Search Results (611)

High-precision WTC is essential for satellite altimetry and ocean dynamic environment monitoring. Existing WTC approaches often rely on globally unified statistical frameworks, which inadequately represent wind-speed-dependent nonlinear sea-surface microwave radiative responses and are prone to systematic bias under uneven observation distributions. To address these limitations, this study proposes an adaptive WTC method integrating overlapping wind-regime modeling, multi-scale collaborative sample balancing, and a model soft-fusion strategy. Firstly, a modeling framework with overlapping transition zones for low-, moderate-, and high-wind-speed regimes is established according to wind-speed-driven variations in sea-surface radiative responses, and sub-models are trained independently. Subsequently, a multi-scale sample balancing, combining global and local weights, is designed to enhance learning from sparse samples. Finally, a soft-fusion strategy based on a trapezoidal membership function is applied to dynamically weight sub-model outputs, ensuring retrieval continuity across transition zones. Using HY-2C Calibration Microwave Radiometer (CMR) observations, the proposed method is developed, trained, and evaluated against model-derived WTC and collocated Jason-3 AMR-2 measurements. Results show that the proposed method improves overall WTC retrieval accuracy and stability while effectively reducing systematic biases under wind-speed regimes with sparse observations, providing an effective and robust approach for high-accuracy WTC retrieval under various wind-speed conditions. Full article

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RG_ratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R² = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated. Full article

Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < ±0.5 m s⁻¹) and wind direction biases (MB < ±0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM_2.5 concentrations with moderate-to-high correlations (r = 0.44–0.65) and low bias, while AOD and O₃ column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO₂ and CO column abundances but larger discrepancies for HCHO and SO₂, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products. Full article

High-frequency FY-4B aerosol optical depth (AOD) observations provide useful spatiotemporal constraints for dust nowcasting, but their application over bright deserts is limited by retrieval gaps and high-AOD uncertainty. This study develops a physics-informed spatiotemporal learning framework for 15–60 min FY-4B AOD nowcasting over the Taklimakan Desert. Historical FY-4B AOD, valid masks, ERA5 dynamic fields, model-level diagnostics, and surface constraints are organized on a unified 48 × 64 grid. An LSTM–TCN–Transformer temporal backbone is combined with spatial-context encoding, mask-aware observation encoding, and structured source–transport prediction heads to represent both temporal evolution and spatial plume structures. A physics encoder represents boundary-layer mixing, vertical wind shear, source-region emission, upwind transport, and deposition loss. Mask-aware encoding and structured prediction heads are used to handle missing retrievals, source and transport increments, high-AOD tails, and low-confidence regions. Results show that FY-4B AOD constrains the main dust-belt position and spatial extent within 1 h, with skill decreasing from 15 to 60 min. High-coverage samples show more stable spatial structures, whereas low-coverage and extreme high-AOD cases have larger peak underestimation and boundary errors. The proposed framework improves high-AOD event detection and spatial-structure preservation compared with persistence, advective persistence, ConvLSTM, and ST-UNet baselines. An additional case-based comparison with MODIS MAIAC AOD and MERRA-2 dust optical depth shows partial spatial colocation between predicted high-value footprints and independent aerosol-enhancement references; however, the reported skill scores should still be interpreted mainly as spatiotemporal consistency with the FY-4B AOD product field rather than direct validation of true atmospheric dust loading. Full article

To address the limitations of the traditional cross-scanning method in absolute calibration of weather radars using metal spheres, including insufficient spatial coverage, limited target acquisition efficiency, and echo underestimation in inter-range bins, this study proposes a sector scanning field calibration method. In this approach, standard metal spheres are suspended from UAVs, and a three-dimensional scanning volume around their theoretical positions is constructed to enable high-density echo sampling. By applying drive backlash correction, quadratic Gaussian surface fitting, and three-dimensional ellipsoid model inversion, key radar parameters can be retrieved. Experimental results show that the improved sector scanning method enhances automation, accuracy, and robustness in field environments and minor target drifts. The experiments were conducted under low-wind and low-clutter conditions. The average calibration error of antenna pointing is 0.08°, the average error of echo intensity calibration is 0.3 dB, the average beamwidth error is 0.07°, the range resolution is 6.6 m, and the average radial ranging error is 14 m. These results indicate that the proposed method can meet the main calibration requirements of weather radars in the present experiments. Full article

This paper evaluates a dense, total-variation-based optical flow method for retrieving wildfire smoke plume motion vectors from geostationary, deep-space, and airborne remote sensing imagery. Using multiple major fire events, we assess the robustness of the approach across a range of spatial resolutions and time intervals. The test cases include Geostationary Operational Environmental Satellite (GOES) observations of the 2025 Los Angeles Fires and the 2024 Park Fire, imagery from NASA’s Enhanced MODIS Airborne Simulator (eMAS) for the 2019 Sheridan and Williams Flats Fires, and a complementary Park Fire image pair from the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR). Optical flow is computed directly on radiance fields, and smoke plumes are isolated using smoke masks derived from the Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery (SIT-FUSE) framework where available. Performance is evaluated by comparing the root mean square error (RMSE) between original image pairs and between the first image and the second image after warping with the retrieved motion field. RMSE is computed both globally and over smoke-only regions. Across GOES and eMAS cases, optical flow systematically reduces RMSE, often by more than a factor of two within smoke regions, indicating substantially improved frame-to-frame alignment of plume structures after motion correction. The DSCOVR/EPIC case, despite its coarser spatial resolution and longer temporal separation, also shows a marked reduction in global RMSE, demonstrating that the method remains informative under a broader range of observational conditions. For a selected subset of 10 consecutive GOES Park Fire pairs, we additionally compare the retrieved smoke motion vectors with collocated winds from the High-Resolution Rapid Refresh (HRRR) model and find the closest agreement in a broad lower-tropospheric layer centered near 875 hPa. These results show that dense optical flow can capture fine-scale plume evolution in high-temporal-resolution datasets while also providing useful motion estimates in coarser, global-view imagery. RMSE reduction is interpreted here as evidence of improved motion-compensated alignment, while the HRRR comparison provides initial physical context rather than independent validation. The resulting smoke motion vector fields provide a foundation for future comparison with model winds and for applications in plume analysis, fire hazard monitoring, and air quality studies. Full article

Agile innovation is becoming increasingly important for complex mechatronic products. Existing studies often remain at the project management level and offer limited operational guidance for conceptual structural design. This paper proposes an agile innovation design method that integrates the Length–Time (LT) dimension and Theory of Inventive Problem Solving (TRIZ) to translate user feedback into engineering-oriented conceptual solutions. First, user pain points are organized into a fishbone-based functional model, and core problems are mapped to LT dimensions using a natural-language processing rule set. Second, a neural network trained on cases of technological evolution predicts the corresponding TRIZ evolution law. Third, structurally similar engineering cases are retrieved based on LT-dimensional similarity and transformed into conceptual schemes by structural mapping. Finally, the technique for order preference by similarity to an ideal solution is used to rank alternative schemes with explicit normalization, distance calculation, and sensitivity checking. The method is demonstrated through the conceptual redesign of a vertical-axis wind turbine. Full article

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating Harris Hawks Optimization (SC-HHO) retrieval algorithm, which is tailored to the high-dynamic flight environment and stringent payload constraints of UAVs. This algorithm enables real-time wind retrieval with low dependence on external reference data while effectively compensating for platform motion. The performance of the proposed system was validated through the comparative experiment and the UAV-borne experiment. In the comparative experiment, the CDWL showed correlation coefficients above 0.976 in horizontal wind speed and 0.987 in horizontal wind direction relative to a benchmark airborne CDWL system, with corresponding root-mean-square errors better than 0.395 m/s and 4.135°, respectively. During the UAV-borne experiment, the CDWL retrieved platform velocity using the self-compensating mechanism, achieving a standard deviation of 0.080 m/s relative to global navigation satellite system (GNSS) measurements, and successfully acquired wind field information. These results confirm that the developed system provides a viable and practical technical solution for UAV-based remote wind sensing. Full article

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

The radial structure and azimuthal asymmetry of tropical cyclone (TC) eyewall winds are critical for intensity change and wind-related hazards, yet they remain difficult to characterize using conventional observations. Using multi-platform C-band synthetic aperture radar (SAR) wind fields and collocated Stepped Frequency Microwave Radiometer (SFMR) wind speed and rain-rate observations, this study examined TC inner-core structure, eyewall asymmetry, and rainfall-dependent wind retrieval uncertainty for 51 TCs and 130 SAR scenes. The TC inner-core structure was characterized using a best-track-constrained center refinement and quality control procedure, in which the storm center was refined from the minimum of a Gaussian-smoothed SAR wind field and scenes were screened by eye/annulus sampling, eye–eyewall contrast, and annular wind organization. Of the 130 SAR scenes, 53 were retained for refined-center evaluation, and the 32 QC-passed scenes were used for the primary storm-centered structural analysis. The RMW showed a weak tendency to decrease with an increasing SAR-derived maximum azimuthal-mean wind speed, and the normalized wavenumber-1 asymmetry at the RMW decreased in stronger storms. Under strict temporal collocation ($\left|\Delta t\right|\le 30$ min), the SAR–SFMR comparison achieved an RMSE of 4.22 m s⁻¹, a bias of −1.61 m s⁻¹, $R^2$ = 0.82, and a regression slope of 0.90. Rainfall-related SAR–SFMR mismatch was most evident around the eyewall and adjacent outer-eyewall region, indicating the need to consider center uncertainty, scene suitability, temporal collocation, and rain-sensitive retrieval effects when interpreting SAR-derived TC inner-core structure. Full article

Metallurgical coal operations are a significant but poorly constrained source of methane (CH₄) in Canada. We present a multi-instrument analysis of 63 methane plume detections at Fording River Operations, British Columbia (January 2022–March 2026), using the Airborne Visible/Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG; n = 39), the Earth Surface Mineral Dust Source Investigation (EMIT; n = 4) and Tanager-1 (n = 20). Of these, 41 plumes (65%) were quantified, with retrieved emission rates of 34–3622 kg CH₄ h⁻¹; 54% exceeded the 500 kg h⁻¹ super-emitter threshold. Because 73% of detections fall in September and no detections are available for 2023, results characterize the late-summer overpass window and should not be extrapolated seasonally without further coverage. The central finding is a systematic, asymmetric wind-speed disagreement between two numerical weather prediction (NWP) products that maps onto the Integrated Mass Enhancement (IME) quantification outcome. A univariate logistic regression identifies HRRR wind speed as a significant predictor of quantification success (OR = 0.29 per m s⁻¹, 95% CI [0.15, 0.56], p < 0.001; AUC = 0.80; 5-fold cross-validated AUC = 0.79 ± 0.20, fold range 0.45–1.00). Cross-validation against ERA5-Land shows that HRRR exceeds ERA5 by a mean of +0.86 m s⁻¹ (+43%) for unquantified events but shows near-zero disagreement for quantified events (–0.09 m s⁻¹, –6%). A sensitivity analysis restricted to HRRR-forced retrievals (EMIT + Tanager-1, n = 24) confirms the finding is not an artefact of mixed wind data sources (OR = 0.28, AUC = 0.83, p = 0.018). Full article

In the period 2 to 4 March 2026, two rainstorms with intense convective weather occurred within and in the vicinity of Hong Kong, China, in the early rain season of the year in southern China. This is rather uncommon because the atmosphere is still generally stable (with very low or even zero value of convective available potential energy), and upper tropospheric divergence does not yet exist in the region climatologically. The rain episode is documented in this paper from both observational and forecasting aspects. On the observational side, a low-level vortex is found on and near the surface based on Doppler velocity measurements from a newly installed C-band solid-state weather radar. Combining the three-dimensional wind field as retrieved from the weather data and the measurements from the other ground-based remote-sensing meteorological equipment, the intense convection is mainly triggered by middle to lower tropospheric waves, and the vertical circulation in the atmospheric boundary layer may be stretched vertically upward to form the low-level vortex. In the second rainstorm, features of elevated thunderstorms are also identified. On the forecasting side, a high-resolution, limited-area atmosphere–ocean–wave coupled model manages to capture the occurrence and the timing of the heavy rain. The sub-seasonal forecast by a global model also provides a useful indication of the occurrence of above-normal rainfall over southern China, with a rather special feature of a deep and stationary westerly trough located to the north of the Indochina Peninsula. The microscale cyclone could be successfully picked up by the real-time run of a high-resolution numerical weather prediction model with data assimilation. This paper also discusses the weather service aspect of this rather unusual rainstorm episode. Full article

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. Full article

For offshore renewable energy planning and intelligent power management, access to long-term, high-resolution, and physically consistent meteorological and power generation records is essential. Such data supports a wide range of tasks, including resource assessment, hybrid system capacity sizing, grid operation planning, and data-driven forecasting model development. This article presents the construction of a 10-year continuous hourly dataset for 16 deep-sea grid sites in the Beibu Gulf, China, spanning from January 2016 to December 2025. The raw meteorological variables, including 10 m wind speed, wind direction, solar irradiance, and 2 m air temperature, were retrieved from the NASA POWER satellite database and subsequently cleaned using a 24 h periodic substitution algorithm designed to preserve the physical integrity of daily weather cycles. The dataset is organized into two sub-datasets, the Historical Weather Dataset and the Normalized Power Yield Dataset, with the latter providing normalized wind and solar power outputs on a 1.0 per-unit (p.u.) basis derived from a wind turbine power curve model and a PV thermodynamic model. All 32 CSV files are freely accessible online with UTF-8 encoding. The utility of the dataset is illustrated through two representative application cases including offshore site selection with hybrid capacity sizing and physics-informed deep learning forecasting, demonstrating its suitability for both engineering analysis and machine learning model development. Full article