Search Results (327)

Sub-cloud evaporation changes the isotopic composition of precipitation, which greatly reduces the reliability of precipitation isotopic data as precipitation simulation data. This study employed the precipitation isotope datasets of Haikou in northern Hainan Island from June 2020 to February 2024 to quantitatively study the influence of sub-cloud evaporation on precipitation isotopes in tropical islands. Due to the sub-cloud evaporation, the slope of the local meteoric water line (LMWL: δ²H = 8.33δ¹⁸O + 14.33) is lower than the average slope of the theoretical LMWL (8.48). The average value of the residual ratios of raindrop after evaporation (f) is 86%. The complex and unstable sources of water vapor result in no obvious seasonal variations in the atmospheric humidity, which in turn leads to no obvious seasonal variations in Δd and f. The humid and hot environmental conditions reduced the impact of sub-cloud evaporation on precipitation isotopes. The two main uncertainties in the simulation of below-cloud evaporation are the influence of recycled water vapor on precipitation isotopes and the Stewart model’s assumption that raindrops at the cloud base achieve isotopic equilibrium with the surrounding water vapor, as it is difficult to realize. The results of this study are of great significance for improving the accuracy of precipitation simulation in tropical monsoon islands. Full article

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

During the laser cladding process, the temperature, flow velocity, and element concentration of the molten pool will be affected by the plasma. Quantitative analysis of the mechanism by which the plasma affects heat and mass transfer during the laser cladding process is of great significance for improving the quality of the cladding layer. In this paper, a multi-field coupled numerical model of the laser cladding process of Fe60 using an ASTM 1045 disk laser was established. In the modeling, the interaction between the plasma cloud and the laser beam (the ionization process of metal vapor absorbing photon energy and the absorption and shielding effect of the plasma on laser energy), as well as the influence of surface tension, buoyancy, and shock waves generated by the expansion of the plasma cloud on the Marangoni flow of the liquid metal, was considered. A comparative analysis was performed on the transient evolution of the temperature field, flow field, and concentration field during the laser cladding process in the presence of the plasma cloud. The results show that the ionization process of metal vapor and the shielding effect of the plasma cloud cause a decrease in laser energy and the temperature of the cladding layer. The Marangoni flow is affected by the shock waves generated by the expansion of the plasma cloud, resulting in a decrease in the flow velocity of the melt. A slight decrease in the concentration of Fe, Cr, and Ni elements and a slight increase in the concentration of C element in the cladding layer are caused by melt evaporation. Full article

This article investigates the combined effects of different common defects on the structural integrity and operational and environmental safety in the operation of an existing Light Cycle Oil (LCO) storage tank. This study correlates all the tank defects (like corrosion and local plate thinning, deformations, and local stress concentrators) against the loads and their combinations that occur during the tank’s lifetime. All the information gathered by various inspection techniques is used together to create a digital twin of the equipment that will be further analyzed by Finite Element Analysis. A tank condition assessment is a complex activity, and it is based on the experience of the engineer performing it. Since there are multiple methods for performing a comprehensive analysis, starting from the basic visual inspection (which is the most important) and some measurements followed by analytical calculations, up to full wall thickness measurements, 3D scan of deformations and FEA analysis of the tank digital twin, it depends on the engineer performing the evaluation to chose the best method for each particular case from technical and economical point of views. The goal of this article is to demonstrate that analytical and FEA methods have the same result and also to establish a well-determined standard calculation model for future applications. Full article

Cavitating flows are of great interest in the fields of hydraulic machineries, which can significantly affect mechanical performance and safety. Despite various efforts being dedicated to figuring out the interaction between flow and cavitation fields, their correlation has not been clearly addressed. To this end, in this study, a convolutional neural network, U-Net, was adopted to build a model that can predict the vapor volume fraction from velocity fields. Large eddy simulations of cavitating flows around a Clark-Y hydrofoil were conducted, and the simulated snapshots with velocity and vapor volume fraction were adopted as a dataset for training the network. The predicted vapor volume fraction shows good agreement with the referred simulation results, with a L₁ deviation lower than 2 × 10⁻⁴, considering all the snapshots. The comparable L₁ deviation between the training and validation datasets suggests the existence of a strong correlation between velocity and cavitation fields. The cavitation–velocity interaction derived from using U-Net suggests that the location with zero velocity indicates the interior part of attached and cloud cavitations, and the local vortical velocity fields usually suggest the existence of cavitation shedding. Full article

The direct relationship between aerosols and clouds strongly influences the effects of clouds on the global climate. Aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei (IN), affecting cloud formation, microphysics, and precipitation, as well as increasing the reflection of solar radiation at the cloud tops. Processes such as gas-to-particle conversion and new particle formation (NPF) control aerosol properties that, together with meteorological conditions, regulate cloud droplet nucleation through Köhler theory and related effects. The indirect aerosol effects described by Twomey and Albrecht demonstrate how changes in aerosols impact droplet number, cloud lifetime, and precipitation efficiency. Cloud microphysical processes, including droplet growth, collision-coalescence, and solid-phase mechanisms such as riming, vapor diffusion, and aggregation, shape precipitation development in warm, cold, and mixed-phase clouds. Ice nucleation remains a significant uncertainty due to the diversity of aerosol types and nucleation modes. This work synthesizes these physical interactions to better understand how the chemical and physical properties of aerosols influence cloud and precipitation processes, supporting improvements in weather and climate prediction models despite numerical challenges arising from the complexity of aerosol–cloud interactions. Full article

Precipitable water vapor (PWV) is a critical component of the Earth’s atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based observation stations can only provide PWV measurements at discrete points, whereas spaceborne infrared remote sensing enables spatially continuous coverage, but its retrieval algorithm is restricted to clear-sky conditions. This study proposes an innovative approach that uses ensemble learning models to integrate infrared and microwave satellite data and other geographic features to achieve all-weather PWV retrieval. The proposed product shows strong consistency with IGRA radiosonde data, with correlation coefficients (R) of 0.96 for the ascending orbit and 0.95 for the descending orbit, and corresponding RMSE values of 5.65 and 5.68, respectively. Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. Unlike MODIS PWV products, which suffer from cloud-induced data gaps, the proposed method provides seamless spatial coverage, particularly in regions with frequent cloud cover, such as southern China. Temporal consistency was further validated across four east Asian climate zones, with correlation coefficients exceeding 0.88 and low error metrics. This algorithm establishes a novel all-weather approach for atmospheric water vapor retrieval that does not rely on ground-based PWV measurements for model training, thereby offering a new solution for estimating water vapor in regions lacking ground observation stations. Full article

The widespread adoption of liquefied natural gas (LNG) as a marine fuel has driven the development of LNG bunkering operations in global ports. Major international hubs, such as Shenzhen Port, have implemented ship-to-ship (STS) bunkering practices. However, this process entails unique safety risks, particularly hazards associated with vapor cloud dispersion caused by bunkering hose releases. This study employs the Phast software developed by DNV to systematically simulate LNG release scenarios during STS operations, integrating real-world meteorological data and storage conditions. The dynamic effects of transfer flow rates, release heights, and release directions on vapor cloud dispersion are quantitatively analyzed under daytime and nighttime conditions. The results demonstrate that transfer flow rate significantly regulates dispersion range, with recommendations to limit the rate below 1500 m³/h and prioritize daytime operations to mitigate risks. Release heights exceeding 10 m significantly amplify dispersion effects, particularly at night (nighttime dispersion area at a height of 20 m is 3.5 times larger than during the daytime). Optimizing release direction effectively suppresses dispersion, with vertically downward releases exhibiting minimal impact. Horizontal releases require avoidance of downwind alignment, and daytime operations are prioritized to reduce lateral dispersion risks. Full article

The article is focused on the quantitative assessment of the thermal impact of polar mesospheric clouds (PMCs) on the mesopause caused by the emission of absorbed solar and terrestrial infrared (IR) radiation by cloud particles. For this purpose, a parameterization of mesopause heating by PMC crystals has been developed, the main feature of which is to incorporate the thermal properties of ice and the interaction of cloud particles with the environment. Parametrization is based on PMCs zero-dimensional (0-D) model and uses temperature, pressure, and water vapor data in the 80–90 km altitude range retrieved from Solar Occultation for Ice Experiment (SOFIE) measurements. The calculations are made for 14 PMC seasons in both hemispheres with the summer solstice as the central date. The obtained results show that PMCs can make a significant contribution to the heat balance of the upper atmosphere, comparable to the heating caused, for example, by the dissipation of atmospheric gravity waves (GWs). The interhemispheric differences in heating are manifested mainly in the altitude structure: in the Southern Hemisphere (SH), the area of maximum heating values is 1–2 km higher than in the Northern Hemisphere (NH), while quantitatively they are of the same order. The most intensive heating is observed at the lower boundary of the minimum temperature layer (below 150 K) and gradually weakens with altitude. The NH heating median value is 5.86 K/day, while in the SH it is 5.24 K/day. The lowest values of heating are located above the maximum of cloud ice concentration in both hemispheres. The calculated heating rates are also examined in the context of the various factors of temperature variation in the observed atmospheric layers. It is shown in particular that the thermal impact of PMC is commensurate with the influence of dissipating gravity waves at heights of the mesosphere and lower thermosphere (MLT), which parameterizations are included in all modern numerical models of atmospheric circulation. Hence, the developed parameterization can be used in global atmospheric circulation models for further study of the peculiarities of the thermodynamic regime of the MLT. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), commonly used measures of the instability and inhibition within a vertical column of the atmosphere, serve as a proxy for estimating convection potential and updraft strength for an air parcel. In operational forecasting, CAPE and CIN are typically derived from radiosonde thermodynamic profiles, launched only twice daily, and supplemented by model-simulated equivalent values. This study uses remote sensing observations to derive CAPE and CIN from continuous data, expanding upon previous research by evaluating the performance of both passive and active profiling systems’ CAPE/CIN against in situ radiosonde CAPE/CIN. CAPE and CIN values are calculated from Atmospheric Emitted Radiance Interferometer (AERI), Microwave Radiometer (MWR), Raman LiDAR, and Differential Absorption LiDAR (DIAL) systems. Among passive sensors, results show significantly greater accuracy in CAPE and CIN from AERI than MWR. Incorporating water vapor profiles from active LiDAR systems further improves CAPE values when compared to radiosonde data, although the impact on CIN is less significant. Beyond the direct capability of calculating CAPE, this approach enables evaluation of the various relationships between the water vapor mixing ratio, CAPE, cloud development, and moisture transport. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

The SPICAV IR spectrometer aboard the Venus Express orbiter measured spectra of the 1.1 and 1.18 μm atmospheric transparency windows at the Venus night side in 2006–2014. The long-term measurements encompassed the major part of the Venus globe, including polar latitudes. For the first time, the H₂O volume mixing ratio in the deep Venus atmosphere at about 10–16 km has been retrieved for the entire SPICAV IR dataset using a radiative transfer model with multiple scattering. The retrieved H₂O volume mixing ratio is found to be sensitive to different approximations of the H₂O and CO₂ absorption lines’ far wings and assumed surface emissivity. The global average of the H₂O abundance retrieved for different parameters ranges from 23.6 ± 1.0 ppmv to 27.7 ± 1.2 ppmv. The obtained values are consistent with recent studies of water vapor below the cloud layer, showing the H₂O mixing ratio below 30 ppmv. Within the considered dataset, the zonal mean of the H₂O mixing ratio does not vary significantly from 60° S to 75° N, except for a 2 ppmv decrease noted at high latitudes. The H₂O local time distribution is also uniform. The 8-year observation period revealed no significant long-term trends or periodicities. Full article

Multilayer clouds, comprising vertically stacked cloud layers with distinct microphysical characteristics, constitute a critical yet complex atmospheric phenomenon influencing regional to global climate patterns. Advances in observational techniques, particularly the application of high-resolution humidity vertical profiling via radiosondes, have significantly enhanced multilayer cloud detection capabilities. Multilayer clouds are widely distributed around the world, showing significant regional differences. Many studies have been carried out on the formation mechanism of multilayer clouds, and observational evidence indicates a close relationship between multilayer cloud development and water vapor supply, updraft, atmospheric circulation, as well as wind shear; however, a unified and comprehensive theoretical framework has not yet been constructed to fully explain the underlying mechanism. In addition, the unique vertical structure of multilayer clouds exhibits different climate effects when compared with single-layer clouds, affecting global climate patterns by regulating precipitation processes and radiative energy budgets. This article reviews the research progress related to multilayer cloud observations and their climate effects and looks forward to the research that needs to be carried out in the future. Full article