Search Results (185)

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. Because wind turbines are susceptible to these conditions, accurately characterising wind flow in forested environments is vital to ensuring structural reliability and realistic energy-yield assessments. In Latvia, where approximately 51.3% of the territory is covered by forests; the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Wind conditions were evaluated using NORA3 reanalysis data. Wake effects were simulated with the Jensen wake model to estimate annual energy production (AEP), which then informed levelised cost of energy (LCOE) calculations at various hub heights. The results indicate clear seasonal variability and show that increasing hub height leads to higher AEP and lower LCOE, owing to higher wind speeds and reduced turbulence. For forest heights of 0–25 m, the AEP loss increases from 7.8% (hub height = 199 m) to 22.9% (hub height = 114 m). Higher hub heights are also less sensitive to canopy-induced variability, reducing the impact of forest-related turbulence on energy production. Full article

This paper proposes a short-term photovoltaic (PV) power prediction method that integrates aerosol optical feature mining with a dual-channel attention mechanism to address the complex non-linear attenuation effects of atmospheric aerosols and the limitations of existing models in handling sudden meteorological changes and aerosol evolution. Using the optical properties of aerosols and clouds (OPAC) database, a high-dimensional aerosol optical feature set is constructed, which is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm. The prediction scenarios are categorized into polluted and clean regimes through K-means clustering. A dual-channel encoder–decoder network, combining bidirectional long short-term memory (BiLSTM) and iTransformer, is developed to capture high-frequency meteorological volatility and low-frequency aerosol evolution. A bidirectional cross-attention mechanism enables deep feature interaction between the optical and meteorological channels. The method is validated using in situ measurements from a PV station in Hebei, China, along with aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological data from the ECMWF Reanalysis v5 (ERA5). Experimental results demonstrate an average reduction of approximately 29.83% in mean absolute error (MAE) on polluted days and 15.22% on clean days. Interpretability analysis reveals distinct physical mechanisms driving the predictions, emphasizing the role of extinction on polluted days and scattering on clean days. Full article

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

The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates the frequency, amplitude, and dynamical–thermal characteristics of SSWs under historical and Representative Concentration Pathway (RCP) 4.5 scenarios, focusing on stratospheric air temperature (T_s) and zonal wind speed (U_h) at the 10° N and 60° N latitudes. The fifth-generation ECMWF atmospheric reanalysis (ERA5) is employed as the reference dataset. Simulations of five Coupled Model Intercomparison Project Phase 5 (CMIP5) models, represented by M1 to M5, are analyzed. The primary group of models included 1) the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1-3, M1), 2) the Hadley Center Global Environmental Model, version 2—Carbon Cycle (HadGEM2-CC, M2), and 3) the Max Planck Institute Earth System Model—Medium Resolution (MPI-ESM-MR, M3). The analysis period covers SSW events related to the Quasi-Biennial Oscillation (QBO) in the Northern Hemisphere (NH) from 1980 to 2100. The key findings indicate that while M1, M2, and M3 simulate SSW occurrence correctly for the 21st century, they exhibit significant systematic deficiencies in capturing the structural dynamics of SSW events. Specifically, the M1, M2, and M3 models underestimate the polar stratospheric temperature amplitude (T_amp) by approximately 75–80% and zonal wind amplitude (U_amp) by more than 60% compared to the ERA5 analysis. Furthermore, ERA5 exhibits a strong negative correlation (R ≈ −0.8) between U_h and T_s that is not estimated accurately using the present models. The importance of the horizontal resolution of the models and wave–mean flow interactions in determining SSW intensity and occurrence is also found to be a critical metric. Results suggest that SSW definition criteria affect Arctic and midlatitude weather system prediction at a rate of 61–82%. It is concluded that the primary configurations of CMIP5 models for accurately capturing the dynamical structure and evolution of QBO–SSW interactions are needed, and that they affect future projections of SSW events. Full article

Air pollution remains a major global public health concern, with fine particulate matter (PM_2.5) recognized as an important environmental risk factor for lung cancer. This ecological study assessed lung cancer mortality attributable to long-term PM_2.5 exposure in the 26 Brazilian state capitals and the Federal District (Brasília) from 2014 to 2023. Annual mean PM_2.5 concentrations were estimated using reanalysis-based PM_2.5 concentration estimates and atmospheric reanalysis data, ensuring consistent spatial and temporal coverage. Mortality data were obtained from the Brazilian Mortality Information System (SIM/DATASUS). Health impacts attributable to PM_2.5 exposure were estimated using the World Health Organization’s AirQ+ model, based on exposure–response functions from the Global Burden of Disease framework. During the study period, 97.41% of annual PM_2.5 means exceeded the WHO Air Quality Guideline of 5 µg/m³, and 28.52% surpassed the current Brazilian regulatory limit. Higher concentrations were observed mainly in capitals from the North and Southeast regions, reflecting the influence of biomass burning, urbanization, and regional atmospheric processes. Approximately 13.56% of lung cancer deaths in Brazilian capitals were attributable to PM_2.5 exposure, with the highest absolute numbers concentrated in the Southeast region. These findings demonstrate a substantial and spatially heterogeneous lung cancer burden associated with urban air pollution in Brazil and highlight the need for strengthened air quality management and targeted urban public health policies. Full article

Reduced soil moisture (SM) together with elevated vapor pressure deficit (VPD) suppresses gross primary productivity (GPP) and thus weakens the capacity of the terrestrial carbon pool. Against the backdrop of global climate change, soil and atmospheric drought exert a more profound impact on vegetation growth, and their combined impacts remain unclear. Based on multi-source remote sensing observations and reanalysis datasets, three vegetation remote sensing indices, GPP, SIF, and NDVI (collectively referred to as Vegetation Remote Sensing Indices, VSI), are employed in this study to assess the relative impacts of soil and atmospheric drought on terrestrial vegetation. First, Copula-based conditional probabilities are applied to identify which factor (reduced SM or high VPD) plays a dominant role under conditions of declining vegetation productivity and to determine their corresponding thresholds. Furthermore, the underlying driving mechanisms are elucidated by utilizing Structural Equation Modeling (SEM) for path analysis to clarify how climatic factors indirectly affect vegetation productivity by influencing SM and VPD. The results suggest that vegetation growth in China’s different climatic zones is affected by distinct factors. Specifically, SM is the primary factor influencing vegetation productivity, dominating 71.16% of the nation’s vegetated areas. Its influence is particularly pronounced in arid and semi-arid regions. In contrast, the impact of VPD is predominantly concentrated in semi-humid plain regions. Furthermore, the critical thresholds for SM in different climate zones are identified: the threshold averages approximately 0.33 m³/m³ in humid and plateau regions and 0.13 m³/m³ in arid and semi-arid regions. The SEM analysis further reveals the complex pathways by which climatic variables influence vegetation growth. In SM-dominated regions, higher SM directly promotes vegetation growth; in VPD-dominated regions, drier air imposes a stronger suppression on vegetation growth. Nonetheless, the plateau temperate semi-arid zone demonstrates distinct hydrometeorological characteristics. Attributed to the region’s unique hydrometeorological conditions, the negative effects of higher VPD are generally outweighed by the favorable conditions for photosynthesis with which it co-occurs. These findings clarify the intricate impacts of SM and VPD on vegetation productivity, providing a foundational framework for the development of tailored ecological management strategies and drought early warning systems. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article

Climate change increasingly threatens the sustainability of regional water resources; therefore, robust station-scale precipitation projections are essential for basin-level planning. This study aims to develop and evaluate a hybrid, machine-learning-based statistical downscaling framework to generate monthly precipitation projections for the 21st century in the Büyük Menderes Basin, western Türkiye, using the HadGEM3-GC31-LL global climate model from the CMIP6. Monthly observations from 23 rainfall observation stations and ERA5 reanalysis predictors were employed to train station-specific Random Forest (RF) models, with optimal predictor sets identified through a multistage selection procedure (MPSP). Coarse-resolution general circulation model (GCM) fields were harmonized with ERA5 data using a three-stage inverse distance weighting (IDW), Delta, and Variance rescaling approach. The downscaled projections were bias-corrected using Quantile Delta Mapping (QDM) to maintain the climate-change signal. The RF models exhibited strong predictive skill across most stations, with test Nash–Sutcliffe Efficiency (NSE) values ranging from 0.45 to 0.81, RSR values from 0.43 to 0.74, and PBIAS values from −21.99% to +5.29%. Future projections indicate a basin-wide drying trend under both scenarios. Relative to the baseline, mean annual precipitation is projected to decrease by approximately 12.2, 19.6, and 33.7 mm in the near (2025–2050), mid (2051–2075), and late (2076–2099) periods under SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5, a moderate greenhouse gas scenario). Under the high-emission SSP5-8.5 scenario, projected decreases are 25.2, 53.2, and 86.9 mm, respectively. Late-century reductions reach approximately 15–22% in several sub-basins. These findings indicate a substantial decline in future water availability and underscore the value of RF-based hybrid downscaling and trend-preserving bias correction for water resources planning in semi-arid Mediterranean basins. Full article

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

This study addresses the issues of sparse observations from buoys in the tropical Indian Ocean and systematic biases in reanalysis products by proposing a daily-mean wind speed reconstruction framework that integrates multi-source meteorological fields. This study also considers the impact of different source domains on model pre-training, with the goal of providing reliable data support for wind energy assessment. The model was pre-trained using data from the Americas and tropical Pacific buoys as the source domain and then fine-tuned on Indian Ocean buoys as the target domain. Using annual leave-one-out cross-validation, we evaluated the model’s performance against uncorrected ERA5 and CCMP data while comparing three deep reconstruction models. The results demonstrate that deep models significantly reduce reanalysis bias: the RMSE decreases from approximately 1.00 m/s to 0.88 m/s, while R² improves by approximately 8.9% and 7.1% compared to ERA5/CCMP, respectively. The Branch CNN–Transformer outperforms standalone LSTM or CNN models in overall accuracy and interpretability, with transfer learning yielding directional gains for specific wind conditions in complex topography and monsoon zones. The 20-year wind energy data reconstructed using this model indicates wind energy densities 60–150 W/m² higher than in the reanalysis data in open high-wind zones such as the southern Arabian Sea and the Somali coast. This study not only provides a pathway for constructing high-precision wind speed databases for tropical Indian Ocean wind resource assessment but also offers precise quantitative support for delineating priority development zones for offshore wind farms and mitigating near-shore engineering risks. Full article

The thermal forcing of the Tibetan Plateau (TP) significantly influences the Asian summer monsoon. However, its interaction with cloud feedbacks remains unclear due to the limitations of synchronous analysis and traditional cloud classification over the TP. By applying an improved cloud-classification algorithm—which integrates cloud microphysical properties to improve low-cloud detection—to CERES data (2001–2023), we generated a long-term cloud-type dataset. Combined with ERA5 reanalysis data, we systematically analyzed the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the TP Monsoon Index (TPMI). Results indicate a vertical cloud redistribution over the TP, with high cloud cover (HCC) decreasing and low cloud cover (LCC) increasing. HCC is strongly synchronized with snowfall and significantly affects surface radiation, while net CRF and sensible heat flux show delayed responses, peaking when HCC leads by about one month. A composite analysis of winter low-HCC events reveals that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately 1–2 months, while the TPMI shows a significant response around month zero. These findings highlight the key role of cloud–radiation–snowfall interactions in modulating TP thermal forcing. Full article

We examine how the Arctic Oscillation (AO) shapes winter sea-surface-temperature (SST) variability in the East/Japan Sea, with a focus on sub-seasonal SST memory (how long anomalies persist) and air–sea coupling (where SST and atmospheric anomalies co-vary). Using daily OISST v2.1 and ERA5 reanalysis for 1993–2022, we first analyze winter persistence of SST and key atmospheric drivers and identify East Korea Bay and the Subpolar Front as hotspots of long-lived SST anomalies. A rank-reduced multivariate maximum covariance analysis then extracts the leading coupled mode between SST and a set of atmospheric fields under positive and negative AO phases; in both phases the coupled mode is front-anchored, but its amplitude and spatial focus differ. Finally, to quantify the mixed-layer memory, we construct Ornstein–Uhlenbeck-like time-integrated responses of the atmospheric principal components. The effective integration timescales, determined by maximizing zero-lag correlations with the SST mode, cluster at approximately 2–3 weeks for wind-stress curl and near-surface variables and 4–7 weeks for sea-level pressure and meridional wind, with longer timescales during negative AO. The time-integrated atmospheric responses exhibit SST-like persistence, confirming the mixed layer’s role as a stochastic integrator. These AO-conditioned memory windows define practical lead times over which integrated atmospheric indices can act as predictors of winter marine heatwaves and cold-surge-impacted SST anomalies. Full article