Atmosphere, Volume 14, Issue 12 (December 2023) – 132 articles

The Cantareira Water Production System (CWPS), which supplies water to the most populous region in Brazil, is facing significant challenges due to hydroclimate change, thus threatening its water security. This research integrates data from climate models and field observations with hydrological modeling, aimed at quantifying trends in key variables of the hydrological cycle. The GFDL-CM4 climate model, the most suitable for the study area, was employed to generate runoff data under both current conditions and future scenarios (SSP2-4.5 and SSP5-8.5). Our analysis reveals an increasing trend in the frequency of dry hydrological years. The Standard Precipitation Index (SPI) and Drought Magnitude (DM) confirm an increase in both the occurrence and duration of droughts in future scenarios. The runoff in all basins was reduced, causing a substantial decrease in minimum flows of 16.9%, medium flows of 11.8%, and high flows of 9.2% for the SSP5-8.5 scenario. This research introduces an approach to hydroclimate impact assessment, combining rigorous data analysis with advanced modeling techniques. Our findings not only provide a comprehensive understanding of the challenges faced by the CWPS, but also offer critical quantitative insights essential for developing effective public policies and adaptive strategies for sustainable water resource management. Full article

Quantifying the level of CO₂, the main greenhouse gas (GHG), is essential for research on regional and global climate change, especially in the densely populated North China Plain with its severe CO₂ emissions. In this study, 12 airborne flights were managed and conducted during the autumn–winter period of 2018–2019 in downtown Shijiazhuang and its surrounding areas, which are representative of the typical urban conditions in the North China Plain, to explore the spatial and temporal distributions of CO₂. The results showed that the measured columnar averages of CO₂ ranged between 399.9 ± 1.5 and 443.8 ± 31.8 ppm; the average of the 12 flights was 412.1 ppm, slightly higher than the globally averaged 410.5 ± 0.20 ppm and the 2 background concentrations of 411.6 ± 2.1 ppm and 411.4 ± 0.2 ppm in low-latitude Mauna Loa and middle-latitude Waliguan in 2019, indicating the potential influences of anthropogenic activities. The typical stratification of the planetary boundary layer (PBLH), residual layer (RL), and elevated inversion layer (IL) was crucial in constraining the high CO₂ concentrations. This illustrated that the warming effect of CO₂ within the PBLH may also have some influences on regulating the thermal structure of the low troposphere. Based on a backward trajectory analysis, it was evidenced that there were three different categories of air masses for autumn and one category for winter. Both trajectories in the PBL, i.e., below 1000 m, from the local and southern areas with tremendous anthropogenic emissions (autumn) and from the western regions (winter) led to comparatively high levels of CO₂, but the mid-tropospheric CO₂ concentrations above 1000 m were commonly homogeneously distributed, with higher levels appearing in winter because the concentration in the free troposphere followed the global seasonal pattern, with a summer minimum and winter maximum as a result of the seasonality of the net CO₂ exchange and the balance between photosynthesis and respiration. These results provide an in-depth understanding of the vertical concentrations of tropospheric CO₂ in the North China Plain, which will offer scientific references for the evaluation of carbon accounting and carbon emissions. Full article

The FY-4B satellite is one of the second generation of China’s geosynchronous meteorological satellites aiming at numerical weather forecasts. The space environment monitoring package (SEMP) onboard the FY-4B is a comprehensive instrument package for plasma, high-energy particle, and energetic neutral particle measurements. The low-energy particle analyzer (LEPA) is one of the instruments of the SEMP and consists of two top hat electrostatic analyzers designed for plasma detection. The electron and ion sensors are back-to-back assembled and are integrated to a shared electronic box. It measures the three-dimensional velocity distribution of low-energy electrons and ions on the geosynchronous orbit. In this paper, we present the ground calibration and in-flight performance of the instrument. With the electrostatic deflectors and the cylindrically symmetric structure, the instrument provides high-cadence measurements of electron and ion velocity distributions with a wide field of view (FOV) of 180° by 100°, an angular resolution of 16.7° × 20°, and a broad energy range for both the electrons and ions from tens of eV to above 30 keV, with a 1 s time resolution. The geometric factors of the electron and ion analyzers are 1.1 × 10⁻³ cm²·sr·eV/eV and 1.4 × 10⁻³ cm²·sr·eV/eV, respectively, which fulfills the requirements of the low-energy plasma measurement. The LEPA monitored typical space environment disturbance such as geomagnetic storms and successfully recorded the responses of plasma energy fluxes. Satellite surface charging events were measured, with the highest potentials of −2000 V in the shadow period and −500 V in the nonshadow period. Full article

The formaldehyde (HCHO) dataset collected from January to December 2022 at the CNR Liberti Observatory (42.10° N; 12.64° E), a rural site located 30 km NE of Rome, is reported. The daily, weekly, and seasonal trends are examined. The highest average seasonal HCHO concentration (1.9 ppb) was measured during summer, whereas similar values (1.5 ppb) were found for winter and spring periods. The meteorological parameters monitored at the site allowed the interpretations of the maxima observed during the period investigated. The daily trends examined for the different seasonal periods, along with other pollutants available (NO₂, NO, and O₃), showed how the sources gradually shifted from primary to secondary. The occurrence of wildfires and other events in the area were also considered in explaining peak events (>4.2 ppb). The site examined was sometimes impacted by the nearby urban anthropic pressure of Rome, but in many cases, particularly during the summer months, the influence of the natural background surrounding the site was evident. Full article

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1${}^{\circ }$ by 0.1${}^{\circ }$ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin. Full article

Rapid urbanization and industrialization have heightened concerns about air quality worldwide. Conventional air purification methods, reliant on chemicals or energy-intensive processes, fall short in open spaces and in combating emerging pollutants. Addressing these limitations, this study presents a novel water-film air purification prototype leveraging the adhesion between low-curvature liquid surfaces and air convection friction. Uniquely designed, this prototype effectively targets toxic gases (e.g., formaldehyde, SO₂, NO₂) and particulate matter (such as PM_2.5) while allowing continuous airflow. This research explores the adhesion and sedimentation capabilities of a low-curvature water solution surface under convection friction, reducing the surface energy to remove airborne pollutants efficiently. The prototype was able to reduce the initial concentration in a 30 m³ chamber within 180 min by 91% for formaldehyde, 78% for nitrogen dioxide (NO₂), 99% for sulfur dioxide (SO₂), and 96% for PM_2.5. Experimentally validated indicators—decay constants, CADR, and purification efficiency—enable a comprehensive evaluation of the purification device, demonstrating its efficacy in mitigating air pollution. This innovative design, which is cost-effective due to its use of easily accessible components and water as the primary medium, indicates strong potential for large-scale deployment. This study points to an environmentally friendly and economical approach to air purification, shedding light on a promising direction for enhancing indoor air quality. Further optimization and exploration of diverse pollutants and environmental conditions will propel the practical applications of this pioneering technology. Full article

This paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO₂ and CH₄ measured at three distinct experimental sites in the city. The period of study was July 2021. This study also included a comparison between ceilometer data and lidar data, which demonstrated the favorable applicability of the ceilometer data for PBLH estimation. An examination of the correlation between changes in average CO₂ concentrations and PBLH values revealed stronger correlations for the IAG and UNICID stations, with correlation coefficients (ρ) of approximately −0.86 and −0.85, respectively, in contrast to the Pico do Jaraguá station, which exhibited a lower correlation coefficient of −0.42. When assessing changes in CH₄ concentrations against variations in PBL height, the retrieved correlation coefficients were approximately −0.78 for IAG, −0.66 for UNICID, and −0.38 for Pico do Jaraguá. The results indicated that CO₂/CH₄ concentrations are negatively correlated with PBL heights, with CO₂ concentrations showing more significant correlation than CH₄. Additionally, among the three measurement stations, IAG measurements displayed the most substantial correlation. The results from this study contribute to the understanding of the relationship between PBLH and greenhouse gas concentrations, emphasizing the potential of remote sensing systems like ceilometers in monitoring and studying atmospheric processes. Full article

Due to the advantages of a short cycle, low investment and low energy consumption per ton of steel, short-process electric furnace steelmaking is about to welcome a golden period of rapid development in China. During the operation of the electric furnace, a large amount of smoke and dust is generated. Most studies focus on organized emissions, and the impact of unorganized emissions in workshops on the environment cannot be ignored. This paper evaluates the thermal environment in the electric furnace steelmaking workshop based on the analytic hierarchy process and obtains the influence weight of the fugitive emission location. The mass concentration of dust at each measuring point increased by 1.17 mg/m³ on average, and the concentration of unorganized emission dust near the outlet was 23.572 mg/m³. The numerical simulation calculation model is established by the CFD method, a fixed initial jet velocity is set, the initial velocity of the ladle soot plume is changed, and the inclination angle, arrangement height and dust removal air volume of the dust hood are respectively adjusted in different tapping periods. The impact of simulation on the efficiency of dust collection for different dust removal hood configurations was investigated, considering variations in inclination angle, arrangement height and dust removal airflow. The optimal structural parameters for the dust removal hood were determined to be an inclination angle of 60° and an arrangement height of 2.4 m, and an optimal dust removal airflow was determined to be 110,000 m³/h. This study provides a theoretical foundation for engineering practice. Full article

The successive tidal force (TF) at the epicenter of the Jiashi M6.6 earthquake in Xinjiang, China, was calculated for the period from 13 December 2019 to 10 February 2020. With periodic changes in tide-generating forces, the variations in the electron density (Ne) data recorded by the China Seismo-Electromagnetic Satellite (CSES) and outgoing longwave radiation (OLR) data provided by NOAA on a large scale at N25°–N55°, E65°–E135° were studied. The results show that (1) in the four cycles during which the TF changes from trough to peak, the earthquake occurred during one peak time when the OLR changed around the epicenter via calm–rise processions and in other similar TF phases, and neither an increase in the OLR nor earthquake occurred. (2) With a change in the TF, the spatiotemporal evolution of the OLR from seismogenic processes to its occurrence was as follows: microenhancement–enhancement–microattenuation–enhancement–calmness; this is consistent with the evolution of outward infrared radiation when rocks break under stress loading: microrupture–rupture–locking–accelerated rupture–rupture. (3) Ne increased significantly during the seismogenic period and was basically consistent with OLR enhancement. The results indicate that as the TF increases, the Earth’s stress accumulates at a critical point, and the OLR increases and transfers upward. The theoretical hypothesis underlying the conducted study is that the accumulated electrons on the surface cause negatively charged electrons in the atmosphere to move upward, resulting in an increase in ionospheric Ne near the epicenter, which reveals the homology of seismic stress variations in the spatial coupling process. The quasi-synchronous change process of these three factors suggests that the TF changed the process of the stress accumulation–imbalance in the interior structure of this earthquake and has the effect of triggering the earthquake, and the spatiotemporal variations in the OLR and ionospheric Ne could be indirect reflections of in situ stress. Full article

Hail is a type of severe convective weather disaster characterized by abundant water vapor and strong updrafts, resulting in intense and high reflectivity echoes in hail clouds, often accompanied by an overhanging form. Although hail research has made great progress, it is still challenging to achieve accurate identification of hail. Compared with traditional radar, dual-polarization radar can output a variety of polarization parameters and provide information about the shape and phase of precipitation particles, which is conducive to the identification of hail particles. In this study, dual-polarization radar data are used to explore more hail features from various perspectives, starting with the morphological characteristics of hail clouds and using common feature extraction methods in the field of image processing. A comprehensive approach to high-dimensional features is developed. Using machine learning methods, hail identification models are constructed in both the traditional mechanism feature space and the new feature space constructed in this study. Experimental results strongly confirm the significant effectiveness of the five-dimensional new mechanism features developed in this paper for hail identification. Full article

The concentric impact rings of the Vredefort Crater contain rocks with elevated uranium concentrations resulting from the geological signature of a meteoric impact. The decay of this uranium was estimated to lead to elevated indoor radon concentrations in the Crater, but such a study has never been carried out. This study explores the relationship between the natural radionuclides found in the geology of the Vredefort Crater and indoor radon concentrations. This was achieved through soil sampling and radionuclide surveys conducted on three impact rings, supplemented by indoor radon measurements in dwellings found in the area. In situ measurements revealed that one impact ring had higher-than-average uranium concentrations at 50 Bq/kg. Surprisingly, the measured indoor radon levels were lower than expected (113 Bq/m³). These measurements were taken during the COVID-19 pandemic and colder months, conditions that would typically result in elevated indoor radon levels. Soil samples indicated uranium activity of 30 Bq/kg, comparable to the world average of 35 Bq/kg. However, defunct mine tunnels in the area exhibited elevated radon concentrations, averaging 364 Bq/m³. The disparity between expected and measured indoor radon levels was attributed to the composition of surficial deposits, bedrock, and architectural features of the dwellings preventing radon accumulation. Full article

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production. Full article

Appropriate greening design can enhance the microclimate of residential areas. This study investigated different greening cases for residential buildings in hot summer–cold winter zones. Four sorts of greening layouts were tested in a residential area in Chongqing, China. Arbor–grass mix and arbor–shrub–grass mix showed effective cooling and humidifying effects, and were chosen for further study using the ENVI-met model. The simulations were conducted in Chongqing, comparing sixteen greening cases for determinant and enclosed building forms. Results indicate that the greening design for determinant layout should give priority to ensuring the greening area and shortening the distance from the sidewalk. While enclosed layout should concentrate greening in dense populations, using arbor–shrub–grass mix to improve the wind environment. In cases where the distribution of arbors and shrubs covers a ratio of 7:4, constituting 30% of the overall green space, there is a reduction in environmental temperature by 1.4 °C and in PET by 4.8 °C. This study provides the optimal greening layout for two types of residential areas in China’s hot summer–cold winter zones, guiding landscape construction in these residential areas to optimize the microclimate. Full article

The Experiment to Study Thunderstorm High-Energy Radiation (ESTHER) is a small project of the Italian National Institute for Astrophysics (INAF), devoted to the study of high-energy emissions from thunderstorms, such as Terrestrial Gamma-ray Flashes and gamma-ray glows, which will start in 2024. In order to reduce the absorption typically undergone by gamma-ray radiation in the lower layers of the atmosphere and make these events detectable on the ground, the ESTHER set-up will be installed at high altitudes on Mt. Etna (Italy). We carried out a detailed analysis of lightning occurrence in this geographic region in order to test how suitable such a location is for the installation of a detection system to investigate thunderstorms and related emissions. The analysis pointed out a strong clustering of lightning in the proximity of the mountain peak and over the main volcano craters, where the frequent presence of volcanic ashes could increase, under the conditions of humid air typical of thunderstorms, electrical conductivity. An estimate of the gamma-ray absorption in the air undergone by typical TGF radiation allowed us to evaluate the suitability of two possible installation sites suggested for the project. This study represents a preliminary work for ESTHER and serves as a launching pad for future analyses. Full article

Marine heatwaves (MHWs) are becoming more frequent and intense in many regions around the world, as well as in China’s marginal seas. However, the seasonal characteristics and associated physical drivers of MHWs are largely unknown. In this study, we analyze, based on multiple reanalysis and numerical model data, the seasonal characteristics and causes of MHWs in the South China Sea (SCS) over a near 30-year period (1991–2022). There exist significant seasonal variabilities in the spatiotemporal features and formation mechanisms of MHWs. MHWs in the SCS show significant increasing trends in terms of frequency, duration, and intensity. MHWs during the summer half-year are stronger than the winter half-year as a whole, with them being more likely to occur over the eastern SCS in the summer half-year and the western region in the winter half-year. However, the increasing trend of MHWs in the winter half-year exceed those in the summer. Additionally, we find that MHWs are associated with the unusually strong west Pacific subtropical high (WPSH) both in the summer and winter half-years. Nevertheless, the dominant factors for MHWs are different in the varied seasons. According to upper ocean temperature equation analysis, surface heat flux anomalies (especially shortwave radiation flux) are major effect factors in the summer half-year, while ocean dynamic processes play the main role in the winter half-year. An analysis of the typical MHWs also proves this conclusion. Moreover, MHWs occurring in winter are often accompanied by temperature anomalies within the mixed-layer depth. The findings imply that the formation mechanisms and space–time distribution of MHWs exist with a seasonal contrast in the SCS, rather than simply being due to large-scale circulation and flux anomalies. This may provide a useful reference for a deeper understanding and forecasting of MHWs under different seasons and weather. Full article

In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R²) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains. Full article

Analyzing the development and evolution characteristics of typhoons are conducive to improving typhoon monitoring and optimizing early warning models. Based on the deep learning model YOLOv5 and Himawari-8 data products, this study analyzes the movement path and cloud evolution of typhoon “Infa”. The specific conclusions of this study are as follows. (1) Based on the YOLOv5 model and brightness temperature perturbation algorithm, the central positioning of the typhoon is realized, where the Himawari-8 bright temperature image is used as the input of the model and the output of the model is the typhoon range boundary. The results show that this method was 90% accurate for monitoring ocular typhoons and 83% accurate for blind typhoons. The typhoon center location determined by the brightness temperature perturbation algorithm closely matched the CMA best-path dataset (CMA) (goodness of fit ≈0.99). (2) This study observed that as typhoons developed, cloud parameters evolved with the cloud cluster becoming denser. However, as the typhoon neared land, the cloud structure collapsed and cloud parameters decreased rapidly. (3) Changes in the typhoon cloud system were linked to topography and surface temperature. Changes in cloud optical thickness (COT) were influenced by the digital elevation model (correlation −0.18), while changes in cloud top temperature (CTT) and cloud top height (CTH) were primarily affected by surface temperature changes (correlation values: CTT −0.69, CTH −0.37). This suggests that the ocean environment supports the vertical development of typhoon clouds and precipitation. In summary, this study optimized the positioning simulation of typhoon movement paths and cloud change trends, and this is helpful for improving the early warning and response-ability of typhoons in coastal cities and for reducing the threat of typhoons to the daily life of residents in coastal areas. Full article

Indoor air quality (IAQ) directly affects human health. The increase in PM and CO₂ concentration indoors caused an increase in the prevalence of sick building syndrome (SBS) symptoms. Plants could contribute to reducing particulate matter (PM) and CO₂. This study identifies the most efficient evergreen plant species for improving indoor air quality by assessing the ability of five different indigenous Korean evergreen plant species to reduce PM and CO₂ and regulate humidity and temperature under indoor environmental conditions in acrylic chambers. The clean air delivery rates (CADR) were calculated to evaluate the efficacy of plants in reducing PM and CO₂. We assessed the performance of removing the five study plants on PM₁ (~0.68–3.01 m³/h/leaf area), PM_2.5 (~0.73–3.08 m³/h/leaf area), PM₁₀ (~0.67–3.04 m³/h/leaf area), and CO₂ (~0.48–1.04 m³/h/leaf area). The species Ilex pedunculosa, Pittosporum tobira, and Gardenia jasminoides were the most effective at reducing PM. The CADR of CO₂ also differed among the five plant species and corresponded to their photosynthetic rate. Viburnum odoratissimum var. awabuki, which had the high photosynthetic rate, was most effective at reducing CO₂. By contrast, PM reduction was correlated with plant leaf structure. Plants with a high leaf density can accumulate more PM. The plants were also able to control temperature and humidity. The average temperature of the control chamber was higher, and the humidity was lower than that of the plant chambers. In this study, the five evergreen species effectively reduced air pollutants and can be used to improve IAQ. Full article

In order to improve the inversion accuracy of stellar occultation data and to provide a reference for the selection of inversion methods with higher accuracy in the future, this study compared and analyzed the inversion effects of two different methods on the same set of data, which are the effective cross-section method and the onion-peeling method, respectively. Firstly, the inversion principle of the effective cross-section method is introduced in detail. The regularisation parameters and screening conditions for the observation data in the inversion process were clarified based on the ozone observation characteristics. Second, the algorithm was applied to invert the GOMOS observational data from 1 December 2002. The atmospheric radiative transmittance obtained from the observations was filtered, and the inversion results were compared with those obtained using the onion-peeling method. Third, the errors in the height distribution obtained by both methods were calculated using the GOMOS secondary results from 1 December 2002 as the reference value. Finally, the inversion errors of other trace components were computed to further validate the accuracy of the two methods. The results demonstrate that the effective cross-sectional method is more accurate for the inversion of ozone, particularly in low-altitude regions affected by refraction. The method achieved a maximum error of 1.2%, with an apparent magnitude of 2, an effective temperature greater than 10,000 K, and a regularisation parameter of 10¹⁵. Furthermore, when applying the same method to the inversion of nitrogen trioxide and calculating the error, it was observed that the results of both methods were comparable at altitude of 30–60 km, with an error value ranging from 0 to 2%. However, at approximately 25 km, the inversion accuracy of the onion-peeling method surpassed that of the effective cross-sectional method. This research provides a theoretical foundation for further investigation of the stellar occultation inversion method and enhancing the accuracy of inversions. Full article

With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM_2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on machine-learning algorithms, namely, the deep neural network (DNN), M5’ decision tree algorithm (M5P), M5’ rules decision tree algorithm (M5Rules), alternating model tree (AMT), and multiple linear regression (MLR). Based on the predictive results from these five models, the study evaluates the optimal model for forecast horizons and proposes a real-time PM_2.5 concentration prediction system by integrating various models. The results demonstrate that the prediction errors vary across different models at different forecast horizons, with no single model consistently outperforming the others. Therefore, the establishment of a hybrid prediction system proves to be more accurate in predicting future PM_2.5 concentration compared to a single model. To assess the practicality of the system, the study process involved simulating data, with a particular focus on the winter season when high PM_2.5 concentrations are prevalent. The predictive system generated excellent results, even though errors increased in long-term predictions. The system can promptly adjust its predictions over time, effectively forecasting the PM_2.5 concentration for the next 12 h. Full article

Very Low Frequency (VLF) waves radiated from ground-based transmitters are crucial for long-distance communication and underwater navigation. These waves can reflect between the Earth’s surface and the ionosphere for Earth–ionosphere waveguide propagation. Additionally, they can penetrate not only the ionosphere but also the magnetosphere, where they interact with high-energy particles in the radiation belt. Therefore, studying the spatial and temporal distribution of VLF radio signals holds significant importance. Such research enables us to understand the propagation characteristics of VLF signals, their interaction with radiation belt particles, and their response to space weather and lithospheric activity events. In this paper, we investigate the seasonal variations in the intensity of the Northwest Cape (NWC) transmitter (19.8 kHz) radio signals at satellite altitude and the displacement of the electric field’s peak center. Our analysis is based on the nightly China Seismo-Electromagnetic Satellite (CSES) data from 2019 to 2021. The results reveal the following: (1) There is no significant seasonal variation in the electric field strength within a small area (2.5° radius) around the NWC transmitter. However, a clear seasonal variation in the electric field strength is observed within a larger area (15° radius), with higher strength during winter compared with summer. (2) The power spectral density of the electric field remains constant within the peak central area (approximately 1~2° radius), but it decays with distance outside this region, showing a north–south asymmetry. Moreover, the decay rate of the radiation electric field is slower in the northern direction than in the southern direction. (3) The center of the electric field moves northward from summer to winter and southward from winter to summer. (4) In winter, VLF waves radiated by the NWC transmitter may predominantly propagate by being ducted toward the conjugate hemisphere. Full article

The icing on power transmission lines, as a major hazard affecting the safety of electricity usage in China during winter, poses a significant challenge in systematically evaluating the weather conditions and their distribution characteristics during the icing period. Understanding the interaction between the microterrain and micrometeorology and achieving a refined analysis of the physical mechanisms during the icing process remain difficult tasks in this field. These are crucial aspects for the development of more accurate icing prediction models across southern China. Therefore, this study provides a comprehensive review and summary of the current research state and progress in the study of power transmission line icing in southern China from three perspectives: (1) large-scale circulation characteristics; (2) microphysical process, terrain–atmosphere interaction, microtopography and local micrometeorological conditions for the occurrence of icing events; and (3) numerical icing event modeling and forecasting. This study also looks ahead to the scientific issues and technological bottlenecks that need to be overcome for the prediction of ice coating on power transmission lines in southern China. The goal is to provide guidance for the causal analysis and forecasting warnings of power transmission line icing in the complex microterrain of the southern region. Full article

The enormous heterogeneity and complexity of landscape patterns and their linkage with the hydrological responses have rarely been quantified and cataloged, especially in ungauged regions. This research therefore linked the landscape characteristics to hydrological processes using a newly developed runoff landscape index (RLI) at the watershed scale in Ardabil Province, northwest Iran. First, 11 common landscape metrics were calculated using Fragstats 4.2.1 software. Then, a runoff landscape index (RLI) was developed based on land cover (λC), soil (λK), and topography (λS) factors in 28 watersheds. Correlation and regression analyses were also conducted to determine the relationship between RLI, commonly used landscape metrics, and mean base flow. The spatial variations of all meaningful landscape metrics and RLI were considerable throughout the study watersheds. The mean values of λC, λK, and λS were found to be 2.78 ± 1.08, 0.50 ± 0.10, and 1.22 ± 0.30, respectively. The mean RLI varied from 0.00009 in the Lay Watershed with an area of 19.09 km² to 0.28 in the Boran Watershed with 10,268.95 km². The correlation coefficient (r > 0.42; p-value < 0.05) was obtained significantly between RLI and only five landscape metrics, including the largest patch index (LPI), landscape shape index (LSI), landscape division index (DIVISION), splitting index (SPLIT), and Shannon’s diversity index (SHDI). In addition, a regression model with R² of 0.97 and 0.67, respectively, in calibration and validation steps was established between river base flow as the dependent variable and main waterway length, LPI, LSI, SPLIT, modified Simpson’s diversity index (MSIDI), and λS as independent variables. The result confirms the significant interdependence of RLI and landscape characteristics, which can be used to interpret the landscape’s dynamic and its effects on hydrological processes. Full article

This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the overall LST, the land use types around the region, and the degree of interaction between surface objects. Finally, future experiments will be further explored through more time series LST and land use data. Full article

The heat transfer characteristics of porous rock layers (PRLs) have significant seasonal differences. This feature has been used to protect the permafrost subgrade under highways and railways from degeneration. However, in cold sandy environments, the transformation law of heat transfer characteristics of PRLs on account of climate warming and aeolian sand filling needs to be solved. This work developed a coupled heat transfer model for the soil–PRL system aimed at analyzing the convective heat transfer process and mechanism of a closed PRL. Furthermore, the impact of climate warming and sand filling on the cooling performance of the PRL under different mean annual air temperatures (MAATs) of −3.5, −4.5, and −5.5 °C was quantified. The numerical results indicated that the natural convection of the closed PRL occurred only in winter, and the effective convective height of the rock layer decreased with the sand-filling thickness. As the thickness of sand filling increased, the critical temperature difference for the occurrence of natural convection increased, accompanied by decreases in the Rayleigh number, the duration, and intensity of natural convection. When the sand-filling thickness exceeded 80 cm, natural convection would not occur in the PRL. Under a warming scenario of 0.052 °C·a⁻¹, the cooling performance of the PRL could offset the adverse impact of climate warming and raise the permafrost table in the first 20 years. Moreover, the closed PRL can be more effective in permafrost regions with colder MAATs. For cold sandy permafrost zones, sand-control measures should be taken to maintain the long-term cooling performance of the PRL. This study is of great significance in guiding porous rock embankment design and road maintenance along the Qinghai–Tibetan Railway. Full article

This study uses a limited area model to improve the understanding of assimilating Aeolus Level 2B wind profiles on a regional level under severe weather conditions. Aeolus wind profile measurements have offered new insights into weather analysis and applications. The assimilation of Aeolus Level 2B winds has enhanced the observed state of the atmosphere spatially and temporally in global modeling systems. This work is focused on the development and evolution of a Mediterranean tropical-like cyclone that occurred between 27–30 September 2018. Aeolus coverage had a good spatial and temporal alignment with the broader area and time periods during which the cyclone originated and developed, affording the opportunity to explore the direct influence of Aeolus satellite retrievals in model initialization processes. Using the WRF 3DVar modeling system, model results showcase the effects stemming from Aeolus data ingestion, with the main differences presenting after the first 24 h of simulation. Smaller or larger deviations in the runs with and without the Aeolus wind data assimilation are evident in most cyclonic characteristics, extending vertically up to the mid-troposphere. The absence of a consistent trend in cyclone intensification or weakening underlines the unique impact of the Aeolus dataset in each case. Full article

Gravity wave (GW) activities play a prominent role in the complex coupling process of wave–wave and wave–background circulation around mid-low-latitude and equatorial areas. The wavelengths are wide, from about 10 m to 100 km, with a period from minutes to hours. However, the oscillations of GW activities are apparently different between the period bands of 0.1 to 1 h (HF) and 1 to 5 h (LF). To further understand the characteristics of GW activities, the neutral winds during 2008–2009 with a resolution of 3 min obtained from a medium-frequency (MF) radar in Kunming (25.6° N, 103.8° E) were analyzed. Using two numerical filters, the HF and LF GWs were estimated. Interestingly, the power spectral density grows larger as the frequency increases. It linearly falls with decreasing frequency when the period is less than 2 h. The seasonal variations in both HF and LF GWs are strongly demonstrated in August–September, November, and February–March with maximum meridional variances of 1100 m² s⁻² and 500 m² s⁻² and maximum zonal variances of 800 m² s⁻² and 350 m² s⁻² in, respectively. The turbulent velocity was also calculated and shows similar oscillations with GW activities. Furthermore, the GW propagation direction exhibits strong seasonal variations, which may be dependent on the location of the motivating source and background wind. Full article

Previous studies have demonstrated that the influence of the El Niño–Southern Oscillation (ENSO) on the Euro-Atlantic atmospheric circulation varies considerably during the boreal winter. Compared to the late winter (January–March) relationship, the early winter (November–December) teleconnection is more uncertain and less understood. In this paper, we revisited this early winter regional ENSO teleconnection using the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and the European Centre for Medium-Range Weather Forecasting (ECMWF) fifth generation reanalysis (ERA5) datasets for the period 1979–2022. It was found that the signal projected well onto the second dominant mode of Euro-Atlantic atmospheric variability, the East Atlantic Pattern (EAP), rather than the previously mentioned North Atlantic Oscillation (NAO). This influence is associated with ENSO-induced dipolar convection anomalies in the Gulf of Mexico and Caribbean Sea (GMCA), which leads to an EAP via exciting Rossby waves propagating northward into the North Atlantic. We further revealed that this ENSO–EAP teleconnection underwent a pronounced interdecadal strengthening around the late 1990s. Prior to the late 1990s, the convective response to ENSO in the GMCA was weak. The atmospheric responses over the Euro-Atlantic were mainly driven by the ENSO-induced convective forcing in the tropical Indian Ocean, which favors an NAO-like pattern. In contrast, since the late 1990s, ENSO has induced stronger precipitation anomalies in the GMCA, which exert a dominant influence on the Euro-Atlantic atmospheric circulation and produce an EAP. These results have useful implications for the further understanding of ENSO-related early winter atmospheric and climate variability in the Euro-Atlantic region. Full article

Addressing the impacts of climate change requires, first of all, understanding the mechanisms driving changes, especially at the regional scale. In particular, policymakers and other stakeholders need physically robust climate change information to drive societal responses to a changing climate. This study analyses late 21st-century (2071–2100) precipitation projections for the Congo Basin under representative concentration pathway (RCP) 8.5, using the Rossby Centre Regional Climate Model (RCM) RCA4. Specifically, we examine the impact of the RCM formulation (reduction of turbulent mixing) on future change in seasonal mean precipitation by comparing the results of the modified model version (RCA4-v4) with those of the standard version (RCA4-v1) used in CORDEX (Coordinated Regional Climate Downscaling Experiment). The two RCM versions are driven by two global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). The results show that seasonal precipitation is largely affected by modifications in the atmospheric column moisture convergence or divergence, and, in turn, associated with changes in the dynamic (ΔDY) and thermodynamic (ΔTH) components of the moisture-budget equation. Projected decreased precipitation in the dry seasons (December–January–February and June–July–August) is linked to increased moisture divergence driven by dynamic effects (changes in circulation), with most experiments showing ΔDY as the main contributor (>60%) to the total moisture budget. Overall, precipitation is projected to increase in the wet seasons (March–April–May and September–October–November), which can be attributed to both dynamic and thermodynamic effects, but with a larger thermodynamic contribution (changes in specific humidity, ΔTH > 45%), compared to the dynamic one (ΔDY > 40%). Through a comparison of the two model versions, we found that the formulation (reducing turbulent mixing) and boundary conditions (driving GCM) strongly influence precipitation projections. This result holds substantial value for ensuring the fitness of models for future projections intended for decision-makers. Full article

To effectively address air pollution and enhance air quality, governments must be able to predict the air quality index with high accuracy and reliability. However, air quality prediction is subject to ambiguity and instability because of the atmosphere’s fluidity, making it challenging to identify the temporal and spatial correlations using a single model. Therefore, a new hybrid model is proposed based on an interpretable neural network and a graph neural network (INNGNN), which simulates the temporal and spatial dependence of air quality and achieves accurate multi-step air quality prediction. A time series is first interpreted using interpretable neural networks (INN) to extract the potentially important aspects that are easily overlooked in the data; second, a self-attention mechanism catches the local and global dependencies and associations in the time series. Lastly, a city map is created using a graph neural network (GNN) to determine the relationships between cities in order to extract the spatially dependent features. In the experimental evaluation, the results show that the INNGNN model performs better than comparable algorithms. Therefore, it is confirmed that the INNGNN model can effectively capture the temporal and spatial relationships and better predict air quality. Full article