature) into corresponding cumulative probabilities defined over a specific climatological period. Horton et al. (2001) used this
1. Introduction In developing synoptic climatology studies of weather events, it can be difficult to develop a method for ranking the significance of the chosen weather events. An example of the need for such a ranking is to choose the most important, prototypical cases for study. As part of an effort to understand the meteorological differences, if any, between days producing major tornado outbreaks from those that produce primarily nontornadic severe convective storms, we have been faced with
1. Introduction In developing synoptic climatology studies of weather events, it can be difficult to develop a method for ranking the significance of the chosen weather events. An example of the need for such a ranking is to choose the most important, prototypical cases for study. As part of an effort to understand the meteorological differences, if any, between days producing major tornado outbreaks from those that produce primarily nontornadic severe convective storms, we have been faced with
.1175/1520-0493(1988)116<0495:OSIOUS>2.0.CO;2 . Doswell, C. A., III , Edwards R. , Thompson R. L. , Hart J. A. , and Crosbie K. C. , 2006 : A simple and flexible method for ranking severe weather events . Wea. Forecasting , 21 , 939 – 951 , doi: 10.1175/WAF959.1 . Doswell, C. A., III , Brooks H. E. , and Dotzek N. , 2009 : On the implementation of the enhanced Fujita scale in the USA . Atmos. Res. , 93 , 554 – 563 , doi: 10.1016/j.atmosres.2008.11.003 . Doswell, C. A., III , Carbin G. W. , and
.1175/1520-0493(1988)116<0495:OSIOUS>2.0.CO;2 . Doswell, C. A., III , Edwards R. , Thompson R. L. , Hart J. A. , and Crosbie K. C. , 2006 : A simple and flexible method for ranking severe weather events . Wea. Forecasting , 21 , 939 – 951 , doi: 10.1175/WAF959.1 . Doswell, C. A., III , Brooks H. E. , and Dotzek N. , 2009 : On the implementation of the enhanced Fujita scale in the USA . Atmos. Res. , 93 , 554 – 563 , doi: 10.1016/j.atmosres.2008.11.003 . Doswell, C. A., III , Carbin G. W. , and
varying duration. This approach, referred to here as the optimal ranking regime (ORR) method, has been used to identify significant IMD periods in U.S. temperature, precipitation, and streamflow ( Mauget 2003a , b , 2004 ; Cordero et al. 2011 ) and reconstructed South American snowpack records ( Masiokas et al. 2012 ) and to compare observed and modeled IMD temperature variability over the United States ( Mauget et al. 2012 ; Brown et al. 2012 ). In the current work, the ORR method is used to
varying duration. This approach, referred to here as the optimal ranking regime (ORR) method, has been used to identify significant IMD periods in U.S. temperature, precipitation, and streamflow ( Mauget 2003a , b , 2004 ; Cordero et al. 2011 ) and reconstructed South American snowpack records ( Masiokas et al. 2012 ) and to compare observed and modeled IMD temperature variability over the United States ( Mauget et al. 2012 ; Brown et al. 2012 ). In the current work, the ORR method is used to
nonlinear “holes.” In a series of papers ( Mauget 2003a , b , 2004 , 2006 ; Cordero et al. 2011 ), intradecadal to multidecadal (IMD) variability in temperature, precipitation, and streamflow has been identified via a method that calculates Mann–Whitney Z statistics over moving time windows of varying durations. In Mauget and Cordero (2014 , hereafter Part I) , this approach—referred to here as the optimal ranking regime (ORR) method—was used to detect IMD variation in U.S. climate division
nonlinear “holes.” In a series of papers ( Mauget 2003a , b , 2004 , 2006 ; Cordero et al. 2011 ), intradecadal to multidecadal (IMD) variability in temperature, precipitation, and streamflow has been identified via a method that calculates Mann–Whitney Z statistics over moving time windows of varying durations. In Mauget and Cordero (2014 , hereafter Part I) , this approach—referred to here as the optimal ranking regime (ORR) method—was used to detect IMD variation in U.S. climate division
1895. Monthly data are appropriate here, given the emphasis on a ranking analysis of U.S. seasonal temperature since that year. Earlier work based on applying the optimal ranking regime (ORR) method to a previous version of divisional temperature data was limited to seasonally averaged mean monthly temperatures ( Mauget and Cordero 2014 ). The release of nClimDiv data allows for similar ORR analyses here of mean summer maximum temperature (TMX S ) and mean summer minimum temperature (TMN S
1895. Monthly data are appropriate here, given the emphasis on a ranking analysis of U.S. seasonal temperature since that year. Earlier work based on applying the optimal ranking regime (ORR) method to a previous version of divisional temperature data was limited to seasonally averaged mean monthly temperatures ( Mauget and Cordero 2014 ). The release of nClimDiv data allows for similar ORR analyses here of mean summer maximum temperature (TMX S ) and mean summer minimum temperature (TMN S
the overall skill of climate forecasts in the MME approach, and more skillful models should receive more weight in the combination of results ( Murphy et al. 2004 ; Schmittner et al. 2005 ; Furrer et al. 2007 ; Watterson 2008 ). Some efforts have been made to weight models in an ensemble. One interesting method for combining the result of various models is referred to as the reliability ensemble average (REA) approach ( Giorgi and Mearns 2002 , 2003 ; Giorgi and Bi 2005 ; Moise and Hudson
the overall skill of climate forecasts in the MME approach, and more skillful models should receive more weight in the combination of results ( Murphy et al. 2004 ; Schmittner et al. 2005 ; Furrer et al. 2007 ; Watterson 2008 ). Some efforts have been made to weight models in an ensemble. One interesting method for combining the result of various models is referred to as the reliability ensemble average (REA) approach ( Giorgi and Mearns 2002 , 2003 ; Giorgi and Bi 2005 ; Moise and Hudson
-scale processes on error characteristics to be isolated. Furthermore, a validation for particular periods in the observed record is possible. From a broader perspective, a main goal of this study is to develop and test a framework for an objective calibration study that will be based on the findings presented here. In section 2 , the model setup, observations, and statistical methods are described. Section 3 explores the parameter uncertainty and other sources of uncertainty. Section 4 discusses
-scale processes on error characteristics to be isolated. Furthermore, a validation for particular periods in the observed record is possible. From a broader perspective, a main goal of this study is to develop and test a framework for an objective calibration study that will be based on the findings presented here. In section 2 , the model setup, observations, and statistical methods are described. Section 3 explores the parameter uncertainty and other sources of uncertainty. Section 4 discusses
evidence of U.S. warming trends associated with rising CO 2 levels presented since the early 1980s ( Walsh et al. 2014 ), these long-term projections should account for the effects of those trends on future U.S. energy consumption. However, since the late nineteenth century, continental U.S. temperature records are not exclusively described by warming trends. Using the optimal ranking regime (ORR) method, past work ( Mauget et al. 2012 ; Mauget and Cordero 2014 ) has identified two leading U
evidence of U.S. warming trends associated with rising CO 2 levels presented since the early 1980s ( Walsh et al. 2014 ), these long-term projections should account for the effects of those trends on future U.S. energy consumption. However, since the late nineteenth century, continental U.S. temperature records are not exclusively described by warming trends. Using the optimal ranking regime (ORR) method, past work ( Mauget et al. 2012 ; Mauget and Cordero 2014 ) has identified two leading U
