Search Results (132)

Vento Norte (VNOR; Portuguese for North Wind) is a downslope windstorm that develops over modest terrain in the central region of Rio Grande do Sul (RS), southern Brazil. The regional topography is characterized by an abrupt terrain transition with elevation differences of approximately 400–500 m. This atmospheric flow typically occurs during the cold season and is characterized by strong wind gusts, rapid warming, and drying of the planetary boundary layer (PBL). In this study, the performance of different PBL parameterization schemes in the Weather Research and Forecasting (WRF) model is assessed for simulating a VNOR event that occurred between 19 and 20 August 2021 in Santa Maria (SMA), RS. Five high-resolution numerical simulations were conducted using the Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN2.5), Quasi-Normal Scale Elimination (QNSE), and Three-Dimensional Turbulent Kinetic Energy (3DTKE) PBL schemes. Model results were evaluated against observations from a flux tower providing turbulence measurements, twice-daily radiosoundings, and hourly surface meteorological observations. Statistical metrics indicate that the MYNN2.5 scheme provided the most accurate representation of the nighttime stable boundary layer preceding the VNOR, as well as its onset and subsequent evolution. Although this study analyzes a single VNOR event and the results may be case-dependent, the overall performance of the MYNN2.5 scheme suggests that it is a promising option for the operational forecasting of VNOR events. These findings provide new insights into the ability of different PBL schemes to reproduce the mean boundary-layer structure and turbulence characteristics associated with downslope windstorms over modest terrain, contributing to the understanding of these events. Full article

Mass transfer–induced Marangoni convection in volatile binary liquids is commonly associated with the amplification of interfacial concentration disturbances, yet the localized evolution preceding the first visible convective cell remains difficult to quantify experimentally. Here, ethanol–water desorption in a confined quasi–two–dimensional cell with a 2 mm liquid thickness was investigated using quantitative Schlieren imaging. The apparent transient concentration field and interfacial concentration profiles were reconstructed to resolve the earliest observable stage of Marangoni onset. The early behavior depended strongly on the initial ethanol mass fraction. Low–concentration cases mainly exhibited Rayleigh plume structures, high–concentration cases developed Marangoni cellular structures too rapidly for reliable early–stage tracking, whereas intermediate–concentration cases provided a resolvable window before Marangoni cell formation. For an initial ethanol mass fraction of 8 wt.%, a localized interfacial onset site appeared before the first visible Marangoni convective cell. This event initiated two counter–propagating spreading fronts, enriched the swept interfacial region, and was followed shortly by visible Marangoni cellular structures within the redistributed region. The apparent surface tension gradient field exhibited a transient evolution, with an initial increase, followed by a decrease during spreading, and a subsequent increase upon front interaction. These results provide experimental reference data for the pre–cellular interfacial redistribution sequence associated with perturbation–driven Marangoni onset in confined ethanol–water desorption systems. Full article

Linear and weakly nonlinear instabilities in thermosolutal rotating convection of a Casson fluid, incorporating the effects of helical forcing, are investigated. The governing equations, expressed in non-dimensional form, are solved by employing the normal mode method. We have shown the effect of various key parameters on convective regions and presented them graphically. The parameter regimes corresponding to the onset of stationary and oscillatory instabilities are systematically delineated. The effect of different key parameters on linear theory is obtained. The Taylor number, helical force parameter, and solute Rayleigh number have a stabilizing effect, whereas the Lewis number and Casson parameter have a destabilizing effect on the system. Within the framework of weakly nonlinear analysis, an amplitude equation is derived using the method of multiple scales. The amplitude equation is solved numerically to calculate the convective amplitude. Using the Nusselt and Sherwood numbers, the heat and mass transfer are analyzed. Full article

The conversion of surplus electrical energy into thermal energy represents an effective pathway for increasing the flexibility of renewable-energy systems. This study presents an experimental and numerical assessment of a compact vapor-assisted sensible heat storage unit designed to transform electrical input into stored thermal energy using a controlled evaporation–condensation process inside a vertical steel cylinder. An 800 W immersion heater was employed to generate vapor, while nine temperature sensors monitored the thermal response of the evaporator, enclosure air, and storage medium. Two operating configurations, insulated and non-insulated, were investigated to characterize charging and discharging dynamics. In parallel, CFD simulations performed in ANSYS Fluent were used to analyze coupled heat transfer and phase-change mechanisms. The results demonstrate efficient electrical-to-thermal energy conversion, with rapid temperature rise during charging driven by vapor-assisted convection following the onset of boiling. Experimental data and numerical predictions consistently reveal a transition from conduction-dominated heating to a phase-change-enhanced regime, which accelerates heat distribution and thermal homogenization within the storage unit. Comparative tests further indicate that reduced external losses improve heat retention during discharge. Overall, the combined experimental–numerical approach confirms the capability of the proposed compact system to store electrically generated heat in a stable and repeatable manner, highlighting its potential for daily photovoltaic energy buffering and small-scale renewable-energy applications. Full article

Hail is one of the typical manifestations of severe convective weather, characterized by its sudden onset and strong localization. In this study, a compact all-fiber coherent Doppler lidar (CDL) working at the 1.5 μm wavelength is employed to detect a hail event. Combined with ERA5 reanalysis data, Parsivel², and cloud-type products from the Fengyun satellite, the synoptic background of the hail event was analyzed. Owing to its high-precision spectrum measurement capability, the CDL can effectively separate the multi-component power spectra of precipitation particles. By comparing particle velocity, spectrum width and skewness as characteristic parameters from signal separation across light rain, hail and heavy rain, the distinctive power spectrum characteristics of hail were identified. This study verifies that CDL can provide high-spatiotemporal-resolution data support for the short-term forecasting of hail events. Full article

Injection-molding purges are heterogeneous, bulky residues whose uncertain composition and irregular geometry hinder direct reinsertion, making cold shredding costly and maintenance-intensive. This work develops a low-infrastructure solar-assisted pre-processing route using a PMMA Fresnel lens to induce controlled sub-onset softening and enable clean shear cutting without destructive thermal histories. The sub-onset softening is here defined into a viscoelastically active range (at or above T_g for the amorphous phase) while remaining below the melting onset (T_m, onset) and below the onset of thermal degradation (T_d, onset). The station was engineered via QFD and risk-oriented design tools, while a weighted Pugh matrix selected shear cutting over saw-based alternatives. A screening factorial DOE showed that lens height, angle, and their interaction significantly govern focal-spot diameter and receiver temperature, yielding linear relations for conservative set-point selection. Receiver benchmarking further indicated that copper reaches substantially higher temperatures than graphite under identical exposure conditions, supporting copper as the simplest, rapid-heating receiver. Under DOE-calibrated operation, tear-free shear cutting was achieved across representative purge families (PP–ABS, PC–ABS–PP, PA66, PA66-filler, and POM) without forced convection. From a recycling and waste-management perspective, the approach converts bulky purge scrap into mill-compatible feedstock with reduced mechanical resistance, lowering tool wear and fines generation, accelerating downsizing, and limiting stockpiling that elevates combustible-inventory fire risk. Overall, the proposed DOE-calibrated, operator-friendly framework improves recycling feasibility by enabling safer handling, more stable preprocessing throughput, and reduced reliance on disposal or long-term storage for heterogeneous industrial purges. Full article

This article examines the thermohaline stability of a power-law fluid saturating a porous layer in the presence of a magnetic field. The system stability is analyzed using both linear and weakly nonlinear instability theories. Within the linear framework, the Galerkin method is employed to derive analytical expressions for the Rayleigh number corresponding to steady and oscillatory modes of instability. Takens–Bogdanov and Hopf bifurcation points are identified, highlighting the transition mechanisms between different instability regimes. An increase in the Hartmann number delays the onset of convection. The critical Rayleigh number is a monotonic increasing function of the solute Rayleigh number, whereas it is a non-monotonic function of the Peclet number. To investigate heat and mass transport characteristics, an amplitude equation is derived in the weakly nonlinear regime. The results reveal that increasing the Hartmann, Lewis, and Peclet numbers enhances both heat and mass transport, whereas an opposite trend is observed with increasing the solute Rayleigh number. Full article

This study assesses the suitability of ERA5 reanalysis data for representing heat sources over the southern Tibetan Plateau (STP). The results show strong agreement between ERA5 sensible heat flux (SH), atmospheric heat source (AH), and independent observations (R² > 0.998, RMSE < 0.82 W·m⁻²). Heat fluxes over the STP exhibit pronounced spatiotemporal heterogeneity. In summer, surface latent heat flux (SLHF) displays a distinct east–west gradient, with values of 140–180 W·m⁻² in the east and lower values in the west, while surface sensible heat flux (SSHF) remains uniformly low (40–80 W·m⁻²). In spring, SSHF shows a reversed west–east pattern, with a high-value center of 100–140 W·m⁻². The SH peak precedes the latent heat peak by approximately 1 h and coincides with an increase of approximately 25 W·m⁻² in mid-to-upper atmospheric heating, confirming the dominant role of SH in vertical thermal development. The atmospheric heat source (Q_atm) leads the Indian monsoon onset by 7–10 days. When Q_atm exceeds 110 W·m⁻², the probability of monsoon onset increases from 35% to 78%. A coupled altitude–moisture column forms between the Bay of Bengal “wet tongue” and enhanced STP heating, with a phase lag of approximately 12 h. On intra-seasonal timescales (10–20 days), STP heat source variability and Philippine Sea convection exhibit a clear ~12-day lagged teleconnection, characterized by a “heat source enhancement–convection intensification” dipole pattern. These results provide quantitative dynamic–thermodynamic indicators for sub-seasonal monsoon prediction. Full article

This study, conducted in the framework of the LIAISE field campaign in NE Spain (May–September 2021), investigates how near-surface relative humidity influences early-stage rainfall characteristics when precipitation is most affected by temperature and relative humidity before rainfall onset. Two instrumented sites were examined, using disdrometers, Micro Rain Radar (MRR), C-band weather radar data, and automatic weather stations. Rainfall events were first classified as stratiform or convective using weather radar data based on a texture analysis of the reflectivity field. Then, only stratiform events were selected and further classified into dry and moist categories according to the upper and lower terciles of near-surface (2 m) relative humidity at the rainfall onset (dry < 54%; moist > 72%). Results show that during dry events, the time delay between the detection of precipitation at ~750 m above ground level (AGL) (by MRR or C-band radar) and its arrival at the surface (measured by the disdrometer) is consistently longer than during moist events, indicating possible evaporation of raindrops during their descent. Surface drop size distributions also differ: dry cases have generally fewer small drops (with diameters < 0.8 mm) but relatively more large drops, leading to higher radar reflectivity values despite similar surface rainfall amounts. However, reflectivity observed aloft by C-band radar and MRR does not present the dependence on relative humidity found at ground level. Findings reported here increase our understanding of the impact of low-level conditions on precipitation characteristics and microphysical associated processes and may contribute to improve correction schemes in operational weather radar quantitative precipitation estimates. Full article

A comprehensive numerical analysis has been conducted to investigate unsteady natural convection (UNC), bifurcation behavior, and heat transfer (HT) in a rectangular enclosure containing thermally stratified air. The enclosure comprises a uniformly heated bottom wall, thermally stratified vertical sidewalls, and a cooled top wall. To assess thermal performance, square and rectangular cavities with identical boundary conditions and working fluid are considered. The finite volume method (FVM) is used to solve the governing equations over a wide range of Rayleigh numbers (Ra = 10¹ to 10⁹) for air with a Prandtl number (Pr) of 0.71. Flow dynamics and thermal performance are analyzed using temperature time series (TTS), limit point–limit cycle behavior, average Nusselt number (Nu_avg), average entropy generation (S_avg), average Bejan number (Be_avg), and the ecological coefficient of performance (ECOP). In the rectangular cavity, the transition from steady to chaotic flow exhibits three bifurcations: a pitchfork bifurcation at Ra = 3 × 10⁴–4 × 10⁴, a Hopf bifurcation at Ra = 3 × 10⁶–4 × 10⁶, and the onset of chaotic flow at Ra = 9 × 10⁷–2 × 10⁸. The comparative analysis indicates that Nu_avg remains nearly identical for both cavities within Ra = 10⁵ to 10⁷. However, at Ra = 10⁸, the HT rate in the rectangular cavity is 29.84% higher than that of the square cavity, while S_avg and Be_avg differ by 39.32% and 37.50%, respectively. Despite higher HT and S_avg in the rectangular enclosure, the square cavity demonstrates superior overall thermal performance by 13.52% at Ra = 10⁸. These results offer significant insights for optimizing cavity geometries in thermal system design based on energy efficiency and entropy considerations. Full article

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order $\beta$-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications. Full article

Warm-cloud hygroscopic seeding is widely used in precipitation enhancement, but the conditions under which seeding amplifies or suppresses rainfall remain unclear. Here, we use a two-dimensional slab-symmetric spectral bin microphysics model from Tel Aviv University to simulate a warm convective cloud that occurred over Hainan, China, on 11 May 2024, and design three sets of sensitivity experiments in which hygroscopic particles of different characteristic diameters are introduced under a fixed-mass injection constraint. We find that seeding with submicrometer particles (0.1–0.9 µm) systematically suppresses precipitation, with the strongest reduction for 0.1 µm particles. When super-micrometer particles (1–9 µm) are used, the precipitation response transitions from suppression to enhancement as particle size increases, and this transition occurs at about 2 µm. Seeding with ultra-giant particles (>10 µm) generally enhances rainfall and also advances its onset, with the enhancement strengthening up to ~60 µm before weakening for even larger particles. We further show that the transitional particle size at which the seeding effect changes sign decreases with increasing background aerosol loading, from maritime to polluted urban conditions. These results identify an environment-dependent critical particle size that governs the sign and efficiency of hygroscopic seeding in warm convective clouds. Full article

Homeostasis, which supports and maintains brain function, results from the continuous regulation of thermodynamics within tissue: the balance of heat production, redox oscillations, and vascular convection regulates coherent energy flow within the organ. Neuroinflammation disturbs this balance, creating measurable entropy gradients that precede structural damage to its tissue components. This paper proposes that a thermodynamic unity can be devised that incorporates nanoscale physics, energetic neurophysiology, and systems neuroscience, and can be used to understand and treat neuroinflammatory processes. Using multifactorial modalities such as quantum thermometry, nanoscale calorimetry, and redox oscillometry we define how local entropy production (s_t), relaxation time (τ^R), and coherence lengths (λc) allow quantification of the progressive loss of energetic symmetry within neural tissues. It is these variables that provide the basis for the etiology of thermodynamic biomarkers which on a molecular-redox-to-network scale characterize the transitions governing the onset of the neuroinflammatory process as well as the recovery potential of the organism. The entropic probing of systems (PEP) further allows the translation of these parameters into dynamic patient-specific trajectories that model the behavior of individuals by predicting recurrent bouts of instability through the application of machine learning algorithms to the vectors of entropy flux. The parallel development of the nanothermodynamic intervention, which includes thermoplasmonic heat rebalancing, catalytic redox nanoreacting systems, and adaptive field-oscillation synchronicity, shows by example how the corrections that can be applied to the entropy balance of the cell and system as a whole offer a feasible form of restoration of energy coherence. Such closed loop therapy would not function by the suppression of inflammatory signaling, but rather by the re-establishment of reversible energy relations between mitochondrial, glial, and vascular territories. The combination of these factors allows for correction of neuroinflammation, which can now be viewed from a fresh perspective as a dynamic phase disorder that is diagnosable, predictable, and curable through the physics of coherence rather than the molecular suppression of inflammatory signaling. The significance of this set of ideas is considerable as it introduces a feasible and verifiable structure to what must ultimately become the basis of a new branch of science: predictive energetic medicine. It is anticipated that entropy, as a measurable and modifiable variable in therapeutic “inscription”, will be found to be one of the most significant parameters determining the neurorestoration potential in future medical science. Full article

We propose a geometry topological framework to analyze storm dynamics by coupling persistent homology with Anti-de Sitter (AdS)-inspired metrics. On radar images of a bow echo event, we compare Euclidean distance with three compressive AdS metrics ($\alpha$ = 0.01, 0.1, 0.3) via time-resolved $H_1$ persistence diagrams for the arc and its internal cells. The moderate curvature setting ($\alpha =0.1$) offers the best trade-off: it suppresses spurious cycles, preserves salient features, and stabilizes lifetime distributions. Consistently, the arc exhibits longer, more dispersed cycles (large-scale organizer), while cells show shorter, localized patterns (confined convection). Cross-correlations of $H_1$ lifetimes reveal a temporal asymmetry: arc activation precedes cell activation. A differential indicator $\Delta \left(t\right)$ based on Wasserstein distances quantifies this divergence and aligns with the visual onset in radar, suggesting early warning potential. Results are demonstrated on a rapid Corsica bow echo; broader validation remains future work. Full article