Search Results (304)

The aim of this work was to reveal the differences in the macro- and microphysical characteristics and precipitation mechanisms of tropical precipitation systems (TPSs) in different regions. Based on the GPM satellite observation from 2014 to 2022, global TPSs were identified, and eight high-frequency areas were defined. Subsequently, their horizontal and vertical development, precipitation characteristics, and microphysical vertical structure were systematically analyzed. The results show that the horizontal development scale of TPSs is mostly between 10⁴ and 10⁵ km², with vertical development exceeding 10 km. The convective area fraction (CAF) ranges from 20% to 60%, and TPSs have a higher CAF and lower vertical development over the ocean than over land. Continental TPSs exhibit significantly stronger vertical development and more intense precipitation in convective cores than oceanic TPSs. The stronger vertical development over land is mainly attributed to stronger updrafts associated with topographic lifting, which further enhances ice-phase microphysical processes and increases ice particle size. Meanwhile, the intensified updrafts also lead to higher collision–coalescence efficiency in the liquid layer, and temperature perturbations over land further enhance turbulent collision efficiency. Together, these processes result in stronger precipitation intensity in the convective cores of continental TPSs. Stratiform regions are characterized by weak precipitation dominated by raindrop breakup with small regional differences. These findings clarify the key land–ocean disparities in TPSs and provide critical observational evidence for optimizing cloud microphysical parameterization schemes in numerical models. Full article

Detecting hail from remote sensing observations remains challenging because hail develops rapidly and its signatures may appear at different levels within a storm. Ground-based radar and geostationary meteorological satellites are the two primary observing systems for this task, yet their observations are often spatially misaligned. Satellite measurements mainly characterize the thermal structure near the cloud top, whereas radar observations capture the lower-level precipitation core. This mismatch is further exacerbated by satellite parallax, namely the apparent horizontal shift of high cloud tops caused by the oblique viewing geometry of a geostationary satellite, together with the vertical tilt of convective storms. Existing joint methods generally combine satellite cloud-top information with radar precipitation information directly, without explicitly correcting the spatial displacement, which limits detection accuracy. To address this issue, we propose HailDeformer, a deep learning framework that first aligns satellite and radar features through a bidirectional deformable cross-attention module equipped with a position-wise confidence gate and optimized with smoothness, contrastive alignment, and observation-structure consistency losses, and then refines the fused representation using an inter-scale attention module and a wavelet-guided refinement module. Experiments on a four-region dataset from China show that HailDeformer consistently outperforms Direct Fusion, Manual Weighting, Cross-Attention Fusion, and Optical Flow Alignment, achieving a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.916, an F1 score of 0.864, a Critical Success Index (CSI) of 0.760, and the lowest False Alarm Ratio (FAR) of 0.149. Ablation studies further confirm that all proposed modules and associated constraints contribute to the overall performance, with the alignment module providing the largest improvement. Additional evaluations demonstrate that HailDeformer remains effective throughout storm evolution and under challenging observational conditions. Full article

In recent years, screen pipe scaling and blockage have occurred in dozens of wells in the Fuling Shale Gas Field, seriously affecting the normal production of gas wells. Investigations show that similar problems exist in the Weirong Shale Gas Field of Sinopec Southwest Branch, and the Changning and Weiyuan Shale Gas Fields of PetroChina. Although well production has been restored through pipe inspection operations, key issues specific to shale gas wells remain unresolved, including the scaling mechanism under gas–liquid two-phase flow regimes unique to horizontal shale gas wells, the scale deposition law at screen pipes caused by complex flow direction changes, and the targeted prevention technologies for high-hardness BaSO₄ scale in high-salinity produced water. By jointly conducting research on the scaling mechanism and prevention technology of shale gas wellbores with Southwest Petroleum University, the Fuling Shale Gas Field has identified the reasons why the amount of BaSO₄ scaling increases with the decrease in pressure and temperature, while it increases with the increase in gas–water ratio. It has clarified the influencing characteristics of factors such as pressure, temperature, gas–water ratio and pipe wall roughness. The amount of scaling on the tubing wall of shale gas wells in this area is very small, and blockage mainly occurs at and near the screen pipe. Due to the complex flow direction change in gas and water in the screen pipe, the precipitated tiny scale particles separate, settle and accumulate, forming variable-diameter steps that continue to grow. Two agents have been developed: the LPPAS scale inhibitor and the barium-strontium-sulfate-chelating plug-removing agent, with a scale inhibition rate as high as over 90% and a scale dissolution rate over 70%, respectively, laying a foundation for the efficient and stable production of shale gas wells. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article

Wire–Laser Additive Manufacturing (WLAM) is a promising directed energy deposition technique for producing and repairing high-performance components with high material efficiency and strong metallurgical bonding. This study optimizes single-track Inconel 718 claddings deposited by WLAM on AISI 304L stainless steel and S355 structural steel substrates, focusing on the relationships between processing parameters, microstructure, post-deposition heat treatment, and mechanical performance. A systematic parametric assessment evaluated the influence of laser power, laser speed, wire feed rate, and shielding gas pressure on key quality metrics, including dilution, wettability, porosity, and cracking. Distinct optimal processing windows were identified for each substrate, reflecting their different thermal responses: for 304L, 8.5 kW laser power, 0.55 m/min laser speed, 5 m/min wire feed rate, and 2 bar argon; for S355, 9.6 kW laser power, 0.6 m/min laser speed, 4.9 m/min wire feed rate, and 4 bar argon. Post-deposition heat treatment markedly enhanced performance by dissolving Nb-rich interdendritic Laves phase and promoting γ′/γ″ precipitation. As a result, clad hardness increased from ≈225 HV 0.3 (as-built) to ≈412 H V0.3 after heat treatment (+84%). Tensile testing confirmed substantial strengthening, with yield strength increasing from 447 to 853 MPa (horizontal build) and from 488 to 960 MPa (vertical), while ultimate tensile strength rose from 824 to 1057 MPa (horizontal) and from 836 to 1090 MPa (vertical). Mechanical anisotropy remained significant, linked to columnar grain morphology and build orientation. Overall, the results provide practical process window and heat treatment guidelines for reliable industrial implementation of high-quality Inconel 718 claddings on steel substrates for demanding applications. Full article

With the increasing deployment of autonomous and semi-autonomous road vehicles, Advanced Driver Assistance Systems (ADASs) rely heavily on multi-modal sensing technologies to ensure safe and reliable operation. Among these sensors, Light Detection and Ranging (LiDAR) provides high-resolution three-dimensional environmental perception but is particularly vulnerable to adverse weather conditions such as snowfall. Snowfall can degrade LiDAR performance through signal attenuation, backscattering, false detections, and sensor surface contamination, ultimately reducing visibility and detection reliability. In this study, an experimental investigation was conducted in a climatic chamber to systematically assess LiDAR performance degradation under controlled snowfall conditions. Key parameters influencing sensor behavior, including chamber air temperature, precipitation intensity, and sensor orientation, were isolated and examined. Chamber temperature was varied to generate snow characteristics representative of dry and wet snow, while precipitation intensity was controlled by adjusting snow gun flow rates. Sensor orientation was modified to evaluate its effect on perceived precipitation and snow accumulation. The experimental results confirm the initial hypothesis that snowfall intensity, snow physical properties, and sensor orientation exert a significant influence on LiDAR performance degradation. Increasing precipitation intensity significantly accelerates both 3D target detection loss and 2D visibility reduction, with polynomial regression revealing a non-linear degradation response. Inclined sensor orientations exhibited more rapid performance deterioration compared to a horizontal configuration. These findings provide valuable insights into LiDAR vulnerability in snowy environments and support the development of mitigation strategies to improve ADAS and autonomous vehicle operation in cold climates. Full article

Deep convection strongly influences regional water cycles over the Tibetan Plateau (TP), often referred to as the “Asian Water Tower.” Using FY-2E thundercloud observations, we examined the deep convection characteristics over the central TP. Deep convective storms over the TP exhibit pronounced spatiotemporal heterogeneity. The frequency distribution of storm areas follows an exponential pattern in all seasons, and the cloud-top black body temperature (TBB) distribution is negatively skewed, with values concentrated between −40 and −36 °C. Deep convection is most active in summer, with storms that are larger and have colder cloud tops. In spring, storms are less frequent but tend to cover larger areas, whereas autumn is dominated by small- to medium-sized systems. Spatially, the southeastern and southwestern TP are high-frequency centers, with storm occurrence 2–3 times higher than in the northern TP. Associations between deep-convection properties and precipitation vary by season and region. In summer, storm-related precipitation is primarily linked to large storm areas, whereas in autumn it is more strongly associated with storms with lower TBB. In the southwestern TP, precipitation intensity is more strongly related to TBB, whereas in the northwestern TP, it is more sensitive to storm area. Topographic slope also modulates both precipitation and storm properties. Most storm precipitation occurs over slopes ≤14°, and heavy precipitation shows a bimodal dependence on slope, with peaks at 3–4° and 11–13°. Gentle slopes favor storm growth and horizontal expansion; as the slope increases, mean TBB increases, and deep convection weakens. Full article

Global climate problems and the sharp decline in biodiversity have attracted widespread attention. Orchids, as the “flagship” species of biodiversity, are important indicators of ecological changes. This study took Chongqing as the study area and conducted a comprehensive survey of orchids through field investigation combined with data review to clarify Chongqing’s diversity distribution pattern. The distribution of orchids was characterized by “high in the east and low in the west, high in the north and low in the south” horizontally. Vertically, the distribution was characterized by an obvious “unimodal distribution”, with higher abundance in the low and middle altitude areas of 500–1499 m. The minimum temperature of the coldest month (Bio6), isothermality (Bio3), altitude (Bio20), and precipitation of the wettest season (Bio16) were the main environmental factors affecting the distribution of the orchid habitat. The suitable habitat of orchids would be greatly reduced in the future (2070SSP-585), and the suitable habitat tends to migrate to the high-altitude areas; therefore, we should pay more attention to the conservation and sustainable use of orchid plant resources. Full article

The classification of precipitation clouds, particularly the identification of severe convective clouds, is of paramount importance for meteorological forecasting and disaster warning systems. Current precipitation cloud observations typically rely on either standalone satellites or ground-based meteorological stations conducting horizontal-layer detection, yet these methods suffer from limitations such as restricted detection ranges and single-source observation. Therefore, this paper employs multi-source satellite data to construct a vertically structured cloud-precipitation dataset. This dataset comprises four categories: clear skies, non-precipitating clouds, general precipitation clouds, and severe convective clouds. A self-developed DFConv attention mechanism is integrated into the UNet network framework to build the Vertical-UNet model for identifying precipitation cloud types within vertical structures. Experimental results demonstrate that Vertical-UNet achieves favorable performance in precipitation cloud classification using the vertical-structure precipitation cloud dataset. The probability of detection (POD) for precipitation clouds reaches 94.54%. The POD for severe convective clouds reached 87.29%, indicating an improvement of 19.9% compared to the CNN model and 17.04% compared to the UNet model. This conclusively validates the model’s efficacy and establishes a foundation for detecting vertically structured precipitation clouds from diverse satellites in future research. 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

In temperate freshwater ecosystems, zooplankton play a crucial role in the pelagic food web and act as sensitive indicators of environmental change. They respond to shifts in water temperature, hydrodynamic mixing, and short-term meteorological events. This study investigated the epilimnetic zooplankton fauna of Lake Karamurat (Bolu, Türkiye), a small tectonic temperate lake, with a specific focus on the influence of rainfall events and wind speed on community structure. The samples were taken seasonally and horizontally using a plankton net (55 µm mesh size) and were analyzed alongside in situ physico-chemical measurements and meteorological data. In total, 74 zooplankton taxa were identified, comprising 54 rotifer species and 20 crustacean species (16 Cladocera and 4 Copepoda). Testudinella greeni was recorded for the first time in Türkiye, representing a new addition to the Turkish Rotifera fauna. Multivariate analyses revealed that electrical conductivity, water temperature, dissolved oxygen, precipitation, and wind speed were key drivers shaping community composition. The findings suggest that wind-driven surface mixing and episodic rainfall events enhanced vertical redistribution, leading to dominance of rotifers and small-bodied cladocerans in the epilimnion. These findings underscore the critical role of sampling strategy in shallow lakes under dynamic conditions and provide new faunistic insights into the zooplankton diversity of Anatolian lakes. Full article

Investigating the spatial distribution and dynamics of terrestrial carbon storage is vital for climate change mitigation. However, horizontal spatial analyses often overlook heterogeneity in complex terrains. Here, we focused on the Gonghe Basin on the northeastern margin of the Qinghai–Tibet Plateau, where resource exploitation and ecological conservation interact. By using land use and DEM data and integrating the InVEST model, Geoda, and a geographical detector, we showed the altitudinal gradient effect and spatiotemporal evolution of carbon storage in the Gonghe Basin from 2000 to 2020 and identified the key factors influencing these patterns. Results show the following: (1) From 2000 to 2020, carbon storage in the Gonghe Basin exhibited a distinct pattern of “high at mid-elevations, low at both summit and valley” along the elevation gradient. High-value areas were concentrated in the forest–grassland zone between 2800–4400 m, while low-value areas were distributed in the human activity-intensive zone of 2100–2800 m and the alpine desert zone of 4400–5000 m. (2) The synergistic drivers of carbon storage differed markedly across elevation gradients. The low-elevation zone (2100–2800 m) was characterized by strengthened interactions between vegetation cover and precipitation as well as human activity variables, indicating a coupled natural–anthropogenic driving regime. In the mid-elevation zone (2800–4400 m), interactive effects shifted from vegetation–natural factor coupling to enhanced synergy with social factors such as population density. In the high-elevation zone (4400–5000 m), stable long-term interactions between vegetation and temperature predominated, while sensitivity to interactions involving human activity factors increased. (3) Although natural factors remained dominant, the explanatory power of human activity factors—including GDP density, land-use intensity, and grazing intensity—increased over time across all elevation gradients, suggesting progressively stronger human intervention in carbon cycling. (4) Based on these findings, this study proposes a “three belts–three strategies” synergistic governance framework—“regulation and restoration” for the low-elevation belt, “conservation and efficiency enhancement” for the mid-elevation belt, and “monitoring and early warning” for the high-elevation belt—aiming to enhance regional carbon sink capacity and ecological resilience through zone-specific, targeted interventions. These findings offer a scientific basis for reinforcing regional ecological security and improving carbon sink management. Full article

Accurate analysis and forecasting of short-term heavy rainfall (hourly rainfall ≥ 20 mm) are crucial for extending warning, enabling targeted preventive measures, and supporting efficient resource allocation. In recent years, machine learning techniques combined with atmospheric physical variables have offered promising new approaches for analyzing and predicting and forecasting short-term heavy rainfall. However, these methods often lack transparency, which hinders the interpretation of key atmospheric physical variables that drive short-term heavy rainfall and their coupling mechanisms. To address this challenge, the present study integrates the interpretable SHAP (SHapley Additive exPlanations) framework with machine learning to examine potential relationships between widely used atmospheric physical variables and short-term heavy rainfall, thereby improving model interpretability. CatBoost models were constructed based on multiple feature-input strategies using 71 physical variables across five categories derived from ERA5 reanalysis data, and their performance was compared with two benchmark algorithms, XGBoost and LightGBM. The SHAP method was subsequently applied to quantify the contributions of individual features and their interaction effects on model predictions. The results indicate that (1) the CatBoost model, utilizing all 71 physical variables, outperforms other feature combinations, with an AUC of 0.933, and F1 score of 0.930, and a Recall of 0.954, significantly higher than the XGBoost and LightGBM models; (2) Shapley value analysis identified 500 hPa vertical velocity, the A-index, and precipitable water as the most influential features on model performance; (3) The predictive mechanism for short-term heavy rainfall is fundamentally bifurcated: negative instances are classified through the discrete main effects of individual features, whereas positive event detection necessitates a sophisticated coordination of intrinsic main effects and synergistic interactions. Among the feature categories, the horizontal and vertical wind fields, stability and energy indices, and humidity-related variables exhibited the highest contribution ratios, with wind field features demonstrating the strongest interaction effects. The results confirm that integrating atmospheric physical variables with the CatBoost ensemble learning approach significantly improves short-term heavy rainfall identification. Furthermore, incorporating the SHAP interpretability framework provides a theoretical foundation for elucidating the mechanisms of feature influence and optimizing model performance. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article