Search Results (6,799)

Solar diffuse radiation (DIFRA) is an important component of solar radiation, but current research into the estimation of DIFRA is relatively limited. This study, based on remote sensing data, topographic data, meteorological reanalysis materials, and measured data from radiation observation stations in Chongqing, combined key factors such as the solar elevation angle, water vapor, aerosols, and cloud cover. A high-precision DIFRA estimation model was developed using the random forest algorithm, and a distributed simulation of DIFRA in Chongqing was achieved. The model was validated using 8179 measured data points, demonstrating good predictive capability with a correlation coefficient (R²) of 0.72, a mean absolute error (MAE) of 35.99 W/m², and a root mean square error (RMSE) of 50.46 W/m². Further validation was conducted based on 14 radiation observation stations, with the model demonstrating high stability and applicability across different stations and weather conditions. In particular, the fit was optimal for the model under overcast conditions, with R² = 0.70, MAE = 32.20 W/m², and RMSE = 47.51 W/m². The results indicate that the model can be effectively adapted to all weather calculations, providing a scientific basis for assessing and exploiting solar energy resources in complex terrains. Full article

With the increasingly severe problems of global climate change and resource scarcity, sustainable development has become an important issue of common concern in various industries. The construction industry is one of the main sources of global energy consumption and carbon emissions, and sustainable buildings are an effective way to address climate change and resource scarcity. Meteorological conditions are closely related to building energy efficiency. Therefore, the research is founded upon a substantial corpus of meteorological data, employing a compressed sensing reconstruction algorithm to supplement the absent meteorological data, and subsequently integrating an enhanced density peak clustering algorithm for data mining. Finally, an intelligent, sustainable, building energy-saving design platform is designed based on this. The research results show that in the case of random defects in monthly timed data that are difficult to repair, the reconstruction error of the compressed sensing reconstruction algorithm is only 0.0403, while the improved density peak clustering algorithm has the best accuracy in both synthetic and real datasets, with an average accuracy corresponding to 0.9745 and 0.8304. Finally, in the application of the intelligent, sustainable, building energy-saving design platform, various required information such as HVAC data energy-saving design parameters, cloud cover, and temperature radiation are intuitively and fully displayed. The above results indicate that the research method can effectively explore the potential valuable information of sustainable building energy-saving design, providing a reference for the design of sustainable buildings and climate analysis. Full article

Atmospheric aerosols significantly influence climate systems and hydrological processes, but their impacts on evapotranspiration remain insufficiently understood, particularly in tropical savanna regions. This study investigates the direct and indirect effects of aerosol optical depth (AOD) on reference evapotranspiration ($E{T}_0$) in the Mato Grosso Cerrado, Brazil, a biome characterized by pronounced seasonal climatic variations. Using data collected from the AERONET network at Fazenda Miranda, AOD was analyzed alongside meteorological variables such as air temperature, global radiation, and $E{T}_0$, estimated using the FAO Penman–Monteith method. The results reveal distinct seasonal patterns, with aerosols having a more pronounced influence during the dry season. Positive correlations were observed between AOD and air temperature, while negative correlations were found between AOD and global radiation, especially during the dry season. The relationship between AOD and $E{T}_0$ varied between years and seasons, with significant reductions in $E{T}_0$ linked to high aerosol concentrations during the dry period. These findings demonstrate that aerosols play a critical role in modulating evapotranspiration and radiation balance, particularly in regions affected by biomass burning. This study provides valuable insights into the interplay between aerosols, climate variables, and hydrological processes, contributing to a better understanding of aerosols’ impacts on tropical ecosystems. Full article

To assess the carbon balance of terrestrial ecosystems, it is crucial to consider the net primary productivity (NPP) of vegetation. Understanding the response of NPP in Xinjiang’s vegetation to climate factors and human activities is essential for ecosystem management, the Belt and Road Initiative, and achieving carbon neutrality goals. Based on the CASA model, this study uses meteorological data, DEM data, and land cover data, employing trend analysis and partial derivative analysis methods to investigate the temporal trends and spatial distribution of NPP in Xinjiang from 2000 to 2020. Additionally, it quantifies the contributions of climate factors and human activities to NPP fluctuations. The key findings are: (1) The average annual NPP is 101.52 gC/m², with an upward trend, showing an overall growth rate of 0.447 gC/m²/yr. Spatially, NPP is higher in northern Xinjiang than in the south, and in mountainous areas compared to basins. (2) Over 21 years, climate factors contributed an average of 1.054 gC/m²/yr, while human activities contributed 0.239 gC/m²/yr to NPP changes. Among climate factors, temperature, precipitation, and sunshine duration contributed 0.003, 0.169, and 0.588 gC/m²/yr, respectively, all showing positive effects on NPP. (3) Forests have the highest average NPP at 443.96 gC/m², with an annual growth rate of 2.69 gC/m²/yr. When forest is converted to cropland, the net loss in NPP is −1.94 gC/m², and the loss is even greater in conversion to grassland, reaching −17.33 gC/m². (4) The changes in NPP are driven by both climate factors and human activities. NPP increased in 77.25% of the area, while it decreased in 22.69%. Climate factors have a greater positive impact than human activities. Full article

Climate change and increasing water demand are causing supply problems in Mexico City and the State of Mexico. The lack of complete and up-to-date meteorological information makes it difficult to understand and analyze climate phenomena such as droughts. Climate Engine provides decades of climate data to analyze such changes. These data were used to calculate SPEI (Standardized Precipitation-Evapotranspiration index) at scales of 1, 3, 6, 9, 12, and 24 months between 1981 and 2023 in the study area. The Standard Normal Homogeneity Test (SNHT) indicated greater homogeneity in temperature data, while precipitation data exhibited potential inhomogeneities. The Mann–Kendall test showed no significant trend for precipitation but a clear increasing trend in temperature. Droughts have become more frequent and severe over the last decade, particularly in the western State of Mexico and the southwest of Mexico City. The wettest years within the last 14 years were 2010, 2015, and 2018, while the most severe droughts occurred in 2017, 2019, 2020, 2021, and 2023. The findings suggest intensifying drought conditions, likely driven by rising temperatures and climate variability. These trends emphasize the need for improved water resource management and adaptation strategies to mitigate the growing impact of droughts in central Mexico. Full article

Weather conditions significantly influence urban cycling, shaping both its frequency and intensity. This study develops a predictive model to evaluate the impact of five key meteorological factors, namely temperature, humidity, precipitation, wind speed, and daylight duration, on urban cycling trends. Using non-linear regression analysis, the research examines cycling data from 2017 to 2019 in Hamburg, Germany, comparing predicted values for 2019 with actual data to assess model accuracy. The statistical analyses reveal strong correlations between weather parameters and cycling activity, highlighting each factor’s unique influence. The model achieved high accuracy, with R² values of 0.942 and 0.924 for 2017 and 2019, respectively. To further validate its robustness, the model is applied to data from 2021 and 2023—years not included in its initial development—yielding R² values of 0.893 and 0.919. These results underscore the model’s reliability and adaptability across different timeframes. This study not only confirms the critical influence of weather on urban cycling patterns, but also provides a scalable framework for broader urban planning applications. Beyond the immediate findings, this research proposes expanding the model to incorporate urban factors, such as land use, population density, and socioeconomic conditions, offering a comprehensive tool for urban planners and policymakers to enhance sustainable transportation systems. Full article

The adverse effects of short-term exposure to pollutants are the focus of many epidemiological studies. Little is known about the modification effects of season and temperature on the association between pollutants and the acute onset of circulatory diseases. The aim of this study was to investigate the effect of PM_2.5 on emergency ambulance dispatches (EADs) due to circulatory system diseases in different seasons and temperature levels, and to locate the vulnerable population. We collected data on daily emergency ambulances, meteorological data, and air pollution concentration in Shenzhen from 2013 to 2020. A distributed lag nonlinear model was conducted to assess the effect of PM_2.5 on circulatory system disease emergency ambulance dispatches modified by season. In addition, generalized additive models were used to detect the interactive effect of PM_2.5 and temperature on emergency ambulance dispatches due to circulatory disease. A 10 μg/m³ increase in PM_2.5 concentration was associated with a 2.43% (1.47–3.40%) increase in the risk of circulatory system disease emergency ambulance dispatches over lags of 0–5 days during the cold season, compared to 0.75% (−0.25–1.76%) during the warm season. This trend was consistent across temperature levels, with a significant 2.42% (1.47–3.10%) increase on low-temperature days, while no significant effect was observed on high-temperature days. For young people, the effect of PM_2.5 on circulatory system disease emergency ambulance dispatches was higher in the cold season and low temperature days. The cold season and low temperature significantly enhanced the adverse effect of PM_2.5 on the acute onset of circulatory system diseases, especially in young people. It is critical to focus on the synergistic effects of temperature and pollutants on the health of different vulnerable populations in different regions and climates. Full article

As global warming continues to intensify, typhoon disasters will more frequently occur in East and Southeast Asia, posing a high risk of causing large-scale power outages in the power system. To investigate the impact of typhoon disasters on high-voltage power grids, a comprehensive risk assessment method that integrates model-driven and data-driven approaches is proposed, which can predict power grid faults in advance and provide support for power grid operators to generate emergency dispatching plans. Firstly, by comparing actual loads with the design strengths of the transmission tower-line system and analyzing the geometric relationship between typhoon wind circles and the system, key variables, such as wind speed, longitude, latitude, and other pertinent factors, are screened. The Spearman correlation coefficient is employed to pinpoint the meteorological variables that exhibit a high degree of relevance, enhancing the accuracy and interpretability of our model. Secondly, addressing the lack of power grid fault samples, three data balancing methods—Borderline-SMOTE, ADASYN, and SMOTE-Tomek—are compared, with Borderline-SMOTE selected for its superior performance in enhancing the sample set. Additionally, a power grid failure risk assessment model is built based on Light Gradient Boosting Machine (LightGBM), and the Borderline-Smoothing Algorithm (BSA) is used for the modeling of power grid faults. The nonlinear mapping relationship between typhoon meteorological data and the power grid equipment failure rate is extracted through deep learning training. Subsequently, the Tree-structured Parzen Estimator (TPE) is leveraged to optimize the hyperparameters of the LightGBM model, thus enhancing its prediction accuracy. Finally, the actual power system data of a province in China under a strong typhoon are assessed, validating the proposed assessment method’s effectiveness. Full article

Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. This study investigates the forecasting of criterion pollutants—$CO,O_3,S{O}_2,N{O}_2,P{M}_{2.5},$ and $P{M}_{10}$—across multiple temporal frames (hourly, daily, weekly, monthly) in Salamanca, Mexico, utilizing temporal, meteorological, and pollutant data from local monitoring stations. The primary objective is to identify robust models capable of short- and mid-term predictions, despite challenges related to data inconsistencies and missing values. Leveraging the low-code PyCaret framework, a benchmark analysis was conducted to identify the best-performing models for each pollutant. Statistical evaluations, including ANOVA and Tukey HSD tests, were employed to compare model performance across different time frames. The results reveal significant variations in prediction accuracy depending on both the pollutant and temporal windows, with stronger predictive performance observed in the weekly and monthly frames. The research indicates that the incorporation of temporal and environmental variables enhances forecast accuracy and highlights the value of low-code AutoML tools, such as PyCaret, in streamlining model selection and improving overall forecasting efficiency. Full article

This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating diverse data sources, including meteorological, phenotypic, and remote sensing data. Advances in computer technology have enabled the creation of more sophisticated models, such as mixed, geostatistical, and Bayesian models. Special attention is given to deep learning techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex data patterns. The article also discusses the effectiveness of other algorithms, such as Random Forest and Support Vector Machines, in capturing nonlinear relationships affecting yields. According to standards adopted in agricultural research, the Mean Absolute Percentage Error (MAPE) in the implementation of prediction issues should generally not exceed 15%. Contemporary research indicates that, through the use of advanced and accurate algorithms, the value of this error can reach levels of even less than 10 per cent, significantly increasing the efficiency of yield forecasting. Key challenges in the field include climatic variability and difficulties in obtaining accurate data on soil properties and agronomic practices. Despite these challenges, technological advancements present new opportunities for more accurate forecasting. Future research should focus on leveraging Internet of Things (IoT) technology for real-time data collection and analyzing the impact of biological variables on yield. An interdisciplinary approach, integrating insights from ecology and meteorology, is recommended to develop innovative predictive models. The exploration of machine learning methods has the potential to advance knowledge in potato yield forecasting and support sustainable agricultural practices. Full article

Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the existing studies based on deep learning have constructed mappings from low-resolution inputs to high-resolution downscaled estimates. However, these methods have failed to capture the relationships between multiple variables as revealed by physical processes. Therefore, this paper proposes a spatial downscaling approach for satellite sea surface wind that employs soft-sharing multi-task learning. Sea surface temperature and water vapor are included as auxiliary variables for SSW, considering the close correlation revealed by physical principles and data availability. The spatial downscaling of auxiliary variables is designed as an auxiliary task and integrated into a multi-task learning network with generative adversarial network and dual regression structures. The proposed multi-task downscaling network achieves flexible parameter sharing and information exchange between tasks through a soft-sharing mechanism and bridge modules. Comprehensive experiments were conducted with WindSat SSW products at 0.25° from Remote Sensing Systems. The experimental results validate the outstanding downscaling capability of the proposed methodology with respect to precision in comparison with buoy measurements and reconstruction quality. Full article

Citrus cultivation represents one of the major economic pillars of the Valencian Community (Spain). Frost events pose a significant threat to these plantations, resulting in substantial economic losses. This study aims to assess the frequency and intensity of frost occurrences in the region from 2004 to 2023, using Meteosat Second Generation satellite imagery. These images provide daily land surface temperature data at 15 min intervals. Frost days were defined as those when temperatures fell below −2.3 °C, the threshold at which orange fruits become susceptible to damage, with different temperature thresholds applied to estimate varying levels of crop damage. Frost duration was also analyzed to classify event intensity and its potential impact on citrus crops. Annual comparisons revealed a decline in both the severity and frequency of frosts, particularly in cases of “moderate” and “intense” damage, supporting forecasts of increased regional aridity and suggesting new opportunities for expanding citrus cultivation to higher altitudes. When compared with farmers’ records, this study’s methodology proves effective in assessing frost impact and offers potential use for winter crop insurance. Validation was conducted using in situ data from the Spanish National Meteorological Agency (AEMET). Full article

We introduce a novel Rayleigh Doppler lidar (RDLD) system that utilizes a high-power single-frequency laser with over 60 W average output power, achieved through fiber solid-state cascade amplification. This lidar represents a significant advancement by addressing common challenges such as mode hopping and multi-longitudinal mode issues. Designed for atmospheric wind and temperature profiling, the system operates effectively between altitudes of 30 km and 70 km. Key performance metrics include wind speed and temperature measurement errors below 7 m/s and 3 K, respectively, at 60 km, based on 30 min temporal and 1 km spatial resolutions. Observation data align closely with ECMWF reanalysis data, showing high correlation coefficients of 0.98, 0.91, and 0.94 for zonal wind, meridional wind, and temperature, respectively. Continuous observations also reveal detailed wind field variations caused by gravity waves, demonstrating the system’s high resolution and reliability. These results highlight the RDLD system’s potential for advancing meteorological monitoring, atmospheric dynamics studies, and environmental safety applications. Full article

The study is about the influence of climate change on tree growth in urban forests in Cuiabá, Mato Grosso, Brazil, using dendrochronology. The study focuses on two species, Cedrela fissilis Vell. and Copaifera langsdorffii Desf., both with dendrochronological potential. Samples were collected from an urban forest fragment, and local (temperature and precipitation) and global (ocean surface temperature—SST and Niño 3.4 index) meteorological data were analyzed to correlate with ring width. The methodology involved collecting, preparing, polishing, and marking the rings. The data series were analyzed using the COFECHA, Arstan, and CooRecorder programs to verify the accuracy of ring dating and SAS program for correlations with climatic variables. Both species exhibited good correlations between growth rings and climatic conditions. Cedrela fissilis and Copaifera langsdorffii were positively correlated with precipitation during the dry season and generally negatively correlated with temperatures. Negative correlations were identified with SST and Niño 3.4 for both species. These results are important for understanding how urban forests respond to climate change and how the study of growth rings can be used to predict the future impacts of these changes on plant species. Full article

In the context of climate change, extreme meteorological events such as severe storms produced by an isolated high-level atmospheric depression (known in Spanish as “Depresión Aislada en Niveles Altos”—DANA) are becoming increasingly frequent in the Mediterranean region, posing significant risks to ecosystems and human infrastructure. This study evaluates the impact of a DANA event in October 2024 on the water quality of Albufera Lake (Spain), a crucial Mediterranean wetland. A comprehensive evaluation was conducted by combining field data on physicochemical and biological parameters with satellite observations (Sentinel-2 and Landsat-8) to assess alterations before and after the event. Variables such as conductivity, nitrate, and total solids exhibited significant reductions immediately following the DANA, with conductivity decreasing by 82% compared to pre-event levels. These alterations signify a substantial renewal of the lake system driven by heavy rainfall and subsequent water releases. However, the lake demonstrated signs of recovery toward pre-event conditions over the following month. These results are consistent with previous findings, underscoring the system’s resilience and the necessity of periodic water releases to maintain ecological balance. The use of remote sensing tools effectively captured these dynamics, offering valuable insights for the long-term monitoring of water quality. This study highlights the urgent need for proactive management strategies to mitigate the effects of increasingly intense meteorological disturbances. Full article