Search Results (308)

The northeast monsoon prevailing over southeastern China in late seasons, generally from October to March, frequently generates water level anomalies upstream of the Taiwan Strait (TWS) that reach the coastal waters of Guangdong in South China, and, with compounding astronomical high tides, elevate coastal flood risk over the region. The risk of coastal flooding or sea inundation is further heightened when monsoon forcing co-occurs with storm surge brought by late-season tropical cyclones (TCs). This study integrates tide gauge observations from Hong Kong (HK) and its vicinity together with Delft3D Flexible Mesh simulations to diagnose a tide-modulated anomaly wave mechanism. Observations show that anomalies originating in or near TWS arrive in HK with station-dependent phasing. These water level anomalies exhibit a characteristic ~6 h periodicity west of the Taiwan Shoal, and display peaks that systematically align with the astronomical high tide. Time–frequency analysis reveals a wave period transformation from ~12 h north of Dongshandao over the coast of southeastern China to ~6 h west of the Taiwan Shoal. We test the hypothesis that wind-forced water anomalies generated in or near TWS undergo shoal-modulated nonlinear tide–wind interaction and tidal-current advection that transform their dominant period and phase-lock them to the tide, producing four anomaly peaks per day downstream and station-dependent phasing in HK. Hindcasts of the November 2024 monsoon episode reproduce the observed timing, periodicity, and spatial transition, while constituent experiments demonstrate that semi-diurnal forcing entering via the TWS is the primary driver of the ~6 h signal, with the Taiwan Shoal acting as the modulation locus. Accurate water level forecasts for the Guangdong coast, therefore, need to incorporate upstream wind forcing over the TWS and bathymetric controls around the Taiwan Shoal, with practical implications for compound flood risk during spring tides and co-occurring monsoon and/or TC events. Full article

This study investigates the temporal and spatial variations in sea level anomaly (SLA) and sea surface wind in the East China Sea (ECS) from 1993 to 2021 using AVISO altimetry data and ERA5 reanalysis wind data. Empirical Orthogonal Function (EOF) and trend analyses were applied to identify dominant modes and long-term changes. Results reveal pronounced seasonal SLA variability, with lower levels in winter/spring and higher levels in summer/autumn, strongly modulated by monsoon winds. The first EOF mode of SLA accounted for 52.73% of variance, showing basin-coherent seasonal fluctuations, while the second mode (7.79%) reflected contrasts between coastal and Kuroshio-influenced regions. The ECS experienced an average sea level rise of 3.77 mm/year, exceeding 6 mm/year along the Jiangsu and Zhejiang–Fujian coasts. Sea surface wind stress variability was greatest in the northern Taiwan Strait and southwest of the Ryukyu Islands, but decreased along the Zhejiang coast. Sea level anomalies (SLAs) in the East China Sea exhibit clear multi-scale coupling with the wind field. The seasonal SLA variability in the East China Sea is jointly modulated by local Ekman forcing due to wind stress, while also being potentially linked to the Kuroshio and open-ocean Rossby waves. These findings underscore the role of wind forcing in regional sea level changes and provide insight for coastal management under climate change. Full article

The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of −0.0014 °C yr⁻¹ during 1982–2009 transitioned to a pronounced increasing trend of 0.0057 °C yr⁻¹ from 2010–2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Niño–Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air–sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean–atmosphere interactions. Full article

Tropical estuarine hydrodynamic processes are governed by complex interactions between tides, monsoons, and fluvial runoff. To obtain long-term (≥30 years) hydrodynamic conditions of the Mekong River Estuary, this study established a Finite Volume Coastal Ocean Model (FVCOM) coupled with validated Weather Research and Forecast (WRF) wind forcing for a 32-year (1988–2019) high-resolution simulation. Validation against in situ observations confirms the model’s robustness. Temporal–spatial patterns of water level and current were analyzed, and extreme parameters for 1–100 year return periods were derived via the Pearson-III probability distribution. Results indicate the study area is a mesotidal environment (tidal range = 3.58 m) dominated by SSE-NNW reciprocating tidal currents. Relative to Vietnam’s national elevation datum, 100-year return period extreme high/low water levels are 2.15 m and −2.03 m, with a maximum storm surge setup of 2.09 m. The 100-year return period maximum current velocity reaches 4.58 m/s (A21 station), and Mekong River runoff exerts a negligible influence (<5% velocity change). This study provides high-precision baseline data for offshore wind farm engineering and disaster risk assessment, offering a methodological reference for tropical estuarine hydrodynamic simulations. Full article

Atmospheric aerosols in Southeast Asia, influenced by climate and seasonal circulation, are examined here. This study analyzes the impact of the 2015 El Niño and monsoonal variability on aerosol properties over Penang, Malaysia, from 2015–2019. Aerosol Optical Depth (AOD), Ångström Exponent (AE), Fine Mode Fraction (FMF), and Single Scattering Albedo (SSA) were analyzed using AERONET observations, complemented by satellite-derived fire data and NOAA HYSPLIT back-trajectory analysis. Pronounced seasonal variability was observed, with elevated AOD during the Southwest Monsoon (0.72 ± 0.15) associated with biomass burning and mixed urban aerosols, and lower AOD during the Northeast Monsoon (0.47 ± 0.12) due to cleaner maritime air masses. The inter-monsoon period exhibited the lowest AOD (0.28 ± 0.10), reflecting enhanced wet scavenging and mixed aerosol sources. Interannually, the 2015 El Niño recorded substantially higher aerosol loading, including extreme AOD events (>1.75), driven by intensified regional fire activity under dry conditions. A statistically significant but weak correlation (R² = 0.12, p = 0.047) indicates biomass burning contributed to AOD, though transport processes were the dominant driver. Trajectory analysis confirmed that aerosols originated from fire-affected Sumatra during the Southwest Monsoon and from the South China Sea during the Northeast Monsoon. These results show that climate and winds drive aerosol changes, so regional monitoring and cross-border air management in Southeast Asia are needed. Full article

The residual current is the ocean current after the tidal component has been removed. Understanding the spatiotemporal distribution characteristics of sea surface residual currents is key to revealing the local current field evolution and typical physical oceanographic processes. The Taiwan Strait is in the East Asian monsoon region, where residual currents are significantly influenced by monsoons during periods of high wind speeds. However, the characteristics and dynamic mechanisms of residual currents under low wind speed conditions (≤5 m/s) remain unclear. Based on high-frequency surface wave radar current data and wind field reanalysis data, this study analyzed the characteristics of residual currents in the southwestern Taiwan Strait under low wind speed conditions, focusing on two orthogonal directions: cross-shore and along-shore. During these periods, residual currents exhibit counter-wind current characteristics. These currents cross the Taiwan Bank and generate wave signals with wavelengths ranging from 35.6 km to 65.8 km and durations of 6 to 12 h in the Xiapeng Depression area. These fluctuations are triggered by the combined timing of low winds and nonlinear current–topography interactions. In terms of dynamic mechanisms, the Coriolis force term and the acceleration term dominate the momentum equations in both two orthogonal directions, indicating that the current field is in a non-steady inertial adjustment phase during this period. Furthermore, this study constructs a two-layer ocean model of rotationally modified gravity waves to analyze the influences of topography, oceanic stratification, and steady current velocity on the characteristics of residual current fluctuations under low wind speed conditions. The theoretical model yields spatial scales that closely match the observed wavelength characteristics. Full article

In the Yingjiang area of western Yunnan, precipitation is high throughout the year, making it one of the regions with the highest annual precipitation in mainland China. Extreme rainfall in this region often triggers severe flooding, yet the key mechanism of water vapor transport underlying abnormally heavy precipitation remains unclear. This study used automatic weather station observations of precipitation, the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts, and Global Data Assimilation System (GDAS) data to analyze, for the first time, large-scale water vapor transport, precipitation mechanisms, and the primary water vapor sources and their contributions in this region. The results show the following: In the Yingjiang area, the water vapor sources at all height levels in summer are dominated by the southwest monsoon water vapor transport pathways, such as the Bay of Bengal and the Arabian Sea, with their total contributions to specific humidity and water vapor flux exceeding 70%. This indicates that low-latitude sea areas such as the Bay of Bengal and the Arabian Sea serve as key moisture source regions for Yingjiang in the global water vapor cycle. Water vapor transport over the windward slope causes strong low-level convergence and high-level divergence phenomena, and the suction effect leads to strong upward motion near the 850 hPa level. The pseudo-equivalent potential temperature isolines tilt along the mountain slope, maintaining an unstable stratification characterized by warm, humid lower layers and cold, dry upper layers, providing favorable thermal conditions for precipitation. In addition, in the summer of 2020, abnormally high southwest seasonal wind and air transport, combined with strong low-level convergence and high-level divergence of the vertical circulation structure, were key factors causing the abnormally high precipitation. This study provides an important reference for the prediction of extreme precipitation and the early warning of rainstorm disasters in the southwest monsoon region in the context of global climate change. Full article

Climate change-induced monsoon variability increasingly threatens the economic viability of renewable energy systems in Southeast Asia. While solar–wind hybrid systems are considered a promising solution, their economic resilience under dynamic monsoon conditions remains poorly understood—a critical research gap for climate-adaptive energy planning in monsoon-affected regions. This study aims to develop an integrated climate–technology–economics framework to assess the economic resilience of solar–wind hybrid systems under projected monsoon variability. The framework combines ERA5 reanalysis data, CMIP6 climate projections, techno-economic optimization via HOMER Pro, and a quantitative resilience assessment covering resistance (ΔLCOE%), robustness (CV~NPV~), and adaptive potential. The methodology is applied to representative ASEAN regions—Vietnam, Thailand, the Philippines, and Indonesia—to evaluate how monsoon-induced changes in solar and wind resources affect system performance. Results indicate that intensified monsoon variability reduces photovoltaic output during the rainy season by up to 15%, increases the levelized cost of energy (LCOE) by an average of 12.5%, and extends project payback periods by 2–4 years. Inland areas exhibit significantly higher vulnerability than coastal regions. However, optimized system configurations—particularly adjustments to the solar–wind capacity ratio and integration of battery energy storage—improve economic resilience by more than 20%. These findings provide quantitative evidence and actionable guidance for climate-resilient renewable energy planning in monsoon-affected ASEAN countries. Full article

Clouds are essential for regulating the hydrological cycle and Earth’s radiation budget, and their fluctuations over the Tibetan Plateau (TP) have a significant effect on both regional climate dynamics and global atmospheric circulation. Using ground-based Vaisala CL51 ceilometer data and Fengyun-4A (FY-4A) satellite observations from October 2020 to June 2022, this study examines cloud occurrence frequency (COF), cloud vertical structure (including cloud base height (CBH), cloud top height (CTH), and cloud layer stratification), and related macroscopic properties over the Large High Altitude Air Shower Observatory (LHAASO). CL51 and FY-4A had cloud occurrence rates of 43.7% and 37.7%, respectively, over the observation period, with a strong correlation coefficient of 0.82. Given the impact of clouds on Cherenkov light observations by the LHAASO Wide Field of view Cherenkov Telescope Array (WFCTA), we specifically evaluated the cloud occurrence during the operational periods of the LHAASO-WFCTA, finding rates of 34.2% (CL51) and 28.0% (FY-4A), with the lowest rates occurring in the early morning. Due to monsoonal moisture inflow and dry northeasterly winds, seasonal COF changes showed clear peaks in summer (78.8%) and minima in winter (24.8%). Seasonal differences existed in the diurnal COF patterns, with nocturnal prominence in summer/autumn and daytime dominance in spring/winter. The CBH showed daily oscillations, peaking at 18:00 (local solar time) and troughing at 08:00 (local solar time), with seasonal CBH minima in summer/autumn and maxima in spring/winter. Low- and mid-level clouds predominated, with clear diurnal cycles: low- and mid-level clouds rose from morning until midday, while high-level clouds appeared after dusk. Vertical cloud structures were predominantly single-layered (81%), with multi-layered complexity peaking in the summer due to convective activity. The CTH distributions showed unimodal patterns in the fall and winter (1.5–3 km), while in the summer, they showed multimodal extents (up to 12 km). These results improve LHAASO-WFCTA observational scheduling, enhance climate model parameterizations, and deepen our understanding of the dynamics of the TP cloud. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

Cities in India experience distinct seasons, including summer, winter and monsoons. the understanding of thermal comfort within modern houses throughout the different seasons is pivotal for determining a passive design strategy for residences, towards carbon neutrality. Long-term investigations were conducted within five typical houses in the warm–humid climate of Kharagpur, India, spanning three seasons from July 2023 to July 2024. These included air temperature (AT), relative humidity (RH), indoor wind speed and globe temperature for calculating standard effective temperature (SET*). The SET* was used in thermal comfort evaluation, focusing on the cooling effects of elevated wind speeds. The results showed that indoor ATs were well stabilized among the houses, ranging from 27 to 32 °C in monsoon, 20 to 23 °C in winter and 30 to 32 °C in summer on average, due to the effects of high thermal mass structure with relatively small openings. Overall, both the house-wise differences (1–2 °C) and diurnal differences (0.5–3 °C) were much smaller than the seasonal differences. It was found that the resultant indoor operative temperatures (OTs) did not fall within the required comfort levels during the summer and monsoons, whereas those of the winter months met the required standard. The current modern Indian houses of high thermal mass structure prevented flexible adaptations to the dynamic seasonal changes as well as changes within a day. The occupants tended to reduce the SET* by increasing the wind speeds with the assistance of mechanical air circulation, thus reducing the perceived AT by 5 °C in summers. Separate design strategies should be adopted seasonally and in different parts of the day, to maintain a thermally comfortable environment for the occupants. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. 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

Ocean winds and waves play a vital role in maritime navigation safety, offshore operations, and coastal zone dynamics. Although both factors have been widely studied individually, the joint characterization of wind and wave events remains limited in the North Indian Ocean. This study, utilizing ERA5 reanalysis data from 1980 to 2022, statistically analyzed the distribution and variation patterns of both wind speed and significant wave height, investigating the occurrence, affected area proportion, frequency, and intensity of SBLWEs. To understand the cause of Strong Breeze and Large Wave Events (SBLWEs), their connections with other phenomena, such as tropical cyclones, were also explored. The results show that regions with strong breezes and large waves are mainly concentrated in the central and western Arabian Sea near Africa and the central and western Bay of Bengal. Monthly averages indicate that wind and wave intensity are much higher during the summer monsoon than in other seasons, with high intensity, probability, and extensive affected areas of SBLWEs. The occurrence probability of SBLWEs is highest in the central and western Arabian Sea (up to ~40%), and the highest probability in the Bay of Bengal is about 20% near the eastern coast of Sri Lanka. The peak period of SBLWEs occurs from June to August, with the largest affected area in July, reaching almost 25%. Over the past 40 years, the number of SBLWEs has shown an increasing trend, with an average of 0.7 events annually. The intensity distribution of SBLWEs resembles that of wind speed and wave height, with the highest intensity areas concentrated in the Bay of Bengal, affected by tropical cyclones. This study can serve as a scientific reference for maritime route planning and offshore operations, helping to reduce the negative impacts of large wind and wave events and enhance navigation safety. 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