The redevelopment of a weather-type classification scheme for North America

Synoptic weather-typing, or the classification of weather conditions into categories, is a useful tool for climate impact applications. Numerous procedures have been developed to accomplish this goal. Before the advent of hi-h-speed computers, manual methods were most common; more recently, more automated methods have come into wide use. Both types of classification have shortcomings manual methods are time consuming and difficult to reproduce, whereas automated methods may not produce easily interpretable results. Several recent methods have incorporated the advantages of both methodologies into a hybrid scheme. This paper describes the redevelopment of one such hybrid scheme, the Spatial Synoptic Classification (SSC). The SSC, originally developed in the mid-1990s, classifies each day at a location into one of six weather types, or a transition. It has been utilized for several applications, from climate trends to human health. Despite its utility, it has several shortcomings, most notably a lower-than-desired match percentage among adjacent stations and a framework that only allows for classification during winter and summer. The new SSC (SSC2) has been altered in several important ways. The most notable changes involve the procedure for selecting seed days, days that typify a particular weather type at a particular location. With the new procedures, the SSC can now produce weather-type classifications year-round, instead of only winter and summer. The spatial cohesiveness among stations has also been improved. The SSC has been expanded to include Canada, Alaska, and Hawaii in addition to the lower 48 US states. SSC calendars are now available for 327 stations with a mean length of 44.6 years, and are updated daily on a website. This paper also presents an important application of the redesigned SSC. It has been used in several heat-stress warning systems worldwide. The synoptic approach is considered to be superior to a traditional apparent temperature approach. as it considers more parameters in its holistic assessment. At each location, one or two of the weather types is associated with mortality levels significantly above the mean. Copyright (C) 2002 Royal Meteorological Society.