Analysis of the latitudinal variability of tropospheric ozone in the Arctic using the large number of aircraft and ozonesonde observations in early summer 2008

Anomalous surface O3 changes in North China Plain during the northwestward movement of a landing typhoon

As high impact weather in a large scale, typhoon movement from the northwest Pacific into inland regions influencing ambient O₃ changes is unclear, especially in North China Plain (NCP). A landing Typhoon Ampil during July 17-24, 2018 was studied herein to characterize the surface O₃ anomalies during its movement over NCP. Landing typhoons present large negative O₃ anomalies at the center of the typhoon and positive O₃ anomalies 600-1700 km away from the center. During the northwest movement of Typhoon Ampil to the NCP, the area and magnitude of both positive and negative O₃ anomalies shrank, particularly in the western and northern periphery, where the typical diurnal change of O₃ dissipated with nocturnal O₃ enhancement in the NCP. The spatiotemporal patterns of surface O₃ anomalies in the NCP were induced significantly during various stages of typhoon movement with a stable structure in the atmospheric boundary layer, strong solar radiation on sunny days, and stratosphere-to-troposphere transport (STT) in the typhoon periphery, depending on the changing intensity, distance, and orientation of the typhoon center. Among them, the STT played a considerable role and contributed 32% to the positive anomalies of surface O₃ in the NCP. Under the influence of westerly jets and high pressure at mid-latitudes on the typhoon movement, strong wind convergences in the upper troposphere were formed intensifying the downdraft of O₃-rich stratospheric air to the boundary layer in the NCP with an asymmetrical distribution of surface positive O₃ anomalies over the periphery of typhoon. This study could improve our understanding of regional ozone changes with meteorological influences.

Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)

Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model–measurement comparisons for the monitoring sites of WOUDC for the period 2019–2020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018–2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815–0.889, root mean square errors (RMSEs) of 7.769–8.729 ppb, and mean absolute errors (MAEs) of 6.111–6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543–16.916 ppb, MAE = 11.130–12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas.