摘要:The spatial and temporal variability patterns of the urban heat island (UHI) in the metropolitan area of Sao Paulo (MASP) were investigated using hourly temperature observations for a 10-year period from January 2002 to December 2011. The empirical orthogonal function (EOF) and cluster analysis (CA) techniques for multivariate analysis were used to determine the dominant modes of UHI variability and to identify the homogeneity between the temperature observations in the MASP. The EOF method was used to obtain the spatial patterns (T-mode EOF) and to define temporal variability (S-mode EOF). In the T-mode, three main modes of variability were recognized. The first EOF explained 66.7% of the total variance in the air temperature, the second explained 24.0%, and the third explained 7.8%. The first and third EOFs were associated with wind movement in the MASP. The second EOF was considered the most important mode and was found to be related to the level of urbanization in the MASP, the release of heat stored in the urban canopy and the release of heat by anthropogenic sources, thus representing the UHI pattern in the MASP. In the S-mode, two modes of variability were found. The first EOF explained 49.4% of the total variance in the data, and the second explained 30.9%. In the S-mode, the first EOF represented the spatial pattern of the UHI and was similar to the second EOF in the T-mode. CA resulted in the identification of six homogeneous groups corresponding to the EOF patterns observed. The standard UHI according to the scale and annual seasons for the period from 2002 to 2010 presented maximum values between 14:00 and 16:00 local time (LT) and minimum values between 07:00 and 09:00 LT. Seasonal analysis revealed that spring had the highest maximum and minimum UHI values relative to the other seasons.
关键词:urban heat island; air temperature; São Paulo; multivariate statistical techniques; empirical orthogonal function; cluster analysis urban heat island ; air temperature ; São Paulo ; multivariate statistical techniques ; empirical orthogonal function ; cluster analysis