Supertyphoons (STs) and strong convection gales (SCGs) are extremely hazardous weather events over land. Knowledge of their processes is crucial for various applications, such as intensity forecasts of gales and the design of high-rise construction and infrastructure. Here, an observational analysis of two strong SCGs and two STs is presented based on data from the Shenzhen meteorological gradient tower, the tallest in Asia. Differences in the intrinsic physical characteristics measured at each event can be associated with different disaster-causing mechanisms. Wind speeds during STs are comparatively much larger but feature slower variations, while those of SCGs are more abrupt. Unlike that observed during STs, the vertical distribution of wind speeds during SCGs obeys a power law or exponential distribution only within 1-h maximum wind speed windows. In comparison with a Gaussian distribution, a generalized extreme value distribution can better characterize the statistical characteristics of the gusts of both STs and SCGs events. Deviations from Kolmogorov’s −5/3 power law were observed in the energy spectra of both phenomena at upper levels, albeit with differences. Different from what is seen in the ST energy spectrum distribution, a clear process of energy increase and decrease could be seen in SCGs during gale evolution. Nonetheless, both SCGs and STs exhibited a high downward transfer of turbulent momentum flux at a 320 m height, which could be attributed to the pulsation of the gusts rather than to the large-scale base flow. Significance Statement: Strong gales induced by typhoons and severe convection have potential serious impacts on human society. The current study compares and analyzes the characteristics of the gales induced by the two different weather systems using the data observed by a 356-m-tall tower in South China. This paper also shows the relationship between gusts of the near-surface wind and the turbulent momentum fluxes, thus suggesting a possible mechanism leading to destructive forces in surface winds. In terms of social value, this study would contribute to increase the awareness of gales (the instantaneous wind speed over 17 m s⁻¹) and improve the prediction and prevention of different types of gales, as well as the wind-resistant design of high-rise buildings.

Based on the meteorological, ozone (O₃), and vertical observation data of 2020, this study sought to evaluate the daily variation in O₃, particularly the characteristics of nocturnal ozone pollution. We also discuss the effect of local and mesoscale horizontal transport and vertical mixing on the formation of nocturnal O₃ pollution. Distinct seasonal characteristics of the daily O₃ variation in Shenzhen were identified. In particular, significant nocturnal peaks were found to regularly occur in the winter and spring (November–December and January–April). The monthly average of daily variation had a clear bimodal distribution. During the period, O₃ pollution frequently occurred at night, with the maximum hourly O₃ concentration reaching 203.5 μg/m³. Nocturnal O₃ pollution was closely associated with horizontal transport and vertical mixing. During the study period, the O₃ maximum values were recorded on 68 nights, primarily between 23:00 and 03:00, with occasional observation of two peaks. The impact of horizontal transport and vertical mixing on the nocturnal secondary O₃ maximum values was elaborated in two case studies, where vertical mixing was mainly associated with low-level jets, with strong wind shear enhancing turbulent mixing and transporting O₃ from the upper layers to the surface.

A tropical cyclone (TC) is an extremely hazardous weather event. These events include heavy rain as an important hazard factor, which poses a serious threat to the public safety of coastal cities. Presently, numerical weather prediction (NWP) is an important and commonly used method to support the forecasting of the impact of TCs. However, relatively high uncertainty still remains in quantitative precipitation predictions provided by NWP, which makes it difficult to meet the demands of public safety management in regions affected by TCs. To remediate this deficiency, the study combines machine learning (ML) techniques with NWP and proposes a new analogue identification method for TC precipitation estimation. The method consists of three parts. First, the output data of historical NWPs, including wind, temperature, humidity of 850 hPa and sea‐level pressure field, are deposited into a massive sample library. Second, the dimensionality of the sample library is reduced by using the locally linear embedding (LLE) method, and a characteristic subspace containing the features of the original sample library is formed. Finally, when a new NWP prediction appears, it can be projected into the characteristic subspace, and the historical NWP sample having the greatest similarity with the current NWP prediction can be identified. The observational precipitation corresponding to the most similar historical NWP sample can then be used to estimate the potential impact of current TCs. This method is verified using observation results from the coastal city of Shenzhen, China. The results show that the prediction of the method proposed in the current study exhibited significant improvement compared with prediction results provided by both the NWP's direct output and a traditional method used in the city.