Search Results (554)

The Arabian Gulf (Persian Gulf) and the Gulf of Oman are two basins, physically linked but comprising different hydrodynamic features. The shape of the basins, the weather, and the exchange through the Strait of Hormuz all affect circulation in the system. This review synthesizes current knowledge of hydrodynamic processes leading to water circulation in both basins, including wind forcing, density-driven exchange, tidal dynamics, and mesoscale changes. The Arabian Gulf has shallow depths, intense evaporation, and limited exchange, which promote hypersaline conditions, long residence times, and greater sensitivity to environmental stress. In comparison, the Gulf of Oman has a deeper, more exposed system, governed by monsoon-driven circulation, upwelling, and mesoscale processes. The present review highlights how various hydrodynamic regimes influence stratification, changes in temperature and salinity, and exchange processes, with direct impacts on nutrient transfer, oxygen delivery, and ecosystem dynamics. An important control point, the Strait of Hormuz, connects the Arabian Gulf and the Gulf of Oman. This review also stresses the importance of observational data and modeling capacity, as well as their limitations, for subsurface processes and connections across scales. It also provides an inclusive assessment of hydrodynamics and water circulation in the two basins. Moreover, it underscores the need for coordinated observational and modeling approaches to improve understanding and enhance management of these globally sensitive aquatic systems. Full article

Night-time low temperature remains a major constraint on thermal stability, crop safety and energy-efficient operation in winter solar greenhouses, especially when heat release and auxiliary heating are triggered only after the indoor temperature has approached a low temperature threshold. This study developed a temperature prediction-based active–passive heat storage and release system integrating Internet of Things monitoring, liquid neural network (LNN)-based multi-horizon temperature forecasting, heat storage and release circulation, and decision-making control. The LNN achieved the best forecasting performance among the tested models, with MAE/RMSE values of 0.620/0.775, 0.683/0.854 and 0.758/0.948 °C for 12 h, 24 h and 48 h forecasts, respectively, and was embedded into the system for prediction-assisted operation. A continuous 30-day winter test was conducted in two consecutive stages: heat storage and release without predictive control (HS-NPC, days 1–15) and with prediction-assisted operation (HS-PC, days 16–30). During the consecutive-stage winter test, HS-PC showed higher daily minimum indoor temperature and night-time mean temperature than HS-NPC by 1.92 °C and 4.29 °C, respectively, while reducing daily exposure below 13 °C and 10 °C by 55.0% and 98.2%. Stage-based equivalent input-energy evaluation indicated reductions of 17.1% and 34.3% for HS-NPC and HS-PC relative to the corresponding TG reference periods, respectively. Because HS-NPC and HS-PC were tested in consecutive weather windows rather than in fully synchronized parallel experiments, these improvements should be interpreted as stage-based operational benefits supported by the TG reference and outdoor environmental statistics, rather than as completely weather-independent causal effects. These results indicate that integrating temperature forecasting with heat storage and release regulation can improve low-temperature buffering and energy-saving operation in winter solar greenhouses, while further synchronized or weather-normalized validation is still needed. Full article

The rapid melting of Arctic sea ice has significantly lengthened the window for Northeast Passage navigation, but the frequent occurrence of accompanying extreme weather events poses severe challenges to shipping safety. Based on 1991–2020 climate data and 2021–2024 NCEP reanalysis data, this study uses wave activity flux diagnosis, composite analysis and statistical test methods to reveal the causes of abnormal circulation and the energy propagation mechanism of heavy precipitation events during the navigation period (July–October) in the starting section of the Arctic Northeast Passage (from the Barents to the Kara Sea). The results show that from 2021 to 2024, there was a high proportion of heavy precipitation events during the navigation period (July–October), with significant temporal and spatial variability; abnormal circulation is triggered by the synergistic effect of the eastward shift in the Ural blocking high and the southward extension of the Arctic polar vortex. The enhanced upper-level westerly jet and the mid-level “tripole-type” teleconnection wave drive jointly drive the northward transport of warm, moist air, and the low-level cyclonic circulation and upper-level divergence trigger a baroclinic lifting mechanism. Rossby wave energy originates from the Mediterranean–Black Sea region, propagating eastward to the study area along the jet axis and enhancing the ascending motion through wave activity flux divergence. The heavy precipitation event in August 2023 is a typical example of the cross-seasonal synergistic sea temperature–sea ice–atmosphere effect. This study reveals the following complete teleconnection chain: “sea temperature anomaly → wave train excitation → sea ice feedback → circulation maintenance”, which will support predicting disastrous weather and developing climate adaptation strategies in the Arctic Passage. Full article

Wind influences human activities both directly and indirectly. Directly, it affects, among others, transportation and wind energy systems through its direction and intensity, while extreme wind events can cause severe damage to infrastructure and buildings and even casualties. Indirectly, the movement of air masses is strictly associated with all meteorological phenomena, highlighting the crucial role of wind in shaping weather conditions. In the context of climate change, anomalies in global and regional circulation patterns modify the characteristics of surface winds. Consequently, investigating long-term wind variability and trends is essential for assessing climate change impacts on the environment and society. The climatology of near-surface (10 m) winds over Greece for the period 1991–2020 is examined using a high-resolution regional reanalysis dataset, focusing on the mean wind speed, mean daily maximum wind gust, and the frequency of strong-wind days. The results reveal substantial spatial and temporal variability, with the most pronounced upward trends of these parameters observed over the Aegean Sea and northeastern Greece. Statistically significant trends are detected mainly during winter and summer. In particular, January and August exhibit the strongest positive trends, locally exceeding 0.05 m s⁻¹ per year for mean wind speed and 0.1 m s⁻¹ per year for mean daily maximum wind gust. Moreover, the frequency of strong-wind days increases in several regions with local trends exceeding 0.2 days per year. These findings highlight the value of high-resolution regional reanalyses for characterizing near-surface wind variability and trends over areas of complex terrain. Full article

Artificial intelligence (AI) weather models achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their reliability for high-impact extremes remains largely uncharacterized. We present an event-based diagnostic evaluation of Aurora, a deterministic AI model, across 16 case studies chosen for physical diversity rather than statistical representativeness, spanning tropical cyclones (TCs), freezes, heatwaves, atmospheric rivers (ARs), and extreme precipitation at lead times from 1 to 21 days. Aurora showed strong short-range (1–7 day) skill: TC track and landfall positions were accurate for well-behaved systems, temperature extremes achieved high spatial agreement, and the atmospheric river structure was reproduced faithfully. This study’s central finding is a pattern–amplitude divergence: beyond 7 days, large-scale circulation patterns remained moderately skillful even as surface amplitudes weakened toward climatological values. Event-specific failures include a severe recurvature forecast failure for Hinnamnor, TC intensity biases, and a pronounced in-sample versus out-of-sample precipitation skill gap that is substantially confounded by event-type differences (large-scale monsoon vs. mesoscale-convective/cutoff-low regimes), so the gap cannot be attributed to training-period recency alone. Across the events examined here, Aurora provides reliable deterministic guidance within 7 days. We recommend deploying Aurora as a rapid ensemble generation and regime-identification tool alongside physics-based numerical weather prediction at short-to-medium range, with its directional intensity and amplitude biases corrected through post-processing before standalone use in operational warnings. Full article

In the campus planning of severe cold regions, indoor pedestrian corridors have emerged as an important strategy for mitigating harsh weather and maintaining pedestrian network continuity. These enclosed systems provide thermal comfort and physical convenience for faculty and students. However, reduced visual contact with outdoor natural environments may be associated with less favorable affective experiences. Existing research primarily emphasizes functional connectivity and spatial efficiency, with limited quantitative evidence on the visual characteristics of indoor corridors and their relationship with emotional perception. Drawing upon environmental psychology, this study examines the associations between corridor visual characteristics and five AI-predicted affective perception dimensions. Using deep-learning-based computer vision, semantic segmentation, and statistical analysis, environmental features and predicted perception scores were analyzed. A local human validation showed moderate overall agreement between human consensus ratings and AI-predicted scores, supporting their use as exploratory perception proxies. The findings indicate that indoor-corridor-related visual features were negatively associated with predicted beauty scores and positively associated with predicted depression scores, while predicted boringness was positively associated with wall enclosure. Predicted safety and liveliness showed weaker associations with individual visual features. These results provide exploratory evidence for balancing thermal protection, circulation efficiency, and psychological comfort in future campus corridor design. Full article

High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century. Full article

Hailstorms are uncommon over southern China but can cause severe damages during their occurrences. This paper analyzes three recent hail events over the Pearl River Estuary associated with different types of thunderstorm cells, namely, a supercell, a squall line, and a rapidly developing deep-layered single cell. The analysis is based on three-dimensional (3-D) wind fields retrieved from a network of five Doppler weather radars over the region using the Pythonic Direct Data Assimilation algorithm. The three cases took place under rather different synoptic and shear environments but were all favorable for severe updrafts that supported hail development. Retrieved peak updrafts exceeded 30 m/s in the supercell case and reached approximately 18 m/s in the single-cell case. The mechanisms of hail maintenance and deposition in these systems are clearly depicted through 3-D structural analysis of the thunderstorms, essentially the wind field, for their development in multiple time steps. For the first time, the 3-D wind field is also compared with observations from a satellite-borne radar, showing consistent evidence of intense updrafts and convective development reaching approximately 15 km above sea level; isolated hail was observed to descend along the downdraft flank of the cell. The evolution the 3-D structure of these convective systems, in particular the vertical velocity field, provides valuable information for nowcasting of the development of hailstorms. Full article

The Yishu Fault Zone (YSFZ) is located in a key tectonic transition zone shaped by the interaction between the Pacific and Tethyan tectonic domains in eastern China. Despite sparse geothermal borehole coverage across this region, the deeply incised fault structures create favorable hydrogeological prerequisites for fault-mediated subsurface heat migration and hydrothermal fluid circulation. This study integrates hydrochemistry, multi-isotope tracing (δD, δ¹⁸O, ⁸⁷Sr/⁸⁶Sr, and δ³⁴S), multi-mineral equilibrium modeling, and silica–enthalpy mixing analysis to constrain the evolution process and genetic mechanism of geothermal groundwater in the middle segment of the YSFZ. The geothermal groundwater displays weakly alkaline to alkaline properties and in situ temperatures of 35.2~75.0 °C, which is characterized by the HCO₃–Na, SO₄–Na, and Cl·SO₄–Na type. Stable isotope signatures demonstrate that the geothermal groundwater is recharged by the atmospheric precipitation with elevations of 822~1274 m. The hydrochemical evolution of the geothermal waters is governed by silicate weathering, evaporite dissolution, and cation exchange. The ⁸⁷Sr/⁸⁶Sr ratios indicate mixed solute contributions from silicate and evaporite lithologies, whereas the δ³⁴S signatures suggest that SO₄²⁻ is predominantly derived from gypsum dissolution. Two distinct hydrochemical evolution patterns can be identified in the study area. Samples GG1 and GG5 are characterized by HCO₃⁻ enrichment, whereas GG2, GG3, and GG4 exhibit enrichment in Na⁺ and SO₄²⁻. Reservoir temperatures estimated using multi-mineral equilibrium geothermometry range from 56.7 °C to 92.1 °C, with circulation depths of 1648~2304 m and cold-water mixing ratios of 48%~69%. The results of this study provide geochemical evidence for hidden geothermal resource exploration in deep fault zones. Full article

Traditionally, fire danger is assessed using daily values of Fire Weather Index (FWI). The methodology used in this study consists of atmospheric modelling for a more accurate representation of the FWI. To this end, two simulations designed with two nested domains of horizontal resolution of 2500 m and 500 m were carried out covering two periods of active fires in Portugal: October 2017 (central region) and August 2018 (southern region). The study shows that high-resolution simulations are able to capture local circulations and accurately represent the diurnal cycle and fire management services can therefore overcome the limitations of a single daily value, leading to a more detailed assessment of fire danger, particularly in topographically complex regions or areas influenced by coastal dynamics. Full article

A significant low-level wind shear event that occurred at Xining Caojiabu Airport on 10 April 2019 was comprehensively analyzed. The analysis utilized data from the airport’s ground automatic weather observation system (AWOS), lidar detection data, ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and ETOPO2v2 topographic data from the National Oceanic and Atmospheric Administration (NOAA). The analysis focused on the evolution of meteorological elements during the wind shear, lidar characteristics, large-scale environmental features, and the main influencing systems. The results indicate that this was a typical “easterly backflow” low-level wind shear event, representing a special type of cold-frontal low-level wind shear, with the wind shear occurring in the prefrontal area as the cold front approached the airport. During the passage of the wind shear, the AWOS stations at Runways 29 and 11 sequentially recorded pressure increases and temperature decreases, reflecting the gradual intrusion of cold air from east to west into the airport. Lidar Plan Position Indicator (PPI), Range-Height Indicator (RHI), and Doppler Beam Swinging (DBS) modes revealed that the wind shear appeared as convergence between southeast and northwest winds, with an impact on the airport that moved from east to west and from bottom to top, belonging to a meso-γ-scale system. The evolution of the sea-level pressure field, pressure-change field, frontogenesis function, and temperature advection indicated that cold air first moved eastward along the Hexi Corridor and then poured back into the Huangshui River Valley through the topographic gap at the eastern end of the Qilian Mountains. The easterly wind converged with the westerly wind, and the topographic funneling effect strengthened the easterly backflow and promoted its westward advance, leading to the occurrence of low-level wind shear. The large-scale influencing systems of this event included a transverse trough over Mongolia at 500 hPa, an upper-level frontal zone, an upper-level jet stream, and a surface cold front. The favorable conditions for the formation of this “easterly backflow” low-level wind shear were the strengthening of baroclinicity in the upper-level frontal zone, intensified cold advection, momentum downward transport induced by the upper-level jet and ageostrophic secondary circulation, and the easterly backflow and wind speed enhancement caused by the special topography. Full article

With an increasing awareness of climate change and its effects on the built environment, climate change adaptation is changing traditional building design practices. Future weather data are essential for building energy simulation (BES) that informs a resilient and energy-efficient building design under climate change. While general circulation models (GCMs) provide future climate predictions, their outputs often require downscaling to improve spatial and temporal resolution and further methodological processing to generate weather data suitable for building-scale analysis. This study aims to examine the methods for generating and utilizing future weather data for BES, with a particular focus on bias correction and uncertainty quantification in GCM predictions. This study summarizes the prevailing methods for bias correction of GCM outputs and the generation of representative future weather data. The characterization of GCM uncertainty and its implications for BES results are discussed. It is shown that GCM outputs can be effectively used for BES to evaluate long-term effects of climate change under various climate scenarios, and the most cost-effective approach often involves a combination of statistical downscaling and adjustment of grid cell size, which balances the need for high-resolution, site-specific weather data with the demand for computational resources. In addition, key challenges are identified, including the selection of appropriate GCMs and climate scenarios, the trade-off between computational cost and representativeness, and the need to include both extreme and typical weather conditions. Furthermore, future research prospects are proposed. Through a synthesis of current advancements in future weather data generation methods, this study contributes to the robustness of climate-responsive building design. Full article

Tropospheric ozone (O₃) is a critical air pollutant that poses significant risks to human health and ecosystems. While previous studies have primarily focused on O₃ changes in Eastern China, limited attention has been given to Northwest China, where fragile but ecologically important systems may be vulnerable to O₃ pollution. The temporal evolution and driving mechanisms of surface O₃ in this region remain poorly understood. Using the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) datasets and simulations from the Community Atmosphere Model with Chemistry (CAM-Chem), we identified a significant increase in summer surface O₃ concentrations across Northwest China from 1980 to 2020, with the most pronounced rise occurring during 1993–2010. This period accounts for the majority of the long-term upward trend, despite relative declines before and after. The increase in O₃ during 1993–2010 is primarily attributed to rising surface temperatures, which reduce hydroperoxyl radical (HO₂) concentrations and enhance nitrogen dioxide (NO₂) production, leading to elevated nitrogen oxides (NO_x) levels and promoting O₃ formation. The warming trend is closely associated with a concurrent decrease in low cloud cover, which increases surface shortwave radiation and further contributes to surface warming. Further investigation reveals that warming sea surface temperature (SST) in the North Pacific influence atmospheric circulation through wave train processes, amplifying the regional geopotential height field. These circulation changes reinforce the reduction in low cloud cover and the associated increases in surface temperature and O₃ concentrations over Northwest China. The decadal variability of North Pacific SST may therefore serve as an important indicator of long-term surface ozone variability in this region. Full article

Atmospheric Motion Vectors (AMVs) from geostationary satellites are a critical observational source for data assimilation, particularly in regions with sparse observations, such as the Southern Hemisphere. This study evaluates the impact of assimilating AMVs from the Geostationary Operational Environmental Satellite (GOES) series into the Numerical Modeling and Assimilation System (SMNA) used at the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE). The SMNA consists of the Brazilian Global Atmospheric Model (BAM) coupled with the Gridpoint Statistical Interpolation (GSI) data assimilation system. Two experiments were conducted in February 2024: a control experiment that assimilated all conventional observations along with AMVs from GOES-16 and GOES-18 satellites, and a second experiment (data denial), in which the AMVs were excluded. This time period coincided with the formation of the tropical cyclone Akará offshore the southeast coast of Brazil. The diagnostic analysis of the assimilation process indicates a substantial increase in the relative contribution of wind observations to the cost function and a reduction in the differences between the background and the analysis, particularly in the mid and upper troposphere. Forecast verification showed that assimilating AMV data led to a reduction in RMSE and an increase in anomaly correlations for several variables, including wind and temperature at various vertical levels. The positive impact of GOES AMV data on the representation of the tropical cyclone Akará is evident in the improved positioning, intensity, and circulation structure of the cyclone, particularly during its intensification phase. With tropical cyclone events over South America becoming more frequent in recent years, results from this study indicate the critical need to assimilate AMV data to improve forecast skill. Furthermore, the assimilation of GOES AMVs significantly enhances the representation of atmospheric circulation over South America, particularly improving the predictability of large-scale events such as cyclones in the South Atlantic. Full article

The Northeast China Cold Vortex (NECV) is a key circulation system affecting weather patterns over North China, frequently triggering thunderstorms, hail, and other severe convective weather. Accurate prediction of NECVs is therefore of great importance. However, substantial forecast errors still remain, largely due to uncertainties in the initial conditions. To improve NECV forecast skills, we investigate the optimal initial errors and targeted observation sensitive areas using a sampling-based approximation of the conditional nonlinear optimal perturbation (CNOP) method together with the Pangu-Weather deep learning model. We first evaluate the model’s performance over Northeast China and find that Pangu-Weather exhibits forecast skill generally comparable to the ECMWF Integrated Forecasting System (IFS) during the May–August 2022 period over Northeast China. Then the CNOP-based approach is used to capture the optimal initial errors with the greatest impact on NECV forecasts. The largest error amplitudes are primarily located upstream of the vortex and near upper-level jet-entrance regions, which are identified as the targeted observation sensitive areas. Perturbation kinetic-energy diagnostics further indicate that baroclinic conversion is the dominant mechanism for error growth. Observing system simulation experiments suggest that, under an idealized assumption of completely eliminating errors in a given region, targeted observations over the sensitive area can produce the largest forecast improvement, with an average error reduction of approximately 13% relative to other areas. This study contributes to a deeper understanding of NECV predictability and may help improve forecasting capability. Full article