Search Results (455)

Low-level jets (LLJs) are common strong wind structures in the atmospheric boundary layer. They have important impacts on aviation safety, wind energy utilization and pollutant dispersion. However, the formation mechanisms of LLJs are complex. Traditional parameterization schemes and numerical models still show limitations in forecasting LLJ occurrence and resolving their structures. In this study, wind lidar, near-surface turbulence and gradient meteorological observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University are combined to construct a multi-source low-level dataset. Four processing modules are designed, including multi-source data fusion, turbulence preprocessing, turbulence intermittency metrics and LLJ identification, to overcome the constraints of single-platform observations. Six commonly used machine learning algorithms (LightGBM, XGBoost, CatBoost, K-nearest neighbors, Balanced Random Forest, and ExtraTrees) are compared. A two-stage classification–regression framework is then adopted. LightGBM is used for LLJ occurrence, and CatBoost is used for LLJ height and intensity, to build an LLJ-2Stage prediction system. The system performs automatic LLJ identification and predicts jet intensity and core height. For LLJ occurrence, the harmonic-mean F1-score of precision and recall reaches 0.820. The coefficient of determination R² is 0.643 for height prediction and 0.794 for intensity prediction. Both the classification and regression parts show good accuracy and stability. The SHAP method is further applied to assess model interpretability and to identify key predictors that control LLJ occurrence, height and intensity. Results indicate that thermal variables, such as net radiation (Rn) and sensible heat flux (H), dominate LLJ occurrence and structural changes. The strength of turbulence intermittency provides valuable supplementary information for locating the LLJ core height. Two representative nocturnal LLJ cases further show a consistent near-surface evolution during the LLJ period, with enhanced TKE and reduced H, followed by a gradual recovery after decay, while Rn remains persistently low, consistent with the SHAP-indicated effects. The proposed framework predicts LLJ occurrence and structural evolution and is of significance for improving understanding of boundary layer processes, air-pollution control, wind energy utilization and low-level aviation safety. Full article

During tropical cyclones (TCs), intense exchanges of momentum, heat, and moisture occur across the air–sea interface. The present study was conducted to investigate the role of surface roughness parameterizations under such conditions. To this end, a series of sensitivity experiments was conducted with a focus on Tropical Cyclone Biparjoy, which originated from the Indian Ocean in 2023. The experiments evaluate the impact of different schemes for momentum, thermal, and moisture roughness length on TC track, intensity, significant wave height, and air–sea heat fluxes. The results indicate that the momentum roughness length scheme is critical for accurately forecasting TC track and intensity and for simulating significant wave height; furthermore, Drennan’s parameterization yielded slightly better results in this case, with the smallest track error (72.0 km MAE) among the momentum schemes. Under the premise that Drennan’s parameterization scheme has high accuracy in momentum roughness, sensitivity experiments on thermal and moisture roughness parameterization were conducted. The Drennan–Fairall2014 combination achieved the lowest errors in TC central pressure (4.25 hPa RMSE) and the maximum sustained wind speed (5.31 m/s RMSE). Thermal and moisture roughness mainly affects the efficiency of turbulent heat transfer between the ocean and the atmosphere and thus has a limited impact on the cooling of sea surface temperature, with SST RMSE differences among schemes within 0.3 °C. This effect is mainly confined to the uppermost ocean layer and does not significantly change the thermal structure of the upper layers. Full article

Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy rainfall event that occurred on 26 December 2023 in Mahalapye District, Botswana. This event is one among many that have negatively impacted the lives and infrastructures in Botswana. The WRF model was configured using the tropical-suite physics schemes, i.e., (Rapid Radiative Transfer Model, Yonsei University planetary boundary layer scheme, Unified Noah land surface model, New Tiedtke, Weather Research and Forecasting Single-Moment six-class) on a two-way nested domain (9 km and 3 km grid spacing) and was initialized with the GFS dataset. Gauged station data was used for verification alongside synoptic charts generated using ECMWF ERA5 dataset. The results show that the WRF model simulation using the tropical-suite physics schemes is able to reproduce the spatial and temporal patterns of the observed rainfall but with some notable biases. Performance metrics, including RMSE, correlation coefficient, and KGE, showed moderate to good agreement, highlighting the model’s sensitivity to physical parameterization and resolution. The results of this study conclude that the WRF model demonstrates promising potential in forecasting extreme rainfall events in Botswana, but more sensitivity tests to different parameterization schemes are needed in order to integrate the model into the early warning systems to enhance disaster preparedness and response. Full article

The built-in V-shaped permanent magnet motor can effectively utilize reluctance torque to improve torque density, but there is also a problem of large torque ripple causing high vibration noise. This article proposes a rotor structure with four magnetic isolation holes to reduce torque ripple in V-shaped built-in permanent magnet motors. Firstly, a finite element analysis model of the built-in V-shaped permanent magnet motor is established. The influence of slot width, rotor rib width, and magnetic bridge parameters on the torque of the permanent magnet motor was studied through parameterized scanning, and an optimization scheme was selected. Then, the position and size of the magnetic hole were optimized through an adaptive single-objective algorithm. Compared with the ordinary built-in V-shaped structure, the torque ripple of the built-in V-shaped permanent magnet motor with four magnetic isolation holes is reduced from 17.7% to 6.7%. The proposed internal V-shaped rotor structure with magnetic isolation holes and the optimization method can effectively reduce torque ripple, thus effectively solving the problem of vibration noise caused by torque ripple. Full article

The construction and operation of reservoirs have made hydrological processes complex, posing challenges to flood modeling. While many hydrological models have incorporated reservoir operation schemes to improve discharge estimation, the influence of reservoir representation on model calibration has not been sufficiently evaluated—an issue that fundamentally affects the spatial reliability of distributed modeling. Additionally, the limited availability of reservoir regulation data impedes dam-inclusive flood simulation. To overcome these limitations, this study proposes a synergistic modeling framework for data-scarce dammed basins. It integrates a satellite-based reservoir operation scheme into a distributed hydrological model and incorporates reservoir processes into the model calibration procedure. The framework was tested using the coupled version of the DRIVE flood model (DRIVE-Dam) in the Nandu River Basin, southern China. Two calibration configurations, with and without dam operation (CWD vs. CWOD), were compared. Results show that reservoir dynamics were effectively reconstructed by combining satellite altimetry with FABDEM topography, successfully supporting the development of the reservoir scheme. Multi-site comparisons indicate that, while CWD slightly improved streamflow estimation (NSE and KGE > 0.75, similar to CWOD) on the calibrated outlet gauge, it enhanced basin-internal process representation, as evidenced by the superior peak discharge and flood event capture with reduced bias, boosting flood detection probability from 0.54 to 0.60 and reducing false alarms from 0.28 to 0.15. The improvements stem from refined parameterization enabled by a physically complete model structure. In contrast, CWOD leads to subdued flood impulses and prolonged recession due to spurious parameters that distort baseflow and runoff response. The proposed methodology provides a practical reference for flood forecasting in dam-regulated basins, demonstrating that reservoir representation enhances model parameterization and underscoring the strong potential of satellite observations for hydrological modeling in data-limited regions. Full article

The planetary boundary layer (PBL) exerts strong control on heat, moisture, and momentum exchange, yet its representation over the steep mountains and deep valleys of Liangshan remains poorly understood. This study evaluates six Weather Research and Forecasting (WRF) PBL schemes (ACM2, BL, MYJ, MYNN2.5, QNSE, and YSU) using multi-source observations from radiosondes, surface stations, and wind profiling radar during clear-sky dry-season cases in spring and winter. The schemes exhibit substantial differences in governing turbulent mixing and stratification. For the specific cases studied, QNSE best reproduces 2 m temperature in both seasons by realistically capturing nocturnal stability and large diurnal ranges, while non-local schemes overestimate nighttime temperatures due to excessive mixing. MYNN2.5 performs robustly for boundary layer growth in spring, and BL aligns most closely with radar-derived PBL height (PBLH). Vertical profile comparisons show that QNSE and MYJ better represent the lower–middle level thermodynamic structure, whereas all schemes underestimate extreme near-surface winds, reflecting unresolved terrain-induced variability. PBLH simulations reproduce diurnal cycles but differ in amplitude, with QNSE occasionally producing unrealistic spikes. Overall, no scheme performs optimally for all variables. However, QNSE and MYNN2.5 show the most balanced performance across seasons. These findings provide guidance for selecting PBL schemes for high-resolution modeling and fire–weather applications over complex terrain. Full article

Storage-oriented reservoir schemes are effective for large-scale hydrological modeling, yet two important limitations remain. First, although some reservoirs seasonally adjust flood storage capacity (FSC), no global study has examined whether constant or seasonally varying FSC performs better. Second, these schemes rely on empirical operational-zone parameterization, but its impact on simulation accuracy has never been systematically assessed. This study presents an open-source Python module integrating three leading storage-oriented schemes (S25, Z17, H22) with both constant and seasonally varying FSC options. Evaluated using daily observations from 289 global reservoirs via Nash-Sutcliffe Efficiency (NSE), constant FSC significantly outperforms seasonal variation, increasing median outflow NSE by 0.18–0.47 and reducing storage error magnitude by 38–61%, and is selected as optimal for 84% of reservoirs. Sensitivity analysis across eight alternative zoning schemes shows that, under constant FSC, outflow remains stable, whereas seasonal FSC sharply increases sensitivity. Storage simulation is more sensitive overall, yet constant FSC consistently yields the smallest errors. This work provides the first global comparison of FSC strategies and the first systematic assessment of operational zone parameter uncertainty. It strongly recommends constant FSC with H22 or S25 as the default for large-scale modeling. The released module offers a flexible, reproducible platform for the community. Future extensions may incorporate demand-driven rules and hybrid calibration to further improve performance in data-rich regions. Full article

Sea Surface Skin Temperature (SSTskin) derived from satellites and its diurnal variation are crucial for climate research, yet conventional ocean models, which primarily solve for the foundation or bulk SST, are not designed to simulate the very thin skin layer temperature (SSTskin). Consequently, specialized parameterizations or coupled model components are often required to obtain SSTskin. This study aimed to capture SSTskin diurnal warming events and evaluate the performance of the improved one-dimensional mixed-layer model (PWP: Price-Weller-Pinkel) in simulating SSTskin. Using high-frequency Himawari-8 satellite observations, a typical diurnal warming event was detected in the coastal waters off northwestern Australia, with the maximum SSTskin diurnal variation reaching 3 °C. The reliability of Himawari-8 data was validated using iQuam in situ observations, showing a mean bias of −0.28 °C. The improved PWP model (incorporating an SSTskin parameterization scheme), forced by ERA5 datasets, was used to simulate SSTskin and its diurnal variation at 90 (0.25° × 0.25°) grid points. Results indicated that the PWP model reproduced the diurnal variation cycle consistently with observations, accurately matched regions with significant warming, and achieved a mean bias of −0.37 °C. However, in low-wind-speed areas (<1 m/s), abnormal SSTskin overestimation (>3 °C) occurred due to rapid thinning of the mixed layer and the absence of horizontal diffusion in this one-dimensional model. The improved PWP model, with its relatively stable SSTskin parameterization scheme, provides a computationally efficient tool for studying vertical processes in the upper ocean. Future work should evaluate vertical mixing schemes under low wind speed conditions to enhance the capability of numerical models to simulate SSTskin. Full article

Terahertz Synthetic Aperture Radar (THz-SAR) is highly sensitive to platform vibrations and trajectory deviations, which introduce severe phase errors and limited resolution. Typically, platform vibrations and trajectory deviations are investigated individually, and vibrations are modeled as a stationary sine term. In this work, a hybrid motion compensation (MOCO) scheme is proposed to address both platform vibrations and trajectory deviations simultaneously, achieving improved imaging quality. The scheme initiates with a parameter self-adaptive quadratic Kalman filter designed to resolve severe phase wrapping. Then, platform vibration is modeled as a non-stationary multi-sine term, whose components are accurately extracted using an improved signal decomposition algorithm enhanced by a dynamic noise adjustment mechanism. Subsequently, the trajectory deviation is parameterized following subaperture division, estimated using a hybrid optimizer that combines particle swarm optimization and gradient descent. Additionally, a composite modulated waveform application ensures low sidelobes and a low probability of intercept (LPI). Extensive simulations on point targets and complex scenes under various signal-to-noise-ratio (SNR) conditions are applied for SAR image reconstruction, demonstrating robust suppression of motion errors. Under identical simulated error conditions, the proposed method achieves an azimuth resolution of 4.28 cm, which demonstrates superior performance compared to the reported MOCO techniques. Full article

Stratocumulus clouds are cloud systems composed of stratiform clouds with embedded convective clouds, possessing strong catalytic potential and serving as key target cloud systems for weather modification operations. In this study, the parameterization of ice nucleation for silver iodide (AgI) particles was applied to the Thompson microphysics scheme in the WRF model. Numerical experiments were designed for a stratocumulus cloud that occurred over the Hulunbuir region, northeastern China, on 31 May 2021, to investigate how the structure and evolution of cloud macro- and microphysical properties and precipitation formation respond to glaciogenic seeding. The simulation results indicate that AgI nucleation increased ice concentrations at 4–5 km altitude, enhancing ice crystal formation through condensation–freezing and deposition nucleation and the growth of ice particles through auto-conversion and riming, leading to increased precipitation. The results also show that owing to the non-uniform distribution of supercooled water within this stratocumulus cloud system, the consumption of AgI and the enhanced ice nucleation release latent heat more strongly in regions with higher supercooled water content. This leads to more pronounced isolated updrafts, altering the structure of shear lines and subsequently influencing regional precipitation distribution after silver iodide seeding concludes. These findings reveal that seeding influences both the microphysical and dynamic structures within clouds and highlight the non-uniform seeding effects within cloud systems. This study contributes to a deeper understanding of the effects of artificial seeding on stratocumulus clouds in high-latitude regions and holds significant reference value for artificial weather modification efforts in mixed-phase stratiform clouds. Full article

This study presents a data-driven, machine learning-based approach to the dimensional synthesis of planar four-link slider–crank function generators. The proposed methodology integrates kinematic analysis to generate physically feasible datasets that capture the relationship between linkage dimensions and the precision points of slider–crank linkages. To synthesize valid, defect-free linkages for an arbitrary number of user-defined precision points, a customized Long Short-Term Memory (LSTM)-based model is developed and trained on the generated dataset. A parameterization scheme for the linkage dimensions is introduced to ensure prediction-level validity, enabling stable convergence and physically realizable predictions. Numerical results demonstrate high accuracy and robustness under both absolute and relative precision-point specifications, despite the model being trained solely on absolute precision points without any initial configuration estimation. In addition to deriving feasible linkage dimensions, the proposed method offers a practical and scalable framework for engineering design applications. Full article

The Ordovician No. 6 fault zone reservoir in the Shunbei Oilfield exhibits ultra-deep-burial, high-pressure, and high-temperature conditions. Its pronounced tectonic control and significant heterogeneity render traditional in situ stress prediction methods—based on linear elasticity and anisotropy assumptions—inadequate for accurately characterizing the evolution and uncertainty of carbonate reservoir stiffness. Therefore, quantitatively predicting the development patterns and distribution characteristics of the Shunbei No. 6 structural fault zone is crucial for the exploration and development of Ordovician carbonate reservoirs in the Shunbei region. This study integrates wave impedance inversion with high-confining-pressure PFC particle flow biaxial test results to establish a constitutive calibration system consistent with seismic and experimental data. It introduces a nonlinear weakening function incorporating higher-order derivative constraints to fuse structural fracture and effective stress weakening effects, enabling dynamic correction of elastic parameters. This approach establishes a novel in situ stress prediction model. Simulation results indicate a predicted range for maximum horizontal principal stress between 201 and 261 MPa, with minimum horizontal principal stress ranging from 124 to 173 MPa. Predicted stress values for three key wells exhibit measurement errors within 6.92% compared to actual logging data, displaying a zoned spatial distribution consistent with regional tectonic stress evolution patterns. Simultaneously, sensitivity analysis reveals that the Young’s modulus fitting accuracy improved from 0.89 to 0.95, with a 43% reduction in mean square error, with the proportion of outliers reduced to below 1%. This significantly enhances response continuity and numerical stability in high-gradient disturbance zones and stiffness drop regions. The new model explicitly incorporates the nonlinear coupling between fracture geometry and pore pressure disturbance into the parameter field, eliminating systematic bias along fracture zones. Higher-order derivative constraints suppress numerical oscillations in high-gradient areas, stabilizing variance and preventing anomaly propagation. Residual distributions exhibit enhanced symmetry and reduced spatial autocorrelation, effectively suppressing numerical oscillations and divergence in complex fracture zones while significantly improving stress prediction accuracy for the study area. Overall, this research provides novel methodologies for predicting in situ stresses in ultra-deep carbonate reservoirs, offering engineering guidance and parameterization references for scheme deployment in complex fractured karst systems. Full article

Soil moisture simulations in semi-arid inland river basins remain highly uncertain due to complex land–atmosphere interactions and multiple parameterization schemes in land surface models. This study evaluated the ability of the Noah-Multiparameterization Land Surface Model (Noah-MP) to simulate soil moisture at meteorological sites representing the upstream, midstream and downstream regions of a semi-arid inland river basin with contrasting climates. A large physics-ensemble experiment (17,280 simulations per site) combining different parameterization schemes for 10 main physical processes was conducted. Natural selection, Tukey’s test and uncertainty contribution analysis were applied to identify sensitive processes and quantify their contributions to simulation uncertainty. Results indicate that Noah-MP captures soil moisture variability across the basin but with notable biases. Three physical processes—frozen soil permeability, supercooled liquid water in frozen soil and ground resistance to sublimation—were sensitive at all sites, whereas radiation transfer and surface albedo were consistently insensitive. At the upstream and midstream sites, supercooled liquid water contributed about half of the ensemble uncertainty, and at the downstream site ground resistance to sublimation contributed roughly 51%. These findings reveal which physical processes most strongly affect Noah-MP soil moisture simulations in semi-arid basins and provide guidance for improving parameterization schemes to reduce uncertainty. Full article

In this work, a decentralized robust direct model reference adaptive controller (MRAC) via e-modification is suggested for the pose control of a quadrotor to prevent parameter drift. The governing equations of motion referred to the rotational system of the quadrotor are parameterized involving matched uncertainties through the control input channel in a decentralized way from the angles of motion, where bounded perturbations are also considered. An error-dependent damping term in the update law is added to enforce robustness. Uniform ultimate boundedness of the tracking error signal is ensured. The translational dynamics are governed through a linear proportional–integral–derivative (PID) control. The performance of the decentralized robust MRAC scheme proposed here is assessed via simulation and compared with that from decentralized robust MRACs using smooth dead-zone modification and $\sigma$-modification. Full article

With the growing demand for wind turbines in deep offshore regions, frequent typhoon disasters at sea have impeded the continued development of the wind power industry. To address the problem of typhoons destroying offshore wind power facilities, this paper investigates the aeroelastic characteristics of long flexible blades on ultra-large offshore wind turbines under typhoon loads. The WRF numerical model is employed for high-precision simulations of Typhoon Mangkhut (No. 1822). By optimizing parameterization schemes and incorporating 3DVAR data assimilation techniques, typhoon wind speed profiles in the target sea area are obtained. Based on IEA 15 MW offshore wind turbine data, 3D unsteady CFD models and full-scale finite element models of the blades are established to acquire the aerodynamic loads and structural responses of the blades in typhoon environments. The results indicate that, under extreme typhoon loads and considering wind shear and tower shadow effects, the forces near the blade root are greater; the maximum out-of-plane aerodynamic force occurs at the 14% span position of the blade at 90° azimuth, and the maximum torsional aerodynamic moment is experienced at the 26.5% span position of the blade at 270° azimuth. When the blade pitch angle and rotor yaw angle do not reach ideal states, the deflection of ultra-long flexible blades can increase by up to 3.26 times. These findings overcome the limitations of traditional uniform wind field studies and provide a theoretical basis for subsequent coping strategies for offshore blades under typhoon conditions. Full article