作者: Scott Beaver , Ahmet Palazoglu
DOI: 10.1175/JAM2437.1
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摘要: Abstract A clustering algorithm is developed to study hourly, ground-level wind measurements obtained from a network of monitoring stations positioned throughout the San Francisco Bay Area California. statistical model based on principal components analysis (or empirical orthogonal functions) used cluster these autocorrelated and cross-correlated observations. Patterns at synoptic time scale are isolated by using windowing scaling operations treat data. Four dominant patterns that affect air quality identified for region, summer days 8 yr historical data assigned modes. One captures high pressure system over western United States, anticyclonic winds which block typical marine flow through region. Differential heating convects polluted mass nearby valley in severe episodes higher-than-average ozone composition occur. second pattern represents seasonal, offshore ridge pres...