In 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.

The diurnal cycle of surface ozone is directly influenced by the chemistry and meteorological processes which affect a region. This study uniquely employs a clustering methodology to examine the complete diurnal pattern of surface ozone for the Houston‐Galveston‐Brazoria region and links the identified patterns to meteorological regimes for June, July, and August of 3 years (2011, 2014, and 2015). Four features were implemented into the clustering algorithm: ozone rate of decrease at night, daily minimum before sunrise, rate of increase after sunrise, and an average of afternoon ozone. Four clusters were chosen, ranging from a mostly flat diurnal pattern with low mixing ratios (~20 ppbv) throughout the day (Cluster 1), to a more variable diurnal cycle with very high mixing ratios (>70 ppbv) in the afternoon (Cluster 4). The clusters are found to associate with distinctive circulation patterns and well‐known regional meteorological processes, such as the low‐level jet and Bermuda High. The uneven distribution of the clusters between the years helps elucidate ozone interannual variability due to meteorology: Cluster 4 had 0 days assigned from 2014 due to the greater influence of circulation patterns bringing clean air from the Gulf of Mexico. We show that the clustering method better characterizes ozone variability than the simplistic method of dividing peak ozone into quantiles. With the clustering analysis, we demonstrate that the ozone diurnal pattern holds more value than just peak ozone hours of the day in providing a clearer understanding of ozone variability and associated meteorological processes.

Abstract. Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a mesoscale and have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone lidar in conjunction with both an offline GEOS-Chem chemical-transport model (CTM) simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS)-1, 2018 OWLETS-2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in five ozone profile curtain clusters. The established five clusters all varied significantly in ozone magnitude vertically and temporally, which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. An overall evaluation of the models reveals good agreement (R≈0.70) in the low-level altitude range (0 to 2000 m), with a low and unsystematic bias for GEOS-Chem and a high systemic positive bias for GEOS-CF. The mid-level (2000–4000 m) performances show a high systematic negative bias for GEOS-Chem and an overall low unsystematic bias for GEOS-CF and a generally weak agreement to the lidar observations (R=0.12 and 0.22, respectively). Evaluating cluster-by-cluster model performance reveals additional model insight that is overlooked in the overall model performance. Utilizing the full vertical and diurnal ozone distribution information specific to lidar measurements, this work provides new insights on model proficiency in complex coastal regions.

Abstract. Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a meso-scale which have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone Light Detection And Ranging (LiDAR) in conjunction with both an offline GEOS-Chem CTM simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS) 1, 2018 OWLETS 2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in 5 vertical ozone profile curtain clusters. The established 5 clusters all varied significantly in ozone magnitude vertically and temporally which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. The two models have fair-to-good relationships with the lidar observations (R = 0.66 to 0.69) in the low-level altitude range (0 to 2000 m), with unsystematic bias for GEOS-Chem and systemically high bias for GEOS-CF. In the mid-level altitude range (2000 to 4000 m), both models have difficulty simulating the vertical extent and variability of ozone concentrations in all 5 clusters, with a weak relationship with the lidar observations (R = 0.12 and 0.22, respectively). GEOS-Chem revealed a systematic high negative bias and GEOS-CF an overall low unsystematic bias range. Using ozone vertical distribution from lidar measurements, this work provides new insights on model’s proficiency in complex coastal regions.

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