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A local winter storm scale (LWSS) is developed to categorize the disruption caused by winter storms using archived surface weather observations from a single location along the U.S. East Coast. Development of LWSS is motivated by the recognition that the observed societal impact from a given winter storm (called realized disruption here) arises from the convolution of two factors, the meteorological conditions that lead to disruption (i.e., intrinsic disruption) and society's susceptibility to winter weather. LWSS is designed to measure the first factor, intrinsic disruption. The scale uses maximum sustained winds, wind gusts, storm-total snowfall and icing accumulations, and minimum visibility to arrive at a categorical value between 0 and 5 inclusive. An alternate method is used to quantify the realized disruption that each storm produced and helps calibrate aspects of LWSS. All winter storms observed at Newark Liberty International Airport over the 15 cold seasons between 1995/96 and 2009/10 were categorized using LWSS. Focusing on one location reduces the variability in societal susceptibility, which allows the relationship between intrinsic disruption and realized disruption to be quantified.
Some important factors related to societal susceptibility were found to increase storms' realized disruption, including occurrence during a weekday, off-peak season, and less than two days subsequent to a previous storm. A closer examination of the 9–11 February 2010 winter storm demonstrates LWSS's ability to depict the spatial variability of the storm's intrinsic disruption. This information is used to infer variations in societal susceptibility between metropolitan areas and reveals the need for an instantaneous intrinsic disruption index to account for temporal variations in storm intensity.
A local winter storm scale (LWSS) is developed to categorize the disruption caused by winter storms using archived surface weather observations from a single location along the U.S. East Coast. Development of LWSS is motivated by the recognition that the observed societal impact from a given winter storm (called realized disruption here) arises from the convolution of two factors, the meteorological conditions that lead to disruption (i.e., intrinsic disruption) and society's susceptibility to winter weather. LWSS is designed to measure the first factor, intrinsic disruption. The scale uses maximum sustained winds, wind gusts, storm-total snowfall and icing accumulations, and minimum visibility to arrive at a categorical value between 0 and 5 inclusive. An alternate method is used to quantify the realized disruption that each storm produced and helps calibrate aspects of LWSS. All winter storms observed at Newark Liberty International Airport over the 15 cold seasons between 1995/96 and 2009/10 were categorized using LWSS. Focusing on one location reduces the variability in societal susceptibility, which allows the relationship between intrinsic disruption and realized disruption to be quantified.
Some important factors related to societal susceptibility were found to increase storms' realized disruption, including occurrence during a weekday, off-peak season, and less than two days subsequent to a previous storm. A closer examination of the 9–11 February 2010 winter storm demonstrates LWSS's ability to depict the spatial variability of the storm's intrinsic disruption. This information is used to infer variations in societal susceptibility between metropolitan areas and reveals the need for an instantaneous intrinsic disruption index to account for temporal variations in storm intensity.
Abstract
A generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.
Abstract
A generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.
