Search Results (291)

Apiculture is among the most weather-dependent sectors of agriculture; however, quantifying the impact of meteorological factors remains challenging. Beehive weight has long been recognized as an important indicator of colony health, strength, and food availability, as well as foraging activity. Atmospheric influences on hive weight dynamics have been a subject of research since the early 20th century. This study aims to estimate hourly hive weight variation by applying linear time-series models to hive weight data collected from active apiaries during intensive foraging periods, considering atmospheric predictors. We employed a rolling 24 h forward ARIMAX and SARIMAX model, incorporating meteorological variables as exogenous factors. The median estimates for the study period resulted in model RMSE values of 0.1 and 0.3 kg/h. From numerous meteorological variables, the hourly maximum temperature was found to be the most significant predictor. ARIMAX model results also exhibited a strong diurnal cycle, pointing out the weather-driven seasonality of hive weight variations. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Due to its persistence and potential negative effects on ecosystems and human health, microplastic pollution in aquatic environments has become a major worldwide concern. Photocatalytic degradation is a sustainable manner to degrade microplastics to non-toxic by-products. In this review, comprehensive discussion focuses on the synergistic effects of various photocatalytic materials including TiO₂, ZnO, WO₃, graphene oxide, and metal–organic frameworks for producing heterojunctions and involving multidimensional nanostructures. Such mechanisms can include the generation of reactive oxygen species and polymer chain scission, which can lead to microplastic breakdown and mineralization. The advancements of material modifications in the (nano)structure of photocatalysts, doping, and heterojunction formation methods to promote UV and visible light-driven photocatalytic activity is discussed in this paper. Reactor designs, operational parameters, and scalability for practical applications are also reviewed. Photocatalytic systems have shown a lot of development but are hampered by shortcomings which include a lack of complete mineralization and production of intermediary secondary products; variability in performance due to the fluctuation in the intensity of solar light, limited UV light, and environmental conditions such as weather and the diurnal cycle. Future research involving multifunctional, environmentally benign photocatalytic techniques—e.g., doped composites or composite-based catalysts that involve adsorption, photocatalysis, and magnetic retrieval—are proposed to focus on the mechanism of utilizing light effectively and the environmental safety, which are necessary for successful operational and industrial-scale remediation. Full article

This study investigates the occurrence and distribution of geomagnetic pulsations (Pc2–Pc5) over South America during 2014, analyzing their dependence on magnetic latitude, local time, and geomagnetic activity. Geomagnetic field data were obtained from the Embrace magnetometer network, which spans Brazil and Argentina and includes regions influenced by the Equatorial Electrojet (EEJ) and the South Atlantic Magnetic Anomaly (SAMA). Both continuous and discrete wavelet transforms (CWT and DWT) were employed to analyze non-stationary signals and reconstruct pulsation activity during quiet and disturbed geomagnetic periods. The results reveal that Pc5 and Pc3 pulsations exhibit a pronounced diurnal peak around local noon, with significantly stronger and more widespread activity under storm conditions. Spatial analyses highlight localized enhancements near the dip equator during quiet times and broader latitudinal spread during geomagnetic disturbances. These findings underscore the strong modulation of pulsation activity by geomagnetic conditions and offer new insights into wave behavior at low and mid-latitudes. This work contributes to understanding magnetosphere–ionosphere coupling and has implications for space weather prediction and geomagnetically induced current (GIC) risk assessment in the South American sector. Full article

Understanding optical turbulence within the atmospheric boundary layer (ABL) is essential for refining atmospheric motion analyses, enhancing numerical weather prediction models, and improving light propagation assessments. This study develops an optical turbulence model for the boundary layer over the South China Sea (SCS) by integrating multiple observational and reanalysis datasets, including ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF), radiosonde observations, coherent Doppler wind lidar (CDWL), and ultrasonic anemometer (CSAT3) measurements. Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. Moreover, this study demonstrates the feasibility of employing ERA5-derived temperature and pressure profiles as alternative inputs for optical turbulence modeling while leveraging CDWL’s high-resolution observational capacity for all-weather turbulence characterization. A comprehensive statistical analysis of the atmospheric refractive index structure constant ($C_n^2$) from November 2019 to September 2020 highlights its critical implications for optoelectronic system optimization and astronomical observatory site selection in the SCS region. Full article

Reducing anthropogenic fossil fuel CO₂ (FFCO₂) emissions in urban areas is key to mitigating climate change. To better understand the spatial characteristics and temporal variations in urban CO₂ levels in the Beijing (BJ) region, we conducted a long-term CO₂ simulation study by using the Weather Research and Forecasting WRF-Chem model and CO₂ observation data. To assess the model performance, three representative sites with high-precision CO₂ observation data were chosen in this study: the rural regional background Shangdianzi (SDZ) site, the suburban Xianghe (XH) site, and the urban BJ site. The simulation results generally captured the observed variations at these three sites, but the model performed much better at the SDZ and XH sites, with mean biases of −0.7 ppm and −2.3 ppm, respectively, and RMSE of 12.3 ppm and 21.4 ppm, respectively. The diurnal variations in the model results agreed well with those in the observed CO₂ concentrations at the SDZ and XH sites during all seasons. In the meanwhile, the diurnal variations in the modeled FFCO₂ were similar to those in the CO₂ observation with a positive bias at the BJ site, which may have been caused by higher emissions especially in winter. Moreover, both the modeled FFCO₂ and biospheric CO₂ (BIOCO₂) have positive correlations with the observed CO₂ concentration, whereas the planetary boundary layer height (PBLH) and observed CO₂ concentration exhibited negative correlations at all sites. In addition, the contributions of FFCO₂ and BIOCO₂ to CO₂ varies depending on the seasons and the location of sites. Full article

Vertically developing convective clouds (VDCCs), characterized by cloud-top ascent and cooling, are critical precursors to severe convective weather due to their association with intense updrafts. However, existing studies are constrained by limited spatiotemporal resolution of data and tracking methodologies, hindering real-time and pixel-level capture of VDCC evolution. Furthermore, large-scale statistical analyses of VDCC spatiotemporal distribution remain scarce compared with mature convective systems, particularly in topographically complex regions like the Tibetan Plateau (TP). To address these challenges, we integrated an optical flow algorithm (for dense atmospheric motion vector (AMV) retrieval) with cloud-top cooling rates (CTCRs, as indicators of vertical development), leveraging the high spatiotemporal resolution and multispectral capabilities of the GEO-KOMPSAT-2A (GK2A) satellite. This approach achieved pixel-level VDCC detection at 10 min intervals across diurnal cycles, enabling comprehensive statistical analysis. Based on this technical foundation, the most important finding in the study was the distinct convective spatiotemporal distribution over the TP and East Asia (EA) by analyzing VDCC detection data in three summers (2021–2023). Specifically, VDCC diurnal peaks preceded precipitation by 2–3 h, confirming their precursor roles in both study regions. Regional comparisons revealed that topographic and thermal forcing strongly influenced VDCC distribution patterns. The TP exhibited earlier and more frequent daytime convection at middle-to-low levels than EA, driven by intense thermal forcing, yet vertical development was limited by moisture scarcity. In contrast, EA’s monsoonal moisture sustained deeper convection, with more VDCCs penetrating the upper troposphere. The detection and statistical studies of VDCCs offer new insights into convective processes over the TP and surrounding regions, offering potential improvements in severe weather monitoring and early warning systems. Full article

The aim of this study was to expand the monitoring network and evaluate the accuracy of inexpensive WoMaster ES-104 sensors for monitoring particulate matter (PM) in temperate latitudes, using the example of the Southern Baikal region. The research methods included continuous measurements of PM_2.5 and PM₁₀ concentrations, temperature, and humidity at three stations (Listvyanka, Patrony, and Tankhoy) from October 2023 to October 2024, using the LCS WoMaster ES-104. ERA5-Land reanalysis data and the HYSPLIT model were used to analyze meteorological conditions and air mass trajectories. The results of this study showed a high correlation between the WoMaster ES-104 and the DustTrak 8533; the correlation coefficient was 0.94 (R² = 0.85) for both fractions. The seasonal dynamics of PM_2.5 and PM₁₀ were characterized by a dual-mode distribution with maxima in summer (secondary aerosols, high humidity) and winter (anthropogenic emissions, inversions). The diurnal cycles showed morning/evening peaks associated with transport activity and atmospheric stratification. Extreme concentrations were recorded in anticyclonal weather (weak north-westerly winds, stable atmosphere). This study confirms the suitability of the LCS WoMaster ES-104 for real-time monitoring of PM_2.5 and PM₁₀, which contributes to sustainable development by increasing the availability of air quality data for ecologically significant regions such as Lake Baikal. Full article

In order to explore the variation law and causes of the humidity effect of local water bodies, this paper selects the data of encrypted automatic weather stations (encrypted stations) and national conventional meteorological stations (conventional stations) in Nanjing from 2014 to 2020, and systematically studies the humidity effects and influencing factors of urban water bodies by constructing the humidity effect intensity (E) based on the conventional stations. The results show that the humidity effect of urban water has significant diurnal and monthly variation characteristics, and is extremely sensitive to temperature change, and compared to nighttime, the daytime period is generally more humid. The humidity effect is mostly normal in winter, while the humidification and humidity reduction effects in summer are particularly significant. There are also significant differences in the humidity effect between different typical water stations, which are mainly influenced by the background environment of urban and suburban areas, macro wind field, and local wind field configuration around the water body due to the dense building density in the main urban area, which is characterized by dry humidification, while the suburbs are characterized by humidity. When the water body is located on the side of a large water body (river or lake), the influence of local water–land wind field and macro wind field on the humidity effect is particularly significant, and the water wind will significantly enhance the humidity effect, while the land breeze will weaken the humidity effect. The research results can provide a reference for the urban planning and the design of the surrounding environment of water bodies in Nanjing. Full article

We model and describe the combined effect of a series of urban heat islands (UHIs), generated by nearby cities aligned as an archipelago, on the vertical diffusion of heat and the temperature structure in the lower atmosphere over an urban chain in northwestern Morocco. We use the Weather and Forecasting Model (WRF) coupled to an urban canopy model to run simulations during the northern summer. We show that when the land surface is characterized accurately, the WRF model can effectively resolve the scale of the urban archipelago effect and describe its detailed diurnal structure. Our results indicate that the combined effect of multiple UHIs in proximity is more impactful than the sum of their parts. Specifically, the urban archipelago’s effect alters the vertical temperature structure through upward diffusion of heat and extends its scale from local to meso-scale. This alters the wind pattern and may affect local weather conditions and air quality. These results underline the importance of considering the urban archipelago effect when studying urban climate. They also extend beyond academic research to offer valuable insights for urban planners in emphasizing the importance of urban typology and spatial proximity in city design and balancing cities’ interconnectivity with sustainable development and resilience. Full article

Total electron content (TEC) serves as a key parameter characterizing ionospheric conditions. Accurate prediction of TEC plays a crucial role in improving the precision of Global Navigation Satellite Systems (GNSS). However, existing research have predominantly emphasized spatial variations in the ionosphere, neglecting the periodic changes of the ionosphere with the diurnal cycle. In this paper, we propose a TEC prediction model, which simultaneously considers both spatial and temporal characteristics to extract spatiotemporal features of ionospheric distribution. Additionally, we integrate several space weather element datasets into the prediction model framework, allowing the generation of multiple space weather feature values that represent the influence of space weather on the ionosphere at different latitudes and longitudes. Moreover, we apply Gaussian process regression (GPR) interpolation to geomagnetic data to characterize impact on the ionosphere, thereby enhancing the prediction accuracy. We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). We also examined the effect of using different numbers of space weather feature values in these models. Our model outperforms the comparison models in terms of prediction error metrics, including mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), and the structural similarity index (SSIM). Furthermore, we analyzed the influence of different batch sizes on model training accuracy to find the best performance of each model. In addition, we investigated the model performance during geomagnetic quiet periods, where our model provided the most accurate predictions and demonstrates higher prediction accuracy in the equatorial anomaly region. We also analyzed the prediction performance of all models during space weather events. The results indicate that the proposed model is the least affected during geomagnetic storms and demonstrates superior prediction performance compared to other models. This study presents a more stable and high-performance spatiotemporal prediction model for TEC. Full article

Scrub mangrove forests, dominated by Rhizophora mangle L., are characterized by high porewater salinity, which might compromise individual sap flow rates (SF) due to seasonal and diurnal microenvironmental variations. We tested the functional, anatomical, and SF responses of 12 individuals to microenvironmental variables such as solar radiation, photosynthetic photon flux, wind speed, evaporative demand, and porewater salinity, measured using an in situ weather station. Measurements were made in the dry and rainy seasons in the Yucatan Peninsula, using Granier heat dissipation sensors, installed on tree branches. During the rainy season, SF was twice as high as that during the dry season (0.22 ± 0.00 L h⁻¹ and 0.11 ± 0.00 L h⁻¹, respectively), despite lower evaporative demand. In both seasons, negative relationships between SF with vapor pressure deficit (VPD; dry τ = −0.54; rainy τ = −0.56) and with photosynthetic photon flux (PPF; dry τ = −0.97; rainy τ = −0.98) were found, indicating a strong hydraulic coupling to atmospheric conditions. Sap flow and transpiration rates of this R. mangle scrub mangrove forest exceeded those of some tropical dry deciduous forests, suggesting adaptations that support water transport in saline environments. The clustered xylem vessels of R. mangle ensure safe sap flow year-round. As an evergreen species, it contributes water to the atmosphere all year-round, underscoring its critical role in the tropical ecohydrological environment. Full article

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article

The urbanization effect (UE) on local or regional climate is a prominent research topic in the research field of urban climates. However, there is little research on the UE of Urumqi, a typical arid region city, concerning various climatic factors and their spatio–temporal characteristics. This study quantitatively investigates the UE of Urumqi on multiple climatic factors in summer based on a decade-long period of WRF–UCM (Weather Research and Forecasting model coupled with the Urban Canopy Model) simulation data. The findings reveal that the UE of Urumqi has resulted in a reduction in the diurnal temperature range (DTR) within the urban area by causing an increase in night-time minimum temperatures, with the maximum decrease reaching −2.5 °C. Additionally, the UE has also led to a decrease in the water vapor mixing ratio (WVMR) and relative humidity (RH) at 2 m, with the maximum reductions being 0.45 g kg⁻¹ and −6.5%, respectively. Furthermore, the UE of Urumqi has led to an increase in planetary boundary layer height (PBLH), with a more pronounced effect in the central part of the city than in its surroundings, reaching a maximum increase of over 750 m at 19:00 Local Solar Time (LST, i.e., UTC + 6). The UE has also resulted in an increase in precipitation in the northern part of the city by up to 7.5 mm while inhibiting precipitation in the southern part by more than 6 mm. Moreover, the UE of Urumqi has enhanced precipitation both upstream and downstream of the city, with a maximum increase of 7.9 mm. The UE of Urumqi has also suppressed precipitation during summer mornings while enhancing it in summer afternoons. The UE has exerted certain influences on the aforementioned climatic factors, with the UE varying across different directions for each factor. Except for precipitation and PBLH, the UE on the remaining factors exhibit a greater magnitude in the northern region compared to the southern region of Urumqi. Full article

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts. Full article