Search Results (109)

To investigate the influence of complex terrain on precipitation characteristics in the Jinsha River Basin (JRB), this study analyzes the spatiotemporal distribution of precipitation amount, frequency, and intensity under different topographic factors in the middle and lower reaches of the JRB (MLJRB), based on hourly precipitation observations from 1745 ground stations deployed by the China Meteorological Administration. The results indicate the following: (1) Precipitation amount increases gradually from low altitudes, peaks at sub-high altitudes, and then decreases. The highest precipitation frequency occurs at high altitudes, while the greatest precipitation intensity is observed at mid altitudes. (2) Spatially, a high-precipitation center with high frequency and intensity is formed in the lower reaches of the JRB, whereas the northern part of the study area exhibits a low center for both frequency and intensity. (3) Pronounced diurnal and monthly variations are observed at all altitudes. Precipitation amount and intensity peak during nighttime hours. On a monthly scale, both precipitation amount and intensity increase from May to July or August and then decrease, while the trend for precipitation frequency is not entirely consistent. (4) Precipitation amount shows little change with increasing slope gradient. Precipitation frequency increases with slope gradient, whereas precipitation intensity exhibits a clear decreasing trend. Eastern slopes receive higher precipitation amount and frequency compared to other aspects, followed by southern slopes, with western slopes receiving the lowest; however, differences in precipitation intensity among different slope aspects are minimal. In conclusion, the MLJRB exhibits strong spatiotemporal variability, distinct vertical differentiation, and pronounced periodic variation in precipitation. Precipitation frequency and intensity in this region are also associated with micro-topography. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we identified and characterized warm rain cases, and second, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, and weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern. Full article

The vertical distribution of atmospheric aerosol layers plays a fundamental role in understanding their climatic and environmental effects. Using one year of lidar observations in Jinhua, together with ground-based meteorological measurements and ERA5 reanalysis data, this study develops an integrated analytical framework to investigate the structural characteristics of aerosol layers in Mie-scattering profiles and their meteorological driving factors. K-means clustering identifies three representative aerosol layer structure types: single-layer concave, single-layer convex, and multi-layer profiles. By combining the Boruta algorithm with a random forest model, the dominant meteorological factors associated with each structure type are quantified across four boundary-layer stages (00–06, 06–12, 12–18, 18–24 LT). Temperature, humidity, wind speed, wind direction, divergence, and vertical velocity exhibit distinct influences across different boundary-layer conditions, revealing differentiated regulatory mechanisms governing aerosol layer structure change. The proposed framework establishes a coupled perspective between atmospheric dynamic/thermodynamic processes and aerosol layer structure formation, providing a basis for refined modeling of aerosol evolution and improved understanding of aerosol–meteorology interactions. Full article

Soil moisture is a critical variable in the eco-hydrological processes of arid regions; however, the vertical stratified mechanisms of soil moisture response to meteorological factors in artificial grassland remain inadequately quantified. Based on 10-min interval monitoring data from 2015 to 2024 in the middle reaches of the Heihe River, this study investigated the dynamics of soil moisture within a 0–160 cm depth profile in an arid artificial grassland. By integrating the Mann–Kendall trend test, Pearson correlation, time-lagged cross-correlation, multiple regression analysis and redundancy analysis, we systematically investigated the changing relationships between meteorological factors and soil moisture. The results revealed the following: (1) main meteorological factors driving surface processes (e.g., net radiation, air temperature, vapor pressure deficit) showed significant increasing trends with strong variability, while relative humidity decreased significantly, and these findings collectively point to a general trend of warming and drying in the region; (2) WS, Ta, rainfall, and RH are the principal factors explaining soil moisture variations, wherein temperature and humidity exhibit positive correlations with soil moisture; (3) RDA results showed that shallow soil moisture (0–20 cm) was primarily governed by air temperature and rainfall, whereas deep soil moisture was increasingly regulated by vapor pressure deficit; (4) time-lagged cross-correlation analysis showed that the response time of soil moisture to rainfall almost increased with soil depth, while the correlation coefficient gradually weakened from 0.43 to 0.06. This study quantitatively elucidates the stratified control mechanism of meteorological factors on the vertical pattern of soil moisture, contributing to a deeper understanding of the response of eco-hydrological processes under climate change and providing a scientific basis for water resource management, agricultural planning, and climate prediction. Full article

Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane ($Z_e)$ and differential reflectivity ($Z_{dr})$. This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions. Full article

The frequency and intensity of tropical cyclones (TCs) in the Arabian Sea have increased in recent decades, heightening concerns regarding regional vulnerability and forecasting difficulties. This study examines the impact of the Madden–Julian Oscillation (MJO) on TCs activity—formation, frequency, and severity—over the Arabian Sea from 1982 to 2021. This study analyzes variations in convection, vertical wind shear (VWS), sea level pressure (SLP), and relative humidity (RH) across different MJO phases utilizing the best-track data from the India Meteorological Department (IMD), the Real-Time Multivariate MJO (RMM) index, and reanalysis datasets from the National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR). Results show that more than 80% of TCs form during the convectively active phases of the MJO (P1–P4). These phases have the most noticeable negative outgoing longwave radiation (OLR) anomalies, as well as higher mid-level moisture and low-pressure anomalies, which are good for cyclogenesis. On the other hand, suppressed phases (P6–P8) have positive outgoing longwave radiation, dry air in the middle troposphere, and high-pressure anomalies, which make it harder for TCs to form. While VWS is predominantly favorable during both active and inactive phases, thermodynamic and convective factors principally regulate the modulation of TC activity. The simultaneous presence of active MJO phases with positive Indian Ocean Dipole (pIOD) and neutral or El Niño conditions markedly increases TC frequency, highlighting a combined influence link between interannual–El Niño–Southern Oscillation (ENSO) and IOD– and intraseasonal (MJO) variability. Additionally, the association between MJO and the Indo-Pacific Warm Pool (IPWP) reveals that TC activity peaks during convectively active MJO phases under the second twenty years of this study, emphasizing the influence of large-scale oceanic warming on TC variability. These findings underscore the critical function of the MJO in regulating TC activity variability in the Arabian Sea and stress its significance for enhancing intraseasonal forecasting and disaster preparedness in the area. Full article

Submarine power cables for offshore wind farms experience continuous cyclic loading from environmental forces and floating-platform motions, making fatigue performance a critical design factor. This study combined global and local analyses to investigate the fatigue behavior of a 66 kV dry-type submarine cable installed using a flexible pull-in installation system. A global dynamic analysis using site-specific meteorological and oceanographic data provided time-series displacement responses that were used to evaluate the fatigue damage to the metallic components of the cable. The results indicated that the minimum fatigue life of 8.71 × 10⁴ cycles occurred at the upper metallic sheath near the fixed end, with a corresponding cumulative damage of 1.147 × 10⁻⁵. Fatigue accumulation was predominantly governed by lateral (y-direction) displacement, while axial and vertical displacement components contributed minimally. Furthermore, the predicted fatigue life of the metallic sheath varied by a factor of up to 3.6 depending on the selected curve, comparing the cyclic stress amplitude and number of cycles to failure (S–N curve), highlighting the importance of accurate material fatigue data. These findings emphasize the need for careful evaluation of the environmental loading and sheath fatigue properties in flexible pull-in installation system-based submarine cable system designs. Full article

With the rapid advancement of clean energy, wind farm planning and construction are expanding worldwide, increasing the demand for accurate resource assessments. This study investigates the influence of vertical wind characteristics on wind farm siting and energy production, using measured meteorological data from the Hangjinqi wind farm. Results show that both mean wind speed increase substantially with altitude, indicating that upper layers provide richer and more stable wind resources. The estimated annual energy production of the site reaches 23,500 MWh, with capacity factors ranging from 35% to 42%, well above the national average. Seasonal and diurnal variations are evident: wind speeds strengthen during winter and spring, particularly at night, while turbulence intensity peaks in the daytime and decreases with height. Carbon dioxide (CO₂) reduction also increases with hub height, with the most pronounced seasonal reductions in spring (3367.6–5041.1 tCO₂, +49.7%) and winter (3215.7–5380.0 tCO₂, +67.4%), equivalent to several thousand tons of standard coal per turbine annually. Optimal performance is observed at 100–140 m, demonstrating efficient utilization of mid- to high-altitude resources. Nevertheless, discrepancies in turbine performance at different hub heights suggest untapped potential at higher elevations. These findings highlight the importance of incorporating vertical wind characteristics into wind farm siting decisions, and support the deployment of turbines with tower heights ≥140 m alongside intelligent scheduling and forecasting strategies to maximize energy yield and economic benefits. Full article

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation. Full article

Local thunderstorms are among the major meteorological hazards in the Australian region. These storms inherently have compound impacts, including hail, flash floods, and wind gusts, and consistently cause some of the highest insured losses. Studies on the climate change impact on local storms face the challenges of unreliable storm climatology and uncertainties in the numerical modeling of physical processes. In this study we have adopted an approach to examining the ingredients of severe storm development based on regional climate simulations. We examined two generations of NARCliM datasets (NSW and Australian Regional Climate Modeling). Projected changes in convective indices for the latter half of the twenty-first century indicate an environment more conducive to thunderstorm development, primarily due to enhanced atmospheric instability, despite a concurrent increase in convective inhibition. A measure that combines the dynamic factor of vertical wind shear further shows that the potential storm days will increase substantially, such as a doubling of days with storms during summer, under the influence of climate change over tropical, eastern, and southeastern Australia. The storm season in a year is also expected to elongate. These projections imply increasing thunderstorm-related hazards in the future, including hail, flood, and high winds. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

In the process of revitalizing historic districts, creating a healthy living environment requires a focus on the microclimate comfort of historic districts. Microclimate comfort refers to the comprehensive physiological perception and psychological satisfaction of climate elements such as heat, wind, and humidity under specific local environmental conditions, typically within a spatial range of horizontal scale < 100 m and vertical scale < 10 m. Among these, wind environment quality, as a key factor influencing pedestrian health experiences and cultural tourism appeal, holds particular research value. This study takes the Dabao Island Courtyard District in Qingdao as its subject, employing computational fluid dynamics (CFD) simulation methods from the artificial intelligence (AI) technology framework for modeling. CFD is a numerical method based on computer simulation, which solves fluid control equations (such as the Navier–Stokes equations) through iterative optimization to achieve high-fidelity simulation of physical environments such as airflow, turbulence, and heat transfer. A three-dimensional geometric model of the Dabao Island courtyard district was established, and boundary conditions were set based on local meteorological data. Numerical simulations were conducted to analyze the wind environment before and after the renovation of different layouts, functional spaces, and spatial scales (individual courtyards, clustered courtyards, and surrounding neighborhoods) of the courtyard district. The results indicate that factors such as building layout, street orientation, and renovation strategies significantly influence the wind environment of the Dabao Island neighborhood courtyards, thereby affecting residents’ perceptions of wind comfort. For example, unreasonable building layouts can lead to excessive local wind speeds or vortex phenomena, reducing wind comfort, whereas reasonable renovation and update strategies can facilitate the introduction of wind corridors into the historical courtyard buildings, improving wind environment quality. This study contributes to better protection and utilization of traditional neighborhoods during urban renewal processes, creating a more comfortable wind environment for residents, providing scientific decision-making support for the renovation of historical neighborhoods under the Healthy China strategy, and offering methodological references for wind environment research in other similar traditional neighborhoods. Full article

In this study, the PM_2.5 pollution transport budget in the atmospheric boundary layer (ABL) of Beijing–Tianjin–Hebei (BTH) and Chengdu–Chongqing (CY) was quantitatively evaluated from the perspective of horizontal and vertical exchange. Based on the aircraft meteorological data relay (AMDAR) observation data, the study found that the vertical exchange process of pollutants is mainly influenced by the combined effects of meteorological conditions and topographical factors. Meteorological factors determine the direction and intensity of the vertical exchange, while the complexity of the terrain affects the exchange pattern through local circulation and air flow convergence. The characteristics of the pollution transport budget between the BTH and CY regions show that the BTH region has a net output of pollutants throughout the year, while the CY region has a net input of pollutants. The total transport budget of the four typical representative seasons in BTH is negative. It indicated that BTH, as the region with the highest intensity of air pollution emission in China, is dominated by outward transport of air pollutants to surrounding regions. Due to the influence of topographic and meteorological conditions in the CY region, the air pollutants tend to accumulate in the basin rather than diffuse. The transport budget relationship of the four seasons is positive and the input of air pollutants can be obviously simulated. Combined with the results of the vertical wind profile, Beijing is more vulnerable to the prevailing cold air sinking in the northwest in winter, which is characterized by the inflow of the free troposphere (FT) into the ABL. As for Chongqing, it is blocked by mountains so that the gas convection at the top of the ABL is obvious. This horizontal convergence phenomenon induces upward vertical movement, which makes Chongqing show a strong characteristic of the ABL transport to the FT. Full article