Abstract
AbstractIn coastal environments, sea breeze recirculation has been found to be an important mesoscale meteorological phenomenon that causes high ozone episodes, yet the identification of this small‐scale circulation pattern remains difficult. In this study, a new method was developed to automatically identify sea breeze recirculation in Houston, TX, by applying K‐Means clustering algorithm to surface winds measurements at near‐coast sites during the DISCOVER‐AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign period from August to October 2013. The key to the clustering algorithm is seven features derived from site‐based surface winds on each day, including zonal (U) and meridional (V) winds in the morning and afternoon, 24‐hr transport direction (θ) and the recirculation factor, which is the ratio of net transport distance (L) to wind run distance (S). For comparison, the same clustering was applied to San Antonio, TX, a noncoastal city yet within the synoptic‐scale distance from Houston. Four clusters were obtained for each region, including three synoptic patterns common to both regions and one mesoscale pattern that differs by region, classified as Stagnation and Sea Breeze Cluster for San Antonio and Houston, respectively. The clustering outputs were verified by wind profiler data in Houston. By linking the wind clusters with surface and aircraft ozone measurements, we revealed a clear connection between circulation patterns and daily ozone variability showing that maximum daily average 8‐hr (MDA8) ozone levels and spatial distributions differed by cluster type (e.g., the highest ozone found in the Stagnation/Sea Breeze Cluster and the lowest ozone in the Southerly Cluster). This automatable method of sea breeze identification we developed can be potentially applied to other coastal cities because it has low data requirement and no ad hoc location‐specific adjustments.
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