Search Results (34)

The geostationary hyperspectral infrared sounder (GeoHIS) provides atmospheric variables at high spatiotemporal resolution. Consequently, GeoHIS can provide valuable information for improving real-time forecasting and enhancing the performance of numerical weather prediction (NWP). GeoHIS provides higher temporal resolution than that of a polar-orbiting platform, observing the same region 2~3 times daily. Therefore, we assess the forecast impact of a next-generation GeoHIS on a numerical model according to observation density using KIM-OSSE (Korean Integrated Model–Observing System Simulation Experiment) in this study. Simulated observations are generated from the nature run dataset (ECO 1280) provided by Cooperative Institute for Research in the Atmosphere at Colorado State University (CIRA/CSU). These simulated observations are then assimilated into KIM, after which KIM generates forecast fields. Using this framework, we evaluated the impact of GeoHIS on a global numerical model. The results showed noticeable improvements in geopotential height, particularly in the mid- and upper troposphere, while wind, temperature, and humidity remain largely unchanged in EXP-1 and EXP-2. An analysis of the sensitivity to GeoHIS temporal resolution, by comparing hourly data and 3-hourly data, revealed that higher temporal resolution leads to greater forecast improvements. Full article

Wind energy is a cornerstone of Saudi Arabia’s renewable energy transition under Vision 2030, yet national-scale wind resource assessment remains constrained by sparse and unevenly distributed ground observations. This study evaluates the spatiotemporal variability of near-surface wind speed and direction across Saudi Arabia using Global Ensemble Forecast System Reforecast (GEFSv12 Reforecast) wind fields integrated with a GIS 10.8-based processing workflow. Wind vectors (U and V) were extracted from NetCDF files, converted to wind speed and meteorological wind direction, and analyzed at 183 grid-cell “virtual stations” covering the Kingdom for a five-year period (2018–2022) at four synoptic time steps (6-hourly). The resulting database comprises approximately 1,336,632 records. A practical verification using five airport stations matched to nearest virtual stations shows strong agreement between GEFS-derived and observed wind speeds (RMSE = 1.823; R² = 0.879), supporting the dataset’s suitability for regional screening. Results reveal pronounced spatial heterogeneity and diurnal structure: northern, northeastern, central, and eastern Saudi Arabia consistently exhibit moderate-to-high winds (often >5.5 m/s) with persistent northwesterly–westerly flow, while western and southwestern coastal zones show stronger diurnal variability associated with thermal and sea-breeze influences. Peak, spatially coherent winds occur during the late-day synoptic period, forming a broad high-wind corridor across central and eastern regions. Given the ~1° (~110 km) resolution, findings are intended to be used for macro-scale wind-resource screening and the prioritization of high-wind zones for follow-up assessment. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

Ground-level ozone pollution poses significant risks to public health and ecosystems and remains a major environmental challenge worldwide. Accurate forecasting is difficult due to the nonlinear formation mechanisms of ozone and its strong dependence on meteorological conditions. This study proposes a Wind Speed and Direction-Based Dynamic Spatiotemporal Graph Attention Network (WSDST-GAT) for multi-step hourly ground-level ozone prediction. The model integrates a wind-aware dynamic graph to represent anisotropic pollutant transport and a Transformer-based temporal encoder to capture long-range dependencies. Meteorological variables are incorporated to enhance physical interpretability and predictive robustness. A co-kriging module is further employed to reconstruct continuous spatial ozone fields with quantified uncertainty. Using hourly observations from 35 monitoring stations in Beijing, WSDST-GAT achieves a Coefficient of Determination of 0.957, with a Mean Absolute Error of 5.25 μg/m³, and a Root Mean Square Error of 9.58 μg/m³. The prediction intervals demonstrate strong reliability with a Prediction Interval Coverage Probability of 94.01% and a Prediction Interval Normalized Average Width of 0.174. These results indicate that the proposed framework provides an accurate and physically informed solution for ozone forecasting and air quality management. Full article

The Changjiang estuary–Hangzhou Bay region is a critical zone of land–sea interaction, where Total Suspended Matter (TSM) dynamics significantly influence coastal ecology and engineering. While previous studies have examined individual factors affecting TSM variability, the synergistic effects of “tide–monsoon–current” interactions and the actual pathways of turbid plume transport remain poorly understood. Using GOCI satellite data, in situ buoy measurements, and voyage data from 2020, this study applied Data Interpolating Empirical Orthogonal Functions (DINEOFs) and comprehensive spatio-temporal analysis to reconstruct continuous high-resolution TSM fields and elucidate multi-factor controls on TSM dynamics. Based on this high-resolution dataset of TSM, we found that, during the dry season, elevated TSM concentrations are primarily driven by wind–tide resuspension and transport under the comprehensive forcing of the Jiangsu Alongshore Current (JAC), the Yellow Sea Warm Current (YSWC), and wind–tide-induced flows. Contrary to the conventional understanding, the Jiangsu-origin surface TSM can transport to the outer sea without supplementing the TSM in the Turbidity Maximum Zone (TMZ). The YSWC in autumn can cause either low C_TSM gradients or high gradients nearshore depending on whether it is carrying Korean coastal turbid water or not. During the wet season, stratification induced by the Changjiang freshwater discharge suppresses wind–tide resuspension, reducing TSM concentrations in the TMZ and the Qidong water. However, the Changjiang freshwater combined with the Taiwan Warm Current (TWC) dilutes surface TSM in Hangzhou Bay, where the two water masses meet on the 10 m isobath. These insights into factor interactions and TSM plume pathways provide a scientific basis for improved environmental monitoring and coastal management. Full article

Coastal dune systems are dynamic landforms shaped by aeolian processes, in which onshore winds transport and deposit sediments behind natural or artificial barriers. The Çukurova Delta Plain, Turkey’s largest delta along the Eastern Mediterranean, contains extensive dune fields, particularly within the Seyhan and Ceyhan Deltas. Despite technological advances in UAV photogrammetry and Structure-from-Motion (SfM) techniques, studies on coastal dune dynamics in Turkey remain scarce. This study demonstrates the first comprehensive assessment of the spatiotemporal evolution of coastal dunes in the Çukurova Delta Plain. Historical aerial photographs and high-resolution UAV imagery were analyzed to evaluate long-term and seasonal morphological changes. The results indicate notable spatial and temporal variability in sediment budgets, with distinct erosion and accretion patterns across the two deltas. While some dune segments remained stable over decades, others displayed strong seasonal responses to wind and sediment dynamics. These findings enhance the understanding of deltaic coastal geomorphology and provide critical insights for sustainable management of vulnerable dune ecosystems under increasing human and climatic pressures. Full article

Ocean surface wind vector (OWV) is a key variable for ocean remote sensing and tropical cyclone (TC) monitoring. This study presents the first comprehensive intercomparison of Ku-band OWV products from FY-3E/WindRAD and HY-2B/SCA scatterometers using full-year data from 2022 (583,805 spatiotemporal collocations), with both sensors sampling the morning–evening local-time sector in sun-synchronous orbits. Results indicate strong agreement in wind speed (R = 0.95; mean bias −0.47 m/s; RMSE 1.30 m/s) and wind direction (mean bias 0.22°; std 28.13°) for wind speeds ≥ 3.4 m/s (Beaufort scale B3 and above), with the highest consistency across Beaufort scale 3–8 (B3–B8); however, at wind speeds greater than 20.8 m/s (B9) the bias increases. A fusion leveraging FY-3E’s fine resolution and HY-2B’s wide coverage is implemented and applied to Super Typhoon Hinnamnor (2022), enhancing the spatial coverage and structural detail of TC winds. Quadrant 34 kt wind radii (R34) are estimated from the fused wind fields and evaluated against the best-track data from the Joint Typhoon Warning Center (JTWC), showing close agreement during compact, symmetric TC stages but larger differences during structural reorganization. Overall, the findings confirm inter-satellite consistency for the two Chinese scatterometers and demonstrate the practical value of a multi-source fusion approach that benefits TC monitoring, wind radii estimation, and marine weather services. Full article

As climate change intensifies, enhancing numerical weather prediction (NWP) accuracy has been increasingly critical. While data assimilation optimizes NWP initial conditions, its effectiveness over complex terrain requires further systematic evaluation. This study implemented a high-resolution WRF/4D-Var data assimilation framework, overcoming its inherent limitation of not supporting two-layer nested assimilation across domains by designing a two-layer nested “assimilation-forecast” workflow. Representative winter and summer cases from February and June 2019 were selected to evaluate improvements in near-surface and upper-air meteorological parameters. The results indicated that the 4D-Var data assimilation significantly improved the correlation coefficients of near-surface variables during winter by 2.9% (temperature), 14.5% (relative humidity), 6.6% (wind speed), and 10.4% (wind direction), with even greater improvements observed in summer reaching 13.3%, 5.8%, 35.3%, and 42.3%, respectively. Meanwhile, 4D-Var considerably enhanced the atmospheric vertical profiling, with the middle troposphere (300–700 hPa) exhibiting the most pronounced improvement. Among different surface types, water bodies exhibited the strongest assimilation response. Results also revealed systematic corrections to the background fields, with February exhibiting more uniform adjustments in contrast to June’s complex spatiotemporal patterns. Positive effects persisted throughout the 24-h forecasts, with the maximum benefit occurring within the first 12 h. These results demonstrate the effectiveness of 4D-Var in regional meteorological forecasting, highlighting its value for constructing high-precision multidimensional meteorological fields to support both weather and air quality simulations. Full article

The variable camber morphing wing has the potential to achieve improved flight performance across different flight conditions by changing its geometry according to changing flight conditions. Evaluating the subtle aerodynamic benefits of variable camber technology necessitates wind tunnel testing under flight Reynolds number conditions. In high Reynolds number wind tunnels, the cryogenic environment readily damages model surface profiles through desublimation and frost, compromising test data accuracy. Consequently, cryogenic wind tunnels must enforce rigorous water vapor control standards. To address potential water vapor effects during cryogenic wind tunnel testing, high-resolution optical measurement techniques were employed to quantify the spatiotemporal evolution of desublimation frost thickness on a typical supercritical airfoil surface. Combined with numerical simulations, the mechanisms governing the frost layer’s influence on aerodynamic characteristics and flow field structures were systematically investigated. The results reveal that the influence of water vapor desublimation on the aerodynamic characteristics under diverse cryogenic working conditions has a commonality, and the difference in aerodynamic parameters shows an increasing tendency as the frost time increases; water vapor desublimation has an obvious influence on the flow structure of the airfoil and its pressure distribution on the surface, which increases flow instability and leads to the backward shift of the shock wave position; larger frost thickness gradients along the flow direction cause more drastic changes in pressure distribution and flow structure; and a larger rate of water vapor desublimation results from a lower temperature and a higher concentration of water vapor in the test environment, which causes frosting to have a more severe impact on the airfoil’s aerodynamic characteristics and flow structure. The findings establish a technical basis for cryogenic wind tunnel moisture control standards and provide a solid foundation for the refined assessment of aerodynamic benefits of the camber morphing wing. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February 2024, along the southeastern Chinese coast. Employing a Coherent Doppler Wind Lidar (CDWL) system onboard the R/V “Yuezhanyu” research vessel, we investigated the spatiotemporal variability of MABL characteristics through integration with ERA5 reanalysis data. The key findings reveal a significant positive correlation between MABL height and surface sensible heat flux in winter, underscoring the dominant role of sensible heat flux in boundary layer development. Through the Empirical Orthogonal Function (EOF) analysis of the ERA5 regional boundary layer height, sensible heat flux, and sea level pressure, we demonstrate MABL height over the coastal seas typically exceeds the corresponding terrestrial atmospheric boundary layer height and exhibits weak diurnal variation. The CDWL observations highlight complex wind field dynamics influenced by synoptic conditions and maritime zones. Compared to onshore regions, the MABL over offshore areas further away from land has lower wind shear changes and a more uniform wind field. Notably, the terrain of Taiwan, China, induces significant low-level jet formations within the MABL. Low-level jets and low boundary layer height promote the pollution episode observed by CDWL. This research provides new insights into MABL dynamics over East Asian marginal seas, with implications for improving boundary layer parameterization in regional climate models and advancing our understanding of coastal meteorological processes. Full article

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article

Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (TDSHM) and data-driven deep learning models (LSTM and CNN) for runoff forecasting under variable rainfall conditions. Using 214 rainfall–runoff events (2013–2023) from the Qiaotou watershed in Nanjing, China, the TDSHM integrates rainfall momentum, wind effects, and hydrodynamic principles to resolve spatiotemporal flow dynamics, while LSTM and CNN models leverage seven hydrological features for data-driven predictions. Results demonstrate that the TDSHM achieved superior accuracy, with a mean relative error of 10.77%, Nash–Sutcliffe Efficiency (NSE) of 0.801, and Mean Absolute Error (MAE) of 3.17 mm, outperforming LSTM (24.38% error, NSE = 0.751, MAE = 4.61 mm) and CNN (28.10% error, NSE = 0.506, MAE = 6.82 mm). The TDSHM’s explicit physical interpretability enabled precise simulation of vegetation-modulated runoff processes, validated against field observations (92% predictions within ±15% error). While LSTM captured temporal dependencies effectively, CNN exhibited limitations in sequential data processing. This study highlights the TDSHM’s robustness for scenarios requiring mechanistic insights and the complementary role of LSTM in data-rich environments. The findings provide critical guidance for flood risk management, soil conservation, and model selection trade-offs between physical fidelity and computational efficiency. Full article