Cluster Sequencing to Analyze Synoptic Transitions Affecting Regional Ozone

Abstract

Abstract Synoptic conditions are known to strongly influence mesoscale flows and hence local ozone levels in the San Francisco Bay Area of California. Sets of individual days sharing certain combinations of synoptic features constitute “static” meteorological patterns (or regimes) that account for much of the variability in regional ozone levels. Upon labeling each day of the core Bay Area ozone season using a small number of synoptic regimes, the resulting daily sequence of static patterns indicates the time evolution of the synoptic system through a series of regimes realized for various durations. Further insight into ozone buildup processes can be gained by examining this sequence of static labels to identify “dynamic” meteorological patterns affecting local ozone levels. Transition probabilities between each pair of synoptic regimes are modeled using binomial statistics to determine transitions that are either energetically favored or disfavored to occur. The persistency of the meteorological regimes is additionally considered to complement the transition probabilities in forming a more complete statistical characterization of the synoptic evolution. The sequencing method allows identification of two scenarios under which exceedance of air quality standards occurs in the Bay Area.