Search Results (800)

The vertical structure and optical–microphysical properties of ice clouds determine their radiative effects. With an average altitude above 3000 m above mean sea level (AMSL) and unique thermal circulation, the Tibetan Plateau forms ice clouds with seasonally varying microphysical characteristics. In this study, satellite lidar observations from CALIPSO and ERA5 reanalysis from 2006 to 2023 reveal significant seasonal variation in ice clouds over the Tibetan Plateau and adjacent regions. In winter, maximums of the backscatter coefficient (β₅₃₂) and ice water content (IWC) were found south of the Qinling-Huaihe Line, as well as in the Sichuan Basin and the Yangtze Plain. In summer, these maximums move onto the Plateau, and the cloud height rises by about 1 km. The altitude of the β₅₃₂ maximum rises from about 4 km in winter to nearly 6 km in summer. Among four cloud categories defined by joint geometric and optical thickness thresholds, clouds with small geometric thickness and large optical thickness (thin and dense clouds) are the most radiatively important. While these clouds are seldom observed over the Tibetan Plateau in winter, they contribute to over thirty percent of local ice cloud occurrences during summer. Their preferred altitude rises from 3–4 km to 6–7 km, occurring under comparatively warmer environmental temperatures. Although limited in geometric depth, the thin and dense clouds exhibit the highest β₅₃₂ and IWC, the lowest multiple scattering coefficient (η₅₃₂), and the highest depolarization ratio (δ₅₃₂). They contribute about thirty percent of the total extinction and backscatter, despite representing only ten to twenty percent of all cases. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

Acoustic propagation in the ice cover of the Polar Ocean is of increasing interest from both scientific and engineering perspectives. The low-frequency elastic waves propagating in floating ice are primarily governed by waveguides stemming from the layered structure of the medium. For shallow water areas, experimental observation indicates that two Scholte-like waves are observed at low frequencies, i.e., the quasi-Scholte (QS) and Scholte–Stoneley (SS) waves, which are different from deep-sea cases. Due to the finite depths of ice, water, and sediment layers, both waves are dispersive. By modeling the waveguide of an ice-covered shallow-water (ICSW) system, the dispersion characteristics of both waves are derived, validated through numerical simulation, and analyzed with respect to layer structure for both soft and hard sediment. Results indicate a consistent conclusion; the QS wave exhibits a unique sensitivity to ice thickness, whereas the SS wave shows marginal sensitivity to ice thickness, and is controlled by the ratio of water depth to sediment depth, regardless of their absolute values. Based on these dispersion characteristics, a two-step inversion procedure is developed and applied to the synthetic signals from a numerical simulation. The conditional observability of the SS wave at the ice surface is also investigated and discussed. Full article

Sea ice concentration (SIC) and thickness (SIT) are critical variables for polar research. In this study, the potential of Tianmu-1 GNSS-R observations for retrieving Arctic SIC and SIT is explored using machine learning algorithms. XGBoost demonstrated superior accuracy and efficiency in the comparison of the three methods. For SIC retrieval, 14 parameters from Tianmu-1 were employed directly, whereas SIT retrieval incorporated additional auxiliary parameters, including SIC, sea ice salinity (S), and temperature (T). Among the different GNSS systems, GLO achieved the lowest RMSE for SIC, at 7.750%, whereas GAL performed comparatively poorly, with an RMSE of 10.475%. In SIT retrieval, the GPS and BDS yielded the smallest RMSE values of 0.276 m and 0.278 m, respectively, while GLO resulted in a slightly higher RMSE of 0.309 m. Daily retrievals of both the SIC and SIT were conducted from 18 October 2023 to 12 April 2024, with consistently stable evaluation metrics throughout the freezing season. In high-concentration regions, the retrieved SIC and SIT closely matched the reference data, whereas larger errors occurred in marginal ice zones and coastal areas. This study reveals the potential of Tianmu-1 to complement existing satellite missions in Arctic sea ice monitoring during the freezing period. Full article

A kinetic and thermodynamic multi-phase model has been developed to describe the adsorption of gases on ice surfaces and their subsequent diffusional loss into the bulk ice phase. This model comprises a gas phase, a solid surface, a sub-surface layer, and the ice bulk. The processes represented include gas adsorption on the surface, solvation into the sub-surface layer, and diffusion in the ice bulk. It is assumed that the gases dissolve according to Henry’s law, while the surface concentration follows the Langmuir adsorption equilibrium. The flux of molecules from the sub-surface layer into the ice bulk is treated according to Fick’s second law. Kinetic and thermodynamic quantities as applicable to the uptake of small carbonyl compounds on ice surfaces at temperatures around 200 K have been used to perform model calculations and corresponding sensitivity tests. The primary application in this study is acetic acid. The model simulations are applied by fitting the experimental data obtained from coated-wall flow-systems (CWFT) measurements, with the best curve-fit solutions providing reliable estimations of kinetic parameters. Over the temperature range from 190 to 220 K, the estimated desorption coefficient, k_des, varies from 0.02 to 1.35 s⁻¹, while adsorption rate coefficient, k_ads, ranges from 3.92 and 4.17 × 10⁻¹³ cm³ s⁻¹, and the estimated diffusion coefficient, D, changes by more than two orders of magnitude, increasing from 0.03 to 13.0 × 10⁻⁸ cm² s⁻¹. Sensitivity analyses confirm that this parameter estimation approach is robust and consistent with underlying physicochemical processes. It is shown that for shorter exposure times the loss of molecules from the gas phase is caused exclusively by adsorption onto the surface and solvation into the sub-surface layer. Diffusional loss into the bulk, on the other hand, is only important at longer exposure times. The model is a useful tool for elucidating surface and bulk process kinetic parameters, such as adsorption and desorption rate constants, solution and segregation rates, and diffusion coefficients, as well as the estimation of thermodynamic quantities, such as Langmuir and Henry constants and the ice film thickness. Full article

During late December 2021, an ice accretion disaster occurred in North Xinjiang, especially in the western part. It is found that the meteorological conditions suitable for the occurrence of ice accretion disasters are when the temperature is between −14 °C and −3 °C, the relative humidity is greater than 80%, the wind speed is between 4.5 m s⁻¹ and 7.5 m s⁻¹, and the pressure is between 919 hPa and 928 hPa. The ice accretion disaster is influenced by large-scale circulation, including the two-trough and one-ridge geopotential height structure in the middle troposphere and the spatially moving Ural Mountain blocking high pressure. Furthermore, using the artificial intelligence-based Pangu model and machine learning algorithms within the application of multiple linear regression and the leave-ten-out cross-validation, a skillful forecast correction model for ice accretion thickness in North Xinjiang is constructed. The prediction model has significant prediction skill for ice accretion thickness in North Xinjiang with 24 h, 48 h, and even 72 h in advance. The findings of the study can improve the timeliness of business system in the short-term and immediate forecast of ice accretion thickness, providing more reliable technical support for the ice prevention and disaster reduction of the power grids. Full article

Antarctic ice shelves regulate ice-sheet discharge and global sea-level rise, yet their rapid retreat underscores the need for new, low-cost monitoring tools. We analyze ambient seismic noise recorded by seismometers on the Pine Island Glacier ice shelf to characterize wind-induced signals and detect persistent structural resonances. Power spectral analysis shows that wind sensitivity is strongly damped compared with bedrock sites: noise increases only 5–7 dB from 0 to 25 m s⁻¹ winds, versus a 42 dB increase at an inland bedrock station, reflecting the contrasted coupling environments of floating and grounded substrates. The horizontal-to-vertical spectral ratio (HVSR) spectrograms reveal two temporally stable peaks at ~2.2 Hz and ~4.3 Hz that persist across stations and remain independent of environmental forcing. Forward modeling indicates that these peaks correspond to S-wave resonances within the ice shelf. The inferred ice-water interface depth (~440 m) agrees with the Bedmap2 thickness estimate (466 m). This work demonstrates that HVSR provides an effective passive, single-station method for measuring ice shelf thickness. Full article

Some cast metallic alloys for ultra-high-temperature structural applications can have better mechanical properties compared with Ni-based superalloys. Research on the directional solidification (DS) of such alloys is limited. The production of DS components of these alloys with “tailor-made” microstructures in different parts of the component has not been considered. This paper attempts to address these issues. A bar of the RCCA/RM(Nb)IC with nominal composition 3.5Al–4Crc6Ge–1Hf–5Mo–36Nb–22Si–1.5Sn–20Ti–1W (at.%) was directionally grown using OFZ processing, where the growth rate R increased from 1.2 to 6 and then to 15 cm/h. The paper studies how the macrosegregation of the elements affected the microstructure in different parts of the bar. It was shown that the synergy of macrosegregation and growth rate produced microstructures from the edge to the centre of the OFZ bar and along the length of the OFZ bar that differed in type and chemical composition as R increased. Contamination with oxygen was confined to the “root” of the part of the bar that was grown with R = 1.2 cm/h. The concentrations of elements in the bar were related (a) to each of the parameters VEC, Δχ, and δ for different sections, (i) across the thickness and (ii) along the length of the bar, or to each other for different sections of the bar, and demonstrated the synergy and entanglement of processing, parameters, and elements. In the centre of the bar, the phases were the Nb_ss and Nb₅Si₃ for all R values. In the bar, the silicide formed with Nb/(Ti + Hf) less or greater than one. There was synergy of solutes in the solid solution and the silicide for all R values, and synergy and entanglement of the two phases. Owing to the synergy and entanglement of processing, parameters, elements, and phases, properties would “emerge” in each part of the bar. The creep and oxidation properties of the bar were calculated as guided by the alloy design methodology NICE. It was suggested that, in principle, a component based on a metallic UHTM with “functionally graded” composition, microstructure and properties could be directionally grown. Full article

With the upgrading of geostationary meteorological satellites, their capabilities in Convective Initiation (CI) identification have been enhanced. To improve the applicability of the ARGI-based CI algorithm in central and eastern China, this study uses Fengyun-4A data, integrates radar and precipitation data to construct a True_CI dataset, and defines False_CI events (satellite-identified events without radar or precipitation signals) for comparative analysis. The results show that True_CI events tend to have longer durations, larger cloud cluster areas, and lower central cloud-top brightness temperature (BT) during development. They exhibit distinct features such as reduced differences between water vapor and infrared channels, increased cloud optical thickness, and ice-phase transformation 30 min before CI occurrence—features absent in most False_CI events. Based on these comparative findings, a new satellite-based CI definition is proposed with a set of reference thresholds, which should be adjusted for different latitudes and seasons. The evaluation of the Defined_CI events (defined using the CI definition) via True_CI events indicates that the CI definition on satellite cloud imagery proposed in this study is reliable, and suggests that further research on the pre-CI environmental conditions of weak convection is needed. Supported by hyperspectral data or numerical model products, such research will help clarify which cloud clusters are prone to developing into convective weather. Full article

A theoretical model of the interaction between a following current and a semi-infinite floating ice sheet under compressive stress near a vertical impermeable wall is developed, within the scope of linear water wave theory, to study the hydroelastic behavior. The conceptual framework defining the buoyant ice structure incorporates the tenets of elastic beam theory. The associated fluid dynamics are governed by strict adherence to the potential flow paradigm. To resolve the undetermined parameters appearing in the Fourier series decomposition of the potential functions, investigators systematically apply higher-order criteria detailing the coupling relationships between modes. The current results are compared with a specific case of results available in the literature, and the convergence analysis of the analytical solution is made for computational accuracy. Further, the free edge conditions are applied at the edge of the floating ice sheet, and the effects of current speed, compressive stress, the thickness of the ice sheet, flexural rigidity, water depth on the strain, displacements, reflection wave amplitude, and the horizontal force on the rigid vertical wall are analyzed in detail. It is found that the higher values of the following current heighten the strain, displacements, reflection amplitude, and force on the wall. The study’s outcomes are considered to benefit not just cold region design applications but also the engineering of resilient floating structures for oceanic and offshore environments, and to the design of marine structures. Full article

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements. Full article

Ice load is a crucial factor when designing structures for polar vessels. Due to the unpredictable nature of sea ice mechanics and the complexity of ship structures, obtaining ice load characteristics through full-scale measurements is considered more effective and reliable. However, conducting full-scale tests in the Arctic for China can be time-consuming and expensive. Using the natural ice fields in the Bohai Sea for full-scale tests can provide valuable insights into the study of ice load. To study ice load characteristics, full-scale measurements were carried out during icebreaker navigation trials in the ice zone of Bohai Sea. Distributed shear strain sensors were installed to measure the ice-induced structural strain on the starboard of the bow, and the local ice loads were determined based on the influence coefficient matrix method. Additionally, video cameras were utilized to record ice conditions, including ice type and thickness. By analyzing the data, the Rayleigh separation method was used to extract the process of ice load action. Statistical analysis was performed on the peak ice load values, with a particular emphasis on the various types of sea ice, ice thickness, and ship speed. The results show that the action period, peak value, mean value, and waveform of ice loads obtained in the full-scale measurement are consistent with the full-scale data of other icebreakers. The conclusion supports the effectiveness and feasibility of conducting ship ice load characteristic testing in the Bohai Sea. Full article

One of the key challenges with developing pulsed induction (PI) electromagnetic induction (EMI) sensors for use in the Arctic is the inaccessibility of the environment, which makes in situ testing prohibitively expensive. To mitigate this, sensor development can be streamlined through the creation of a robust simulation strategy with which to optimize features such as coil turns and geometry. Building on work that previously presented a method for simulating an Arctic PI sensor via a time-domain finite element model (FEM), this paper presents a method for approximating a time-domain simulation with multiple frequency-domain simulations. A comparison between the fast Fourier transform (FFT) of a time-domain simulation and a collection of frequency-domain simulations is presented. These are validated against empirical data with a PI sensor over seawater, with an air gap used as a proxy for sea ice. Using the method described, a range of coils is simulated with dimensions from $0.5\times 0.5$ m up to $1.0\times 2.0$ m, demonstrating the ability of this approach to enable comparison of sensor performance over a wider parameter space. For a parametric sweep over 10 sensor-to-seawater lift-off distances, the improvement from the time-domain simulation (of a 402 $\mathsf{\mu }$s window) to the frequency-domain simulation (comprising 100 discrete frequencies) represents a reduction in simulation time from 38,013 min to 141 min. Full article

Low-altitude palaeoglaciation in Atlantic mountain regions provides important insights into past climatic conditions and moisture dynamics during the Last Glacial Cycle. This study presents the first quantitative reconstruction of palaeoglaciers in Serra da Cabreira (northwest Portugal), a mid-altitude granite massif located along the Atlantic fringe of the Iberian Peninsula. Detailed geomorphological mapping (1:14,000) and field surveys identified 48 glacial and periglacial landforms, enabling reconstruction of two small valley glaciers in the Gaviões and Azevedas valleys using GlaRe numerical modelling. The spatial distribution of palaeoglacial landforms shows a pronounced west–east asymmetry: periglacial features prevail on wind-exposed west-facing slopes, whereas glacial erosion and depositional landforms characterise the more protected east-facing valleys. The reconstructed glaciers covered 0.24–0.98 km², with maximum ice thicknesses of 72–89 m. Equilibrium-line altitudes were estimated using AABR, AAR, and MELM methods, yielding consistent palaeo-ELA values of ~1020–1080 m. These results indicate temperature depressions of ~6–10 °C and enhanced winter precipitation associated with humid, Atlantic-dominated conditions. Comparison with regional ELA datasets situates Cabreira within a clear Atlantic–continentality gradient across northwest Iberia, aligning with other low-altitude maritime palaeoglaciers in the northwest Iberian mountains. The findings highlight the strong influence of the orographic barrier position, moisture availability, valley hypsometry, and structural controls in sustaining small, climatically sensitive glaciers at low elevations. Serra da Cabreira thus provides a key reference for understanding Last Glacial Cycle palaeoclimatic variability along the Western Iberian margin. Full article