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 high‐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 © 2002 Royal Meteorological Society

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. This improved visualization has led to SOMs becoming an increasingly popular tool in various research with climatological applications from other disciplines as well.

While many studies comparing atmospheric reanalysis and surface observations have focused on the similarity of mean fields, trends, or frequencies of extreme events, very few have assessed how similar surface observations and reanalysis data sets are in terms of their specific identification of extreme temperature event days. Here, we assess the similarity between surface observations and three reanalysis products: ERA5, ERA5‐LAND, and NARR, in terms of the days on which they identify extreme heat and cold events for the period 1979–2016 at 230 locations in the United States and Canada. Cold events have a greater match than heat events. ERA5 has the greatest match percentage with station data across the study region. Match percentage is greatest in midlatitude, continental locations, with poorer performance in coastal areas, and the Arctic.