Search Results (27)

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Low-level jets (LLJs) play an important role in the transport of heat, water vapor, and atmospheric pollutants. Based on one year (1 September 2023 to 31 August 2024) of tropospheric wind profiler radar (RWP) observations at the Wenjiang Meteorological Observation Base in Chengdu, this study systematically investigates the wind field structure in the middle and lower troposphere over the Chengdu region and the vertical distribution and evolution characteristics of LLJs. The effective detection height of the RWP reaches at least 7.4 km throughout the year, demonstrating good consistency with concurrent radiosonde data. Horizontal wind speed accelerates markedly above 3 km, with the strongest vertical gradient observed in winter. In the lower layer, the prevailing wind direction is primarily controlled by mountain-valley breezes; with increasing altitude, the westerly belt gradually becomes the dominant wind system. Within the atmospheric boundary layer (below 1 km), the wind field exhibits a distinct diurnal cycle: easterly winds dominate in the afternoon, shifting to northerly winds at night. Surface wind speed peaks in the afternoon, whereas upper-level wind speed peaks at night. The occurrence frequency of LLJs is highest in July (26.3% for LLJ-1), followed by April (17.8%). The prevailing wind directions of LLJs are north-northeasterly and northeasterly, and jet core heights are mainly distributed between 0.7 and 1.9 km. For weaker LLJs (LLJ-1 and LLJ-2), both frequency and intensity are higher at night than during the day, peaking at 22:00. These findings deepen our understanding of boundary layer dynamics over complex basin terrain and provide a high-resolution observational benchmark for model improvements and weather warnings. Full article

Harmonising heterogeneous met-ocean time series to a common temporal resolution is a prerequisite for integrated marine renewable energy assessments. Such datasets often differ in their sampling frequency, statistical distribution, and non-stationarity, complicating joint analysis. This study presents a practical multi-criteria framework for selecting temporal interpolation strategies for met-ocean datasets, explicitly balancing prediction accuracy and computational efficiency. Six environmental variables relevant to offshore renewable energy—wind speed, significant wave height, energy period, peak period, global horizontal irradiance, and upper-ocean thermal gradients—are analysed using ten-year reanalysis datasets for the Madeira Archipelago. Six commonly used deterministic time-domain interpolation methods are evaluated within a unified validation framework combining training–test splits, k-fold cross-validation, and Monte Carlo resampling. Their performances are quantified using the relative root mean square error and computational time, integrated through a composite performance score. The results show that makima interpolation provides the most consistent compromise between accuracy and efficiency for most variables in dense, regularly sampled met-ocean datasets, while spline-based approaches perform better for highly skewed solar irradiance. Preprocessing steps, such as detrending and distribution normalisation, yield only marginal improvements for dense, regularly sampled datasets, and method rankings remain stable under moderate changes in accuracy–speed weightings. Rather than proposing a universal interpolator, this work delivers a reproducible decision-support workflow for temporal resampling of multi-variable met-ocean datasets, supporting early-stage marine renewable energy assessments. Full article

This study experimentally investigates the cooling performance of a single-opening wind catcher model under varying orientations and wind speeds. The wind catcher was connected to a horizontal cavity representing an indoor space, with a rear outlet simulating a window opening. Electric resistors were installed at the catcher shaft and in the middle of the cavity length to simulate the building’s heat loads. Experiments were conducted in a wind tunnel, where K-type thermocouples were employed to record temperature variations for both closed and open cavity ends. Five wind speeds (4–9 m/s) and five orientations (0–180°) were examined. Under the closed-cavity configuration, the maximum temperature reduction (cooling) of 4 °C occurred at an orientation of 180°, at which the catcher opening was positioned on the leeward side. This orientation created a low-pressure region at the catcher’s inlet, located within the wake of the model, which, combined with a favorable vertical temperature gradient, enhanced suction-driven cooling. In the open-cavity configuration, cooling was observed for all orientations and wind speeds. The greatest temperature reduction of 6 °C occurred at the 180° orientation, whereas other orientations produced lower temperatures changes, down to 2 °C. Full article

Small wind turbines operating at low heights frequently experience rapidly fluctuating and highly turbulent wind conditions that challenge conventional reactive pitch-control strategies. Under these non-stationary regimes, sudden gusts produce overspeed events that increase mechanical stress, reduce energy capture, and compromise operational safety. Addressing this limitation requires a control scheme capable of anticipating aerodynamic disturbances rather than responding after they occur. This work proposes a hybrid anticipatory pitch-control approach that integrates a conventional PI regulator with a data-driven rotor-speed prediction model. The main novelty is that short-term rotor-speed forecasting is embedded into a standard PI loop to provide anticipatory action without requiring additional sensing infrastructure or changing the baseline control structure. Using six years of real wind and turbine-operation data, an optimized Random Forest model is trained to forecast rotor speed 20 s ahead based on a 60 s historical window, achieving a prediction accuracy of RMSE = 0.34 rpm and R² = 0.73 on unseen test data. The predicted uses a sliding-window representation of recent wind–rotor dynamics to estimate the rotor speed at a fixed horizon (t + Δt), and the predicted signal is used as the feedback variable in the PI loop. The method is validated through a high-fidelity MATLAB/Simulink model of 14 kW small horizontal-axis wind turbine, evaluated under four wind scenarios, including two previously unseen conditions characterized by steep gust gradients and quasi-stationary high winds. The simulation results show a reduction in overspeed peaks by up to 35–45%, a decrease in the integral absolute error (IAE) of rotor speed by approximately 30%, and a reduction in pitch-actuator RMS activity of about 25% compared with the conventional PI controller. These findings demonstrate that short-term AI-based rotor-speed prediction can significantly enhance safety, dynamic stability, and control performance in small wind turbines exposed to highly variable atmospheric conditions. Full article

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, parameterized by boundary-condition descriptors, to train the surrogate for velocity magnitude and turbulent kinetic energy (TKE). The CAE compresses horizontal flow fields into a low-dimensional latent space, providing an efficient representation of complex flow structures. The DNN establishes a mapping from input descriptors to the latent space, and flow reconstructions are obtained through the frozen decoder. Validation against CFD demonstrates that the surrogate captures velocity gradients and TKE distributions with mean absolute errors below 1% in most of the domain, while residual discrepancies remain confined to near-wall regions. The approach yields a computational speed-up of approximately 4000× relative to CFD, enabling deployment on embedded or edge hardware. For path planning, the domain is discretized as a k-Non-Aligned Nearest Neighbors (k-NANN) graph, and an A* search algorithm incorporates heading constraints and surrogate-based TKE thresholds. The integrated pipeline produces turbulence-aware, dynamically feasible trajectories, advancing the integration of high-fidelity flow predictions into urban air mobility decision frameworks. Full article

Blue–green infrastructure is widely recognized for mitigating the urban heat island effect. However, most existing ENVI-met 5.6.1 studies focus on average thermal conditions and overlook fine-scale spatial gradients. This study investigates the urban park in Luoyang City by integrating high-resolution 3D ENVI-met simulations, multi-source data, and field measurements to quantify thermal gradients between park interiors and surrounding built-up areas. A midline cut-off approach was applied to extract horizontal and vertical thermal profiles. The results show that (1) temperature and physiological equivalent temperature (PET) differences are most pronounced at park edges and transition zones, where vegetation and water bodies serve as natural cooling buffers; (2) urban form indicators, especially the building coverage and open space ratio, significantly impact wind speed and the PET, with greenery improving thermal comfort via shading and evapotranspiration, while impervious surfaces intensify heat stress; (3) the park exhibits a distinct cold island effect, with the average PET in the core area up to 12.3 °C lower than in adjacent built-up zones. The effective cooling distance, which is identified through buffer-based zonal statistics, rapidly attenuates within approximately 200 m from the park boundary. These findings offer a novel spatial perspective on thermal regulation mechanisms of urban landscapes and provide quantitative evidence to guide the design of climate-resilient green infrastructure. Full article

The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs. Full article

Wind fields provide direct power for exchanging energy and matter in the atmosphere. All-fiber coherent Doppler lidar is a powerful tool for detecting boundary-layer wind fields. According to the characteristics of the lidar echo signal, an adaptive noise-reduction peak retrieval (ANRPR) algorithm is proposed in this study. Firstly, the power spectrum data are divided into several continuous range gates according to the time series. Then, the adaptive iterative reweighted penalized least-squares (airPLS) method is used to reduce the background noise. Secondly, the continuity between spectra is enhanced by 2D Gaussian low-pass filtering. Finally, an adaptive peak-retrieval algorithm is employed to extract the Doppler shift, facilitating the synthesis of a spatial atmospheric 3D wind field through the vector synthesis method. When comparing data from different heights of the meteorological gradient tower, both the horizontal wind-speed correlation and the horizontal wind-direction correlation exceed 0.90. Experimental results show that the proposed algorithm has better robustness and a longer detection distance than the traditional algorithm. Full article

Surface winds over California can compound fire risk during autumn, yet their long-term trends in the face of decadal warming are less clear compared to other climate variables like temperature, drought, and snowmelt. To determine where and how surface winds are changing most, this article uses multiple reanalyses and Remote Automated Weather Stations (RAWS) to calculate autumn 10 m wind speed trends during 1979–2020. Reanalysis trends show statistically significant increases in autumn night-time easterlies on the western slopes of the Sierra Nevada. Although downslope windstorms are frequent to this region, trends instead appear to result from elevated gradients in warming between California and the interior continent. The result is a sharper horizontal temperature gradient over the Sierra crest and adjacent free atmosphere above the foothills, strengthening the climatological nocturnal katabatic wind. While RAWS records show broad agreement, their trend is likely influenced by year-to-year changes in the number of observations. Full article

Extreme precipitation events have been occurring frequently worldwide, and their causative factors and convection initiation (CI) mechanisms have been attracting more and more attention in recent years. As a comprehensive study on the CI mechanisms of extreme rainstorms over the northern slope of the Kunlun Mountains (KLM), Xinjiang, based on both observational and high tempo-spatial numerical simulation, the major findings of this work are as follows: A cold pool (CP) was formed in the northwestern Tarim Basin under the influence of early precipitation evaporation, and it moved towards the northern slope of the KLM several hours before the CI. With the movement of the CP, a significant vertical temperature gradient was formed close to the leading edge of the CP, thereby enhancing local convective instability (up to ~10 PVU). In addition, the vertical shear of the horizontal winds at the leading edge of the CP led to a notable increase in the baroclinic component of moist potential vorticity, thus reinforcing the local conditional symmetric instability (up to ~8 PVU), providing another important unstable energy for the CI. In addition, the combined effect of the convergent lifting of a boundary layer jet (BLJ, the maximum wind speed below 1 km exceeding 10 m s⁻¹) and the significant frontogenetical forcing (up to ~100 × 10⁻⁸ K m⁻¹ s⁻¹) at the leading edge of the CP were the causes of the release of the unstable energies. Further analysis of the frontogenetical forcing associated with the CP indicates that the convergence (up to ~2 × 10⁻³ s⁻¹), diabatic heating and slantwise terms (indicates the baroclinicity and inhomogeneity of the vertical momentum in horizontal direction) were the major contributors, whereas the deformation term at the leading edge of the CP provided a relatively weaker contribution. Full article

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization. Full article

Focusing on the complex underlying surface area in central–southern Hainan Island, this study uses the Advanced Research Weather Research and Forecasting Model (Version 4.0) to simulate a typical mountain circulation case without obvious weather system forcing, and tries to reveal the impacts of land cover changes on the mountain circulation. One control experiment (CNTL) and three sensitivity experiments, in which the current land cover is taken as areas of uniform evergreen broadleaf forest (FOREST), grassland (GRASS), and bare soil (DESERT) coverage, are conducted. The results show that the near-surface wind speed increases with decreasing surface roughness, and DESERT shows the most obvious change as compared with the CNTL. In the vertical direction, FOREST shows the strongest valley breeze circulation, with the largest horizontal and vertical extents of circulation, as well as the highest vertical extent of the updraft. DESERT shows the weakest valley breeze circulation with the longest duration. GRASS shows the slightest change from the CNTL. The possible impact mechanism is that the land cover changes could affect the surface energy partitioning, leading to a variation in the temperature distribution (i.e., the horizontal potential temperature gradient and boundary layer stability), in turn affecting the structure and evolution characteristics of the mountain circulation. Full article

Coastal wind speed gradients relevant for offshore windfarming are analysed based on synthetic aperture radar (SAR) data. The study concentrates on situations with offshore wind directions in the German Bight using SAR scenes from the European satellites Sentinel-1A and Sentinel-1B. High resolution wind fields at 10 m height are derived from the satellite data set and respective horizontal wind speed gradients are investigated up to about 170 km offshore. The wind speed gradients are classified according to their general shape with about 60% of the cases showing an overall increase of wind speeds with growing distance from the coast. About half of the remaining cases show an overall wind speed decrease and the other half a decrease with a subsequent increase at larger distances from the coast. An empirical model is fitted to the horizontal wind speed gradients, which has three main parameters, namely, the wind speed over land, the equilibrium wind speed over sea far offshore, and a characteristic adjustment length scale. For the cases with overall wind speed increase, a mean absolute difference of about 2.6 m/s is found between wind speeds over land and wind speeds far offshore. The mean normalised wind speed increase with respect to the land conditions is estimated as 40%. In terms of wind power density at 10 m height this corresponds to an absolute average growth by 0.3 kW/m² and a normalised increase by 160%. The distance over which the wind speed grows to 95% of the maximum wind speed shows large variations with maximum above 170 km and a mean of 67 km. The impact of the atmospheric boundary layer stability on horizontal wind speed gradients is investigated using additional information on air and sea temperature differences. The absolute SAR-derived wind speed increase offshore is usually higher in unstable situations and the respective adjustment distance is shorter. Furthermore, we have found atypical cases with a wind speed decrease offshore to be often connected to stable atmospheric conditions. A particular low-level jet (LLJ) situation is analysed in more detail using vertical wind speed profiles from a wind LIDAR system. Full article

When installing offshore wind farms (OWFs) adjacent to the coast, one needs to consider the combined effects of the wind wakes caused by the OWFs and natural horizontal coastal wind speed gradients (HCWSGs). This study exploits the full Sentinel 1A/B and Envisat archive of synthetic aperture radar (SAR) imagery covering the northern European seas. More than 8700 SAR scenes fit well with our selection criteria and are processed as wind maps for the height 10 m above the sea surface. For eight selected wind farm sites, we systematically compare the wind flow variation before and after wind farm commissioning. Before the commissioning, we observe wind speed gradients up to ±4% for onshore and offshore winds. After the commissioning, we detect a 2–10% reduction in the mean wind speed downstream of the turbines after taking into account the background wind speed gradients. These velocity deficits are proportional to the OWF capacity. Our findings indicate that wind speed maps retrieved from SAR can be used to quantify the complex interactions between natural HCWSGs and turbine-induced effects on the mean wind climate. Ultimately, this can be used in connection with farm planning in coastal waters. Full article