The self-organizing map in synoptic climatological research

TLDR

Self‑organizing maps, a relatively new tool in synoptic climatology used for about a decade, produce a two‑dimensional array of self‑organizing cluster types that represent a continuum of synoptic categories rather than discrete realizations typical of traditional methods. This article reviews the major developments and climatological applications of SOMs in the literature. The authors conduct a comprehensive literature review of SOM methodologies and their use in climatology. SOMs allow identification of more patterns and transitional nodes, provide clearer visualization of spatial variables, and have become increasingly popular across climatological research.

Abstract

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.