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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. [ABSTRACT FROM AUTHOR]

Rare earth resource recycling is an important endeavor for environmental protection and resource utilization. This study explores the method of preparing regenerated magnets using waste magnets as raw materials based on existing processes. By utilizing existing Nd-Fe-B production equipment, various waste magnets are transformed into recycled powder. Next, nascent Nd-Fe-B powders with slightly higher rare earth content are selected as the repairing agent. The regenerated magnets are prepared by incorporating the nascent powder into the recycled powder. The focus lies in investigating the repairing effect of the nascent powder repairing agent on the microstructure of regenerated magnets and exploring the influence of sintering temperature and powder addition on the magnetic properties and microstructure of the regenerated magnets. The results showed that the nascent powder increased the proportion of grain boundary phases and effectively repaired the grain boundary structure of the regenerated magnets. In addition, the Pr element in the nascent powder replaces the Ce element in the recycled powder, which ultimately improves the magnetic properties of the regenerated magnet in a comprehensive manner. This study provides valuable insights and guidance for rare earth resource recycling and the preparation of regenerated magnets. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence parameters, such as turbulent kinetic energy and dissipation rate, are of great significance in weather prediction, meteorological disasters, and forecasting. Due to the lack of ideal direct detection methods, traditional structure function methods are mainly based on Kolmogorov's assumption of local isotropic turbulence and the well-known -5/3 power law within the inertial subrange, which limits their application. Here, we propose a method for directly measuring atmospheric turbulence parameters using coherent Doppler wind lidar, which can directly obtain atmospheric turbulence parameters and vertical structural features, breaking the limitations of traditional methods. The first published spatiotemporal distribution map of the power-law exponent of the inertial subrange is provided in this study, which indicates the heterogeneity of atmospheric turbulence at different altitudes and also indicates that the power-law exponent at high altitudes does not fully comply with the -5/3 power law, proving the superiority of our method. We analyze the results under different weather conditions, indicating that the method still holds. The turbulent kinetic energy and power-law index obtained by this method are continuously compared with the results obtained with an ultrasonic anemometer for a month-long period. The results of the two have high consistency and correlation, verifying the accuracy and applicability of the proposed method. The proposed method has great significance in studying the vertical structural characteristics of atmospheric turbulence. [ABSTRACT FROM AUTHOR]

The height of the stable boundary layer is a key parameter in atmospheric transmission and diffusion, air quality, emergency response, wind energy, and numerical weather prediction models. Existing methods mainly determine the stable boundary layer height via a threshold or minimum value of the wind speed variance under a low-level jet. Based on multi-meteorological element data from a meteorological gradient observation tower, this paper revealed the limitations of existing methods from the perspective of dynamic and thermal effects. In this paper, it is demonstrated that there were four types of shapes of the wind speed variance profile under the low-level jet and a method for using the shape of the variance profile to retrieve the height of the stable boundary layer was proposed. This method distinguished different types of wind speed variance profiles and solved the problems of the misjudgment and omissions (about 34%) present in existing methods. Our experiment showed that the average absolute error of the proposed method was less than 18 m and the average relative error was less than 9%. The results showed that the proposed inversion method was extended to all kinds of wind field detection equipment for inversion of the stable boundary layer height and has very high universality. [ABSTRACT FROM AUTHOR]

The impacts of weather forecast uncertainties have not been quantified in current air quality forecasting systems. To address this, we developed an efficient 2‐D convolutional neural network‐surface ozone ensemble forecast (2DCNN‐SOEF) system using 2‐D convolutional neural network and weather ensemble forecasts, and we applied the system to 216‐hr ozone forecasts in Shenzhen, China. The 2DCNN‐SOEF demonstrated comparable performance to current operating forecast systems and met the air quality level forecast accuracies required by the Chinese authorities up to 144‐hr lead time. Uncertainties in weather forecasts contributed 38%–54% of the ozone forecast errors at 24‐hr lead time and beyond. The 2DCNN‐SOEF enabled an "ozone exceedance probability" metric, which better represented the risks of air pollution given the range of possible weather outcomes. Our ensemble forecast framework can be extended to operationally forecast other meteorology‐dependent environmental risks globally, making it a valuable tool for environmental management. Plain Language Summary: Weather forecasts are intrinsically uncertain, but the impacts of that uncertainty on air quality forecasts are not explicitly quantified in current air quality forecast systems. We proposed here a surface ozone ensemble forecast system, analogous to modern weather ensemble forecast systems, to represent the probability distribution of forecasted surface ozone concentrations given 30–50 possible future weather outcomes. The computation costs of this surface ozone ensemble forecast system were greatly reduced using deep learning techniques that emphasized the spatial patterns of weather. We showed that the surface ozone ensemble forecast system's accuracy met the Chinese operational requirements. However, half of the ozone forecast error was due to weather forecast uncertainties, which cannot be completely eliminated even with perfect pollutant emission estimates and chemistry models. This weather‐induced innate uncertainty in air quality forecasts should be considered for effective air quality management. Key Points: We built a deep‐learning surface ozone ensemble forecast system to quantify pollution risks given the range of possible weather outcomesDeep‐learning models accentuating the spatial patterns of weather effectively represented the ozone‐meteorology relationshipWeather forecast uncertainties contributed 38%–54% of the ozone forecast errors at 24‐hr lead time in Shenzhen [ABSTRACT FROM AUTHOR]

The goal of the paper is to reveal discrepancies of turbulent variables over different surfaces (sea, island, land) based on the measurements taken on three towers during (including before and after) seven typhoon episodes from 2008 to 2018. The atmospheric stability, turbulent spectrum, friction velocity, turbulent kinetic energy, dissipative heating, and gust factor are examined. The similar turbulent characteristics over sea and on the island reinforce the previous conclusion that the turbulent measurements on the island mainly represent the sea surface. The turbulent characteristics over sea and on land are very different due to the different underlying surface roughness. The unstable (stable) condition dominates on the sea (land) surface. Turbulent spectra both over sea and on land follow the canonical Kolmogorov's power law with the −5/3 slope. The cospectra on land are more peaked than those over sea. All of the friction velocity, turbulent kinetic energy, and dissipative heating increase with increasing 10 m wind speed, and those on land are much larger than those over sea. The distributions of gust factors widen and shift to higher on land than those over sea. The distributions of gust factors at heights of 10 m and 40 m are biased to higher values than those at heights of 160 m and 320 m on land. [ABSTRACT FROM AUTHOR]

Black carbon (BC) is an essential climate forcer in the atmosphere. Large uncertainties remain in BC's radiative forcing estimation by models, partially due to the limited measurements of BC vertical distributions near the surface layer. We conducted time-resolved vertical profiling of BC using a 356-m meteorological tower in Shenzhen, China. Five micro-aethalometers were deployed at different heights (2, 50, 100, 200, and 350 m) to explore the temporal dynamics of BC vertical profile in the highly urbanized areas. During the observation period (December 6–15, 2017), the average equivalent BC (eBC) concentrations were 6.6 ± 3.6, 5.4 ± 3.3, 5.9 ± 2.8, 5.2 ± 1.8, and 4.9 ± 1.4 μg m−3, from 2 to 350 m, respectively. eBC temporal variations at different heights were well correlated. eBC concentrations generally decreased with height. At all five heights, eBC diurnal variations exhibited a bimodal pattern, with peaks appearing at 09:00–10:00 and 19:00–21:00. The magnitudes of these diurnal peaks decreased with height, and the decrease was more pronounced for the evening peak. eBC episodes were largely initiated by low wind speeds, implying that wind speed played a key role in the observed eBC concentrations. eBC wind-rose analysis suggested that elevated eBC events at different heights originate from different directions, which suggested contributions from local primary emission plumes. Air masses from central China exhibited much higher eBC levels than the other three backward trajectory clusters found herein. The absorption Ångström exponent (AAE375–880) showed clear diurnal variations at 350 m and increased slightly with height. [ABSTRACT FROM AUTHOR]

Radar data assimilation is an important method for short-term convection forecasting or nowcasting. To improve the short-term (mainly 0–3 h) precipitation forecasts for severe convective storms, an analysis nudging (Newtonian relaxation) based hydrometeor and latent heat nudging (HLHN) technique was developed to effectively assimilate radar reflectivity data in a Weather Research and Forecasting (WRF)-based real time four-dimensional data assimilation and short-term forecasting system (RTFDDA). The purpose of this study is to investigate the performance of the RTFDDA system with radar data assimilation (RTFDDA-RDA) with rapid-cycling forecasting applications for Shenzhen, a subtropical coastal metropolis in southern China. The RTFDDA-RDA system was run to produce hindcasts for ten severe convective storm events occurred in Guangdong region during the 2017 rainy season. Results show that, through nudging cloud hydrometeors retrieved from radar reflectivity and the associated latent heat release, RTFDDA-RDA is able to produce the meso- and convective-scale features of the convective storms in a good accuracy and improve the short-term precipitation forecasting of the convective storms. Subjective and statistical evaluation results demonstrate that RTFDDA-RDA presents a reasonable capability for forecasting convective systems with improving the initial conditions and resulting in significant improvements of precipitation forecasting skills, especially for the 0–3-h nowcasting range. The sensitivity experiments on different latent heating schemes show that, the convective-stratiform separated heating scheme has the best performance of forecasts. Finally, intercomparison of different radar data assimilation approaches will be conducted in future. [ABSTRACT FROM AUTHOR]

The vertical distribution of the aerosol extinction coefficient serves as a basis for evaluating aerosol radiative forcing and air quality modeling. In this study, MODIS AOD data and ground-based lidar extinction coefficients were employed to verify 6 years (2009–2014) aerosol extinction data obtained via CALIOP for Southeast China. The objective was mainly to provide the parameterization scheme of annual and seasonal aerosol extinction profiles. The results showed that the horizontal and vertical distributions of CALIOP extinction data were highly accurate in Southeast China. The annual average AOD below 2 km accounted for 64% of the total layer, with larger proportions observed in winter (80%) and autumn (80%) and lower proportions observed in summer (70%) and spring (59%). The AOD was maximum in the spring (0.58), followed by the autumn and winter (0.44), and reached a minimum in the summer (0.40). The near-surface extinction coefficient increased from summer, spring, autumn and winter, in that order. The Elterman profile is obviously lower than the profiles observed by CALIOP in Southeast China. The annual average and seasonal aerosol profiles showed an exponential distribution, and could be divided into two sections. Two sections exponential fitting was used in the parameterization scheme. In the first section, the aerosol scale height reached 2200 m with a maximum (3,500 m) in summer and a minimum (1,230 m) in winter, which meant that the aerosol extinction decrease with height slower in summer, but more rapidly in winter. In second section, the aerosol scale height was maximum in spring, which meant that the higher aerosol diffused in spring. [ABSTRACT FROM AUTHOR]

The article reports that a tropospheric wind profiler was introduced to Xi Chong, Shenzhen, China in the year 2012. This profiler was used to obsersve the typhoon Usagi crossed Shenzhen in September 2013. It operates at a microwave frequency and measures the changes in refractivity associated with atmospheric discontinuities.

Turbulence kinetic energy (TKE) is widely used to characterize the spatio-temporal changes of atmospheric turbulence. The objective of this study is to investigate the near-surface TKE characteristics under the different convection regimes classified by the ratio of the buoyancy term (BP) to the shear term (SP) based on the sonic anemometer data measured at four boundary layer towers in China with contrasting underlying surfaces. The annual average diurnal variations of TKE and the related variables (BP, SP, friction velocity u ∗ , and wind speed u ¯) at the four towers are analyzed. Large differences exist in each variable between sites, leading to convection regimes differing by site. TKE varies significantly (up to 243%) with the convection regimes. Although the percentage of given convection regime varies greatly between sites, the results from the four towers show that TKE initially increases from the unstable to neutral atmospheric stratification conditions, and decreases from the neutral to stable conditions. Note that the ratio between BP and SP (BP/SP) is closely related to near-surface atmospheric stability. Except for BP, the other three variables (SP, u ∗ , and u ¯) have similar trends with TKE. The buoyancy term tends to decrease as BP/SP decreases. The correlation coefficients (R) between TKE and the related variables vary up to 0.33 (67%) with different convective regimes, whereas the variation trends of R between TKE and a given variable (BP and SP) are broadly similar at the four towers. This study indicates that the variation trend of TKE and its associations with BP and SP seem reliable under the different convection regimes. Our findings advance the understanding of near-surface turbulence and demonstrate that the different convection regimes should be considered when investigating TKE characteristics. • Convection regimes are classified by the ratio of buoyancy to shear term. • TKE differ significantly under different convection regimes at a given site. • TKE trends under different convection regimes are similar at the four towers. • Correlations between TKE and its related variables vary with convection regimes. [ABSTRACT FROM AUTHOR]

The outbreak of the 2019 novel coronavirus (COVID-19) had a large impact on human health and socio-economics worldwide. The lockdown implemented in China beginning from January 23, 2020 led to sharp reductions in human activities and associated emissions. The declines in primary pollution provided a unique opportunity to examine the relationship between anthropogenic emissions and air quality. This study reports on air pollutant and meteorological measurements at different heights from a tall tower in the Pearl River Delta. These measurements were used to investigate the vertical scale response of pollutants to understand reductions in human activities. Compared to that in the pre-lockdown period (from December 16, 2019), the concentrations of surface layer nitric oxide (NO x), fine particulate matter (PM 2.5), and daily maximum 8 h average ozone (MDA8O 3) declined significantly during the lockdown by 76.8%, 49.4%, and 18.6%, respectively. Although the vertical profiles of NO x and O 3 changed during the lockdown period, those of PM 2.5 remained the same. During the lockdown period, there were statistically significant correlations between PM 2.5 and O 3 but not between PM 2.5 and NO x at four heights, indicating that the main composition of PM 2.5 have dramatically changed, during which the impact of NO x on PM 2.5 became insignificant. Additionally, O 3 concentrations were also insensitive to NO x concentrations during the lockdown, implying that O 3 levels were more of a representative of regional background level. In this case, local photochemical formation is no longer a significant ozone source. This evidence suggests that it is possible to mitigation of PM 2.5 and O 3 levels simultaneously by significant reductions in anthropogenic emissions. [Display omitted] • The vertical profile of NOx, PM 2.5 and O 3 before and during COVID19 lockdown were observed from a tower in PRD, China. • Significant reductions in surface NOx, PM 2.5 and MDA8O 3 during the lockdown by 76.8%, 49.4%, and 18.6%. • Compared with the pre-lockdown, the vertical profiles of NOx and O 3 changed but PM 2.5 remain the same during the lockdown. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) models can determine the rainfall flooding threshold as a line. projected in a plane spanned by two principal components, thereby providing a binary result (flood or no flood). • Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, greatly raise the accuracy (ACC) to 96.5% and lowering the false alert rate to 25%. • Rainfall threshold based flood prediction can be executed rapidly and simply, this method allows decision makers time for a high-level assessment of flood risk, providing valuable lead time for citizens in the flood-prone areas to be warned. Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning. [ABSTRACT FROM AUTHOR]

A number of squall line passages over the Pearl River Delta (PRD) are documented for the first time based on the wind data collected by a 356 m-high meteorological tower with high temporal resolution (10 Hz). The mean wind and turbulence characteristics are studied based on a sample of six cases. The mean wind profile is consistent with the international standard for wind engineering, but the turbulence intensity profile has quite significant deviations. Moreover, the energy spectrum of the fluctuating wind is found to be consistent with the −5/3 law, as expected in the inertial subrange. For the purpose of wind gust nowcasting, the performance of a nowcasting algorithm based on upper air wind and thermodynamic profiles is examined using the limited dataset. The results highlight that the mean wind gust estimates are sufficient to nowcast the surface wind gust as measured by the anemometers, but for an extreme squall line case, the maximum wind gust estimates would be useful. The information in this paper is believed to be useful for wind engineering applications and weather nowcasting for wind gusts associated with subtropical squall lines, and, to the best of the authors' knowledge, this is the first study of its kind for the PRD region. [ABSTRACT FROM AUTHOR]