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When a correlation-based circulation pattern classification was applied to daily 700 hPa heights for 25 summer seasons, and the circulation types were related to surface temperature and precipitation for the Lake Superior basin, within-type variability was found to be high. Regarding temperature, the difference between warm and cold days of a type is a positive height anomaly centred to the east of the lake, which is very similar to the height anomaly for winter temperatures identified in an earlier study. The difference between wet and dry days of a type is a pattern quite unlike that for temperature. It is a dipole pattern consisting of a negative anomaly to the northwest of the lake and a positive anomaly to the east and southeast. These small-scale circulation components were missed by the correlation-based classification because they are much smaller than the differences in height between circulation types. Regression models, relating monthly mean temperature anomalies to circulation type frequencies, produced results comparable to those obtained for winter temperature. For precipitation, the variance explained by the regression models is only half that obtained for temperature. Two approaches were used to reduce within-type variability and to improve the regression results: the division of the first few circulation types into warm/cold and wet/dry subtypes on the basis of differences in 700 hPa vorticity (an approach introduced in the earlier paper), and a modified correlation-based circulation pattern classification. The modification consisted of raising the correlation threshold for a few selected grid-points to capture the small-scale circulation component and also, of lowering the threshold for the comparison between maps and map sectors in order to minimize the number of unclassified days. Of the two approaches used to reduce within-type variability, the modified classification scheme produced superior results. In particular, the variance in precipitation explained by the regression models is about 30% higher than that achieved using vorticity to create subtypes. Copyright © 2000 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT: During the last few decades, weather circulation type classifications (CTCs) have been widely used to gain insight into processes at the synoptic scale, but also for studying the relationship between atmospheric circulation and surface climate variability. This study aims to evaluate the best performing CTCs based on COST733 software for the stratification of daily ground‐level precipitation and surface air temperature across Italy by means of four statistical metrics. Six classification methods belonging to the four COST733 groups (threshold‐based, PCA‐based, leader algorithms and optimization algorithms) were investigated on 32 and 26 data time series derived from Italian weather stations for daily mean temperature and daily mean precipitation, respectively. CTCs were computed using gridded mean sea level pressure and geopotential height at 500 hPa derived from the NCEP Reanalysis 2 dataset between 1979 and 2015 and tested on three different numbers of classes (8/9, 11/12 and 18 circulation types). Evaluation metrics showed an evident seasonal variability and high‐spatial heterogeneity reflecting the geographical complexity of the Italian territory. The study points out that the best classification, both for temperature and precipitation, is strongly dependent on the classification variable (mean sea level pressure and geopotential height at 500 hPa) showing relevant differences between surface temperature and precipitation. A low number of circulation types (8/9) resulted as the most appropriate grouping for the Italian domain and the Principal Component Transversal and Simulated Annealing were the best performing classification procedures for ground‐level precipitation and temperature stratification, respectively. [ABSTRACT FROM AUTHOR]