海陆风的观测与模拟研究进展及其对臭氧污染的影响
Advances in the observation and simulation of sea–land breeze circulations and their influence on ozone pollution.
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摘要: 海陆风是沿海地区典型的中尺度环流系统,对区域气候调节和空气质量有着重要影响。本文综述了海陆风在观测、模拟、识别及其与臭氧污染关系方面的研究进展,旨在总结当前研究成果并探讨未来发展方向。结果表明,海陆风的观测研究由早期的单站点、非连续的经验性记录,发展为集多源遥感、地面自动气象站网络等多手段协同的组网式立体连续监测体系;在数值模拟方面,研究从早期的线性解析模式发展到高分辨率的中尺度模式及多模式耦合系统,对海陆风结构、海气通量和污染物输送的刻画更加精细,整体呈现向高分辨率和多物理过程耦合演进的趋势;识别方法逐渐形成以背景风场、气压场和海陆温差为核心的多源融合体系。近年来,大量研究揭示了海陆风的昼夜循环机制通过“输送—积累—再循环”过程显著影响沿海地区臭氧浓度的形成与演变。同时,海陆风通过与天气系统相互作用,影响臭氧污染过程和污染时长,成为驱动高臭氧事件的重要动力过程。未来研究应加强多源观测与模式耦合,深化机制认识,构建智能识别与预报体系,为沿海地区空气质量改善和气候变化应对提供科学支撑。Abstract: Sea–land breeze is a typical mesoscale circulation system in coastal regions and exerts a substantial influence on regional climate regulation and air quality. This study reviews recent progress in sea–land breeze research from the perspectives of observation, numerical simulation, identification techniques, and its interactions with ozone pollution. Observational studies have advanced from early single-site, discontinuous empirical records to integrated, network-based three-dimensional monitoring systems that combine multi-source remote sensing and automatic meteorological station networks. Numerical modeling has evolved from linear analytical frameworks to high-resolution mesoscale models and multi-model coupling systems, enabling more refined representations of sea–land breeze structure, air–sea fluxes, and pollutant transport. Identification approaches have gradually developed into multi-source fusion systems centered on background wind fields, pressure patterns, and sea–land thermal contrasts.Recent studies have further revealed that the diurnal cycle of sea–land breeze markedly influences the formation and evolution of coastal ozone through a “transport–accumulation–recirculation” mechanism. Moreover, interactions between sea–land breeze and synoptic weather systems regulate both the development and persistence of ozone pollution, making it a key dynamical driver of high-ozone events. Future research should enhance multi-source observational–model integration, deepen mechanistic understanding, and advance intelligent identification and forecasting frameworks to support air quality improvement and climate resilience in coastal regions..
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