Clustering Surface Ozone Diurnal Cycles to Understand the Impact of Circulation Patterns in Houston, TX

Abstract

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.

Identification of Sea Breeze Recirculation and Its Effects on Ozone in Houston, TX, During DISCOVER‐AQ 2013

“…Our study period is from late summer (August) to early fall (September and October), during which the BH starts to retreat eastward making way for migratory anticyclones to develop. This explains why the southerly winds in our study are less strong and frequent compared with those in other studies focusing on summer alone or longer periods (Bernier et al, 2019; Davis et al, 1998; Ngan & Byun, 2011; Wang et al, 2016). By contrast, SA has an opposite seasonal change in the frequency of these two clusters due to the abnormal number of days in October.…”

“…Thus, our analysis can be taken to represent the general meteorological conditions in the HGB and SA during August to October. On the contrary, drought years (e.g., 2011) can witness abnormal circulation patterns not representative of other years (Bernier et al, 2019).…”

Clustering diurnal cycles of day-to-day temperature change to understand their impacts on air quality forecasting in mountain-basin areas

“…Currently, numerical models (including the WRF-Chem model and CMAQ model) (Grell et al, 2005;Byun and Ching, 1999) and statistical models (including statistical analysis, machine learning, the hybrid linear-nonlinear method, etc.) (Huang, 1992;Chelani and Devotta, 2006;Borse, 2020) are the two typical methods that D. Kong et al: Diurnal cycles of temperature changes and their effects on air quality have been widely used to forecast air quality by combining weather conditions and emission sources (Gidhagen et al, 2005). In the future, our findings should therefore be combined with numerical models or statistical models to improve air quality forecasting in mountain-basin areas.…”

“…Clustering methods divide the objects into specific groups, with the goal that all data objects assigned to the same cluster have common characteristics, while different clusters have distinct characteristics (Darby, 2005). The cluster-ing methods have been widely used in climate and environmental research (Bardossy et al, 1995;Cavazos, 2000;Luo and Lau, 2017;Bernier et al, 2019). In this study, the regional average values of day-to-day temperature change in the SCB and the K-means clustering method (MacQueen, 1967) are selected to classify the diurnal cycles of day-today temperature change because of the simplicity and convergence characteristics of the K-means clustering method.…”

Clustering diurnal cycles of day-to-day temperature change to understand their impacts on air quality forecasting in mountain-basin areas

“…Clustering methods divide the objects into specific groups, with the goal that all data objects assigned to the same cluster have common characteristics while different clusters have distinct characteristics (Darby, 2005). The clustering methods have been widely used in climate and environmental researches (Bardossy et al, 1995;Cavazos, 2000;Luo and Lau, 2017;Bernier et al, 2019). In this study, the regional average values of day-to-day temperature change in SCB and the K-means clustering method (MacQueen, 1967) are selected to classify the diurnal cycles of day-to-day temperature change, because of the simplicity and convergence characteristics of K-means clustering method.…”