Automated synoptic typing of freezing rain events for hazard analysis

Abstract

The estimation of the recurrence rate and the distribution in severity of extreme ice events, which is required in order to determine design criteria for structures such as electric transmission lines, suffers from the fact that there appears to be a great variability in freezing rain events, in terms of duration, quantity of precipitation, and wind speed. Rauber et al. (2001), developed 7 archetypical patterns for freezing rain storms that indeed articulated differences in duration, spatial extent and intensity of storms. This finding suggests that it would not be correct to consider all freezing rain storms as a single homogeneous statistical population, which is the current practice in the extreme value analysis of ice storms. This paper presents an automated and objective methodology for classifying freezing rain storms that can be used in a de-aggregated extreme value analysis. This paper presents the theory, methodology, and results for clustering freezing rain storms. The procedure is tested using a base data set of previously classified storms: the storms identified and classified by Rauber et al. (2001) that occurred over North Eastern United States. The storms are described by making average anomaly maps based on sea level pressure (SLP), and 1000–500hPa geopotential heights using NCEP reanalysis data. Synoptic categories are then formed using clustering algorithms and principal component analysis. The k-means clustering algorithm is compared with a number of hierarchical algorithms. The resulting clusters using the k-means algorithm are compared amongst themselves as well as with the archetypical patterns of Rauber et al. For each event, statistics on wind speeds and precipitation are calculated using NCEP reanalysis data and NCEP hourly precipitation data respectively to verify the significance of the clusters.The three cluster solutions based on 1000–500hPa anomaly maps using the typing method developed here are found to be suitable for grouping storms in homogeneous populations.