Abstract
Abstract. Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013–2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM2.5, NO2 and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3 % (44.4 µg m−3) in January 2013, 46.4 % (22.2 µg m−3) in December 2015 and 73.3 % (34.6 µg m−3) in December 2016.
Highlights
The atmospheric boundary layer (ABL) is the section of the atmosphere that responds directly to the flows of mass, energy and momentum from the earth’s surface (Stull, 1988)
We construct a 3 × 3 self-organizing maps (SOMs) matrix for daily virtual potential temperature deviation profiles, and the self-organized output shown in Fig. 1 represents nine ABL types (i.e., SOM nodes)
On the SOM plane, the most notable feature is adjacency of like types (e.g., Nodes 1 and 2) and the separation of contrasting types (e.g., Nodes 1 and 9). Such ordering is a feature of the SOM algorithm (i.e., “self-organized”), which allows us to distinguish the unique characteristics of nodes through the variation of specific features across the SOM plane
Summary
The atmospheric boundary layer (ABL) is the section of the atmosphere that responds directly to the flows of mass, energy and momentum from the earth’s surface (Stull, 1988). The ABL structure is determined by complex interactions between atmosphere static stability and those mechanical processes (such as wind shear from synoptic or terrain-induced flows) (Stull, 1988) These processes can operate at a variety of different heights and temporal scales, and their dominance may vary considerably with height and time at any given location (Salmond and McKendry, 2005).
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