Developments in synoptic climatology in the 1990s included advances in traditional synoptic climatology, empirical downscaling, and dynamical downscaling (i.e. regional climate modelling). The research emphasis in traditional, empirical-statistical approaches to synoptic climatology shifted from methodological development to applications of widely accepted classification techniques, including manual, correlation-based, eigenvector-based, compositing and indexing schemes. In contrast, most efforts in empirical downscaling, which became a well-established field of synoptic climatology during the 1990s, were directed to model development; applications were of secondary concern. Similarly, regional climate models (RCMs) burst onto the scene during the decade and focused on model development, although important progress was made in linking or coupling RCMs to regional or local surface climate systems. This paper discusses prospects for the future of traditional synoptic climatology, empirical downscaling and regional climate modelling. It concludes by looking at the present role of geographic information system (GIS) concepts in synoptic climatology and the potential future role of GIS to the field.
Manual and correlation‐based (also known as Lund or Kirchhofer) classifications are important to synoptic climatology, but both have significant drawbacks. Manual classifications are inherently subjective and labour intensive, whereas correlation‐based classifications give the investigator little control over the map‐patterns generated by the computer. This paper develops a simple procedure that combines these two classification methods, thereby minimizing these weaknesses. The hybrid procedure utilizes a relatively short‐term manual classification to generate composite pressure surfaces, which are then used as seeds in a long‐term correlation‐based computer classification. Overall, the results show that the hybrid classification reproduces the manual classification while optimizing speed, objectivity and investigator control, thus suggesting that the hybrid procedure is superior to the manual or correlation classifications as they are currently used. More specifically, the results demonstrate little difference between the hybrid procedure and the original manual classification at monthly and longer time‐scales, with less internal variation in the hybrid types than in the subjective categories. However, the two classifications showed substantial differences at the daily level, not because of poor performance by the hybrid procedure, but because of errors introduced by the subjectivity of the manual classification. © 1997 Royal Meteorological Society. Int.J.Climatol., Vol.17, 1381‐1396 (No. of Figures: 9 No. of Tables: 1 No. of References: 30)
Manual and correlation-based (also known as Lund or Kirchhofer) classi®cations are important to synoptic climatology, but both have signi®cant drawbacks. Manual classi®cations are inherently subjective and labour intensive, whereas correlationbased classi®cations give the investigator little control over the map-patterns generated by the computer. This paper develops a simple procedure that combines these two classi®cation methods, thereby minimizing these weaknesses. The hybrid procedure utilizes a relatively short-term manual classi®cation to generate composite pressure surfaces, which are then used as seeds in a long-term correlation-based computer classi®cation. Overall, the results show that the hybrid classi®cation reproduces the manual classi®cation while optimizing speed, objectivity and investigator control, thus suggesting that the hybrid procedure is superior to the manual or correlation classi®cations as they are currently used. More speci®cally, the results demonstrate little difference between the hybrid procedure and the original manual classi®cation at monthly and longer timescales , with less internal variation in the hybrid types than in the subjective categories. However, the two classi®cations showed substantial differences at the daily level, not because of poor performance by the hybrid procedure, but because of errors introduced by the subjectivity of the manual classi®cation.
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