Search Results (1,249)

This study analyzes heat waves (HWs), cold spells (CSs), and mean temperature trends in Türkiye’s three major metropolises (Istanbul, Ankara, and Izmir) using long-term station data. HW and CS events were defined via a percentile-based threshold approach, utilizing daily maximum (T_max) and minimum (T_min) temperature data from a total of 15 meteorological stations. Temporal trends in annual and seasonal wave frequencies, alongside mean temperature series, were evaluated using the Mann–Kendall test and Sen’s slope estimator. The findings indicate that HW frequencies have significantly increased across the majority of stations, whereas CS frequencies have decreased at most locations. It was determined that while HWs predominantly concentrate in summer and CSs in winter, heat extremes can extend into transitional seasons. Mean temperatures exhibit a statistically significant upward trend across all stations. Furthermore, HWs have become more prominent and CSs have dissipated more rapidly in urban and coastal stations. These results reveal that the risk of heat extremes is escalating while cold extreme events are weakening in Türkiye’s major cities due to warming climate conditions. Full article

Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO₂, NO_x, O₃, PM₁₀, PM_2.5, and SO₂, using hourly data from the Mexico City Atmospheric Monitoring System (SIMAT). Annual and monthly median concentrations were computed to reduce the influence of extreme values and short-term pollution episodes. Station-level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by the use of Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests and performed together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, moderate increases in annual median concentrations were observed at specific locations, particularly for PM₁₀, PM_2.5, NO₂, and NO_x, with relative changes ranging from approximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incremental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

In this study, a novel hybrid optimization approach is proposed to minimize the fuel consumption of commercial aircraft by taking flight-related and meteorological constraints into account during the cruise phase. The new method, the Decision Tree–Robust Multiple Regression–Harris Hawks Optimization Algorithm (DRHA), incorporates data segmentation based on decision trees, modeling of robust multiple regression, and the Harris Hawks optimization algorithm. In this context, a PID speed controller for a Boeing 737-800 aircraft was developed by employing a Software-in-the-Loop (SIL) framework that establishes real-time data exchange between MATLAB/Simulink and the FAA-approved X-Plane flight simulator. Within this framework, a simulation-based fuel consumption dataset was obtained from 1032 different scenarios encompassing various combinations of altitude, speed, aircraft weight, wind speed, and wind direction, thus aiming to reflect a wide range of realistic flight operating conditions. According to comparative analysis outcomes, the proposed DRHA approach significantly outperformed conventional statistical and machine learning-based methods in modeling fuel consumption equations. Namely, a mean absolute error (MAE) and R² value are achieved with values of 1.24 and 0.90, respectively. Moreover, PID controller parameters are optimized under varying conditions thanks to the DRHA method, yielding between 0.07% and 5.33% fuel savings compared to manually tuned controllers. Tests performed under different altitudes, aircraft weights, and wind conditions confirm the algorithm’s robustness and adaptability. The proposed method is anticipated to offer scalable and adaptable solutions for various types of aircraft and real-time control systems. Full article

With the growing penetration of photovoltaic (PV) generation, robust uncertainty characterization is essential for secure operation, economic dispatch, and flexibility planning. This review surveys PV scenario generation from three perspectives: (i) explicit probabilistic approaches (distribution fitting, Copula-based dependence modeling, autoregressive moving average (ARMA)-type time-series methods, and clustering/dimensionality reduction), (ii) deep generative models (GANs, VAEs, and diffusion models), and (iii) hybrid Statistical Relational AI (SRAI) frameworks. We discuss the strengths of explicit models in interpretability and tractability, and their limitations in representing high-dimensional nonlinear, multimodal, and multiscale spatiotemporal dependencies. We also examine the ability of deep generative methods to synthesize diverse scenarios across meteorological regimes and multiple sites, while noting persistent challenges in interpretability, physical consistency, and deployment. To bridge these gaps, we outline an SRAI-oriented integration pathway that embeds statistical structure, meteorology–power relations, spatiotemporal coupling, and operational constraints into generative architectures. Finally, we highlight directions for future research, including unified evaluation protocols, cross-regional data collaboration, controllable extreme-scenario generation, and computationally efficient generative designs. Full article

Skin cancer is the most common cancer worldwide, with ultraviolet radiation (UVR) being a major risk factor. Excessive UVR exposure can damage the skin and eyes, making it essential to monitor the Ultraviolet Index (UVI). However, few affordable devices are available for this purpose, limiting public awareness. This study presents the development, calibration, and experimental validation of a low-cost UVI monitoring device against a professional radiometer. The prototype was deployed in Campina Grande, Paraíba, Brazil, and its measurements were systematically compared with data from a nearby automatic meteorological station. The device, based on the UVM-30A sensor, measures UV radiation and transmits UVI values via a mobile application and a public display. Statistical analysis showed strong agreement with reference data, where Pearson Correlation Coefficient r = 0.849 (R² = 0.721 and RMSE = 1.26), and Confidence Index c = 0.917. The device provides an accessible tool for real-time UVI monitoring, promoting public awareness of solar radiation risks and supporting public photoprotection policies. Full article

Solar-induced chlorophyll fluorescence (SIF) is an emerging predictor in the crop gross primary production (GPP) estimation for its close relationships with vegetation photosynthesis. Conventional crop GPP are estimated by data-driven models upscaled from eddy covariance flux observations, light-use efficiency (LUE) models, and process-based models, which are constrained by the limited availability of in-site experimental and simulated data. By using vegetation remote sensing data and meteorological data to simulate the combined impacts of changes in vegetation physiological factors and environmental factors on GPP estimation, we proposed a new method to estimate GPP for winter wheat over the North China Plain (NCP) based on the SIF-based mechanistic light response (MLR) model with bias correction. Results showed that (1) vegetation and meteorological factors could be used to fit the bias caused by the static input parameters of the MLR model for winter wheat GPP estimation, which solved the unavailability of the input parameters in the MLR models; (2) the MLR model with bias correction could quickly achieve large-scale crop GPP estimation at the regional scale during the vigorous period of winter wheat, whose performance was superior to that of a traditional statistical regression model with an increased R² of 6.4%. Full article

Global climate change poses increasing challenges to agricultural production and global food security by intensifying temperature and precipitation variability and increasing the frequency of extreme weather events. While several studies have examined farmers’ perceptions of climate change in the United States, limited empirical evidence exists for the Delaware, Maryland, and Virginia (Delmarva) Peninsula. This study assessed farmers’ perceptions of climate change in the Delmarva region and identified key factors influencing these perceptions, as well as adaptation strategies employed to address climate-related risks. Primary data were collected through a structured survey administered to farmers across the Delmarva Peninsula, while secondary data consisted of historical temperature and precipitation records obtained from meteorological stations in the region. Descriptive statistics were used to summarize farmer perceptions and adaptation practices, and a logit regression model was applied to examine socioeconomic and experiential factors influencing perceptions of climate change. Analysis of climate data revealed notable variability in temperature and rainfall patterns, with the warmest temperatures occurring during June, July, and August and peak rainfall generally observed between May and September. Survey results showed that a large majority of respondents (88.2%) perceived that climate change is occurring. Logit model results indicated that farmers’ age, education level, acceptance of climate change adaptation practices, and observed changes in climate over the past 5–10 years positively influenced perceptions of climate change. Adaptation strategies included selective crop choices, avoiding cultivation in flood-prone areas, adoption of soil conservation practices, and the use of crop insurance. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

The research focuses on analyzing historical climate variability over the period 1982–2022, as well as future projections of climate change over the period 2025–2099, with regard to the Souss-Massa watershed, a semi-arid region with high dependency on agricultural activities. Precipitation and temperature data were collected annually from five meteorological stations, Agadir, Amaghouz, Amsoul, Aoulouz, and Taroudant, in order to analyze long-term climatic trends and predict possible scenarios of climate change. A trend analysis was carried out using a combination of the Mann–Kendall test and Sen’s slope estimator. The findings of this study indicate that there is an increase in mean annual temperature that is statistically significant (p < 0.05) across all stations, ranging from +0.28 °C per decade at Agadir, which is located along the coastal region of Morocco, to as high as +0.45 °C per decade at Taroudant, which is located inland. Conversely, the precipitation trend is decreasing and not statistically significant (p > 0.05). For projecting future climatic conditions, we used the Statistical Down-Scaling Model (SDSM v4.2.9) with global climate models using outputs from CanESM2 under two emission scenarios, namely RCP 4.5 and RCP8.5. The calibration period (1982–2001) and the validation period (2002–2022) were satisfactory, as indicated by the high values of the coefficients of determination (R² > 0.6) for temperature and moderate values (R² = 0.5–0.6) for precipitation. Projections indicate an increase in temperature, with the mean temperature change ranging from +4.8 °C and +8.7 °C by 2099 depending on the station’s location. Projected precipitation decreases are found under both scenarios, but with stronger decreases under RCP8.5, especially along the coastal regions, with decreases as large as −53.8% at Agadir. However, the precipitation projections have to be used with caution due to the limitations associated with the downscaling methods and the use of a single global climate model. All the projections indicate a trend towards arid conditions, emphasizing the need for adaptive water resources management and improving the ensemble models for climate projections. Full article

Southern Africa is highly sensitive to climate variability associated with the El Niño Southern Oscillation (ENSO), which strongly influences hydroclimate, vegetation dynamics, and atmospheric composition. This study examined the impacts of the 2015/16 El Niño on vegetation, meteorological conditions, and atmospheric emissions over Southern Africa using satellite observations and reanalysis data. Time-lagged cross-correlation analysis of seasonally adjusted time-series was applied to characterize synchronous and delayed interactions among vegetation indices, hydrological variables, meteorological drivers, and air-quality parameters. Bayesian causal impact analysis was further used to quantify El Niño-induced anomalies by comparing observed conditions with counterfactual scenarios representing the absence of the event. The results showed that vegetation greenness responds primarily to concurrent moisture availability, with strong positive associations between NDVI, precipitation, soil moisture, and canopy water. Moisture-related variables exert delayed influences on atmospheric composition, highlighting the role of wet scavenging and dilution. Carbonaceous aerosols (black carbon [BC] and organic carbon [OC]), particulate matter [PM_2.5], and aerosol optical depth exhibit strong synchronous coupling, indicating a dominant biomass-burning source. The causal impact analysis reveals statistically significant and sustained post-2015 increases in fire-related emissions (carbon monoxide [CO], BC, OC, PM_2.5, and aerosol optical depth [AOD]), particularly during austral winter and dry seasons. In contrast, precipitation, soil moisture, evapotranspiration, and vegetation greenness show persistent negative anomalies, reflecting widespread drought stress under elevated temperatures. Overall, the findings demonstrate that the 2015/16 El Niño amplified fire emissions while suppressing ecosystem functioning across Southern Africa, underscoring strong climate–fire–vegetation feedback with important air-quality and environmental implications. Full article

Coastal storms represent a major environmental issue and constitute an important challenge for coastal flood management. This study analyzes the frequency and characteristics of storms on the Senegalese coast between 1993 and 2023, focusing on four coastal cities: Dakar, Saint-Louis, Mbour, and Cap-Skring. The analysis is based on wave data from the ERA5 model and on meteorological and oceanographic data from different models. Storms were detected using the Peak Over Threshold (POT) method, based on the 95th percentile and fitted to a generalized Pareto distribution (GPD). The results reveal a contrasted spatial distribution of coastal storms, with a higher occurrence in Dakar and Saint-Louis. An apparent increase in the frequency of storms is observed in Saint-Louis, Mbour, and Cap-Skring, while an apparent decrease is noted in Dakar; however, these trends are not statistically significant. Extreme coastal water levels (ECWL) associated with storms show an opposite evolution, with an apparent decrease in the first three regions and an apparent increase in Dakar. The most intense and longest storms, in terms of energy content (Es), are mainly observed in Dakar and Saint-Louis. A linear relationship is highlighted between the duration and intensity of storms. Storm occurrence shows a strong seasonal modulation, with a predominance during the dry season (November to May). The most energetic storms are mostly generated by waves from the west to west-northwest direction in Dakar and Saint-Louis, while Mbour and Cap-Skring present a wider directional window. This first analysis at the scale of the Senegalese coast provides essential elements for understanding the risk of coastal storms and constitutes support for coastal flood management in a context of climate change. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

This study compares the advantages and limitations of traditional CMIP6 data fusion methods and machine learning fusion methods when applied to drought identification in the Yangtze River Basin. We consider three traditional fusion methods and five machine learning fusion methods, and calculate drought indices over 3-, 6-, and 12-month periods based on precipitation data from meteorological stations in the Yangtze River Basin (1960–2014) and 15 CMIP6 model datasets. The drought identification index is used to evaluate the performance of the fusion methods. Results indicate that traditional statistical methods have significant limitations in the upper reaches of the basin, where the terrain is highly undulating, but perform better in the middle and lower reaches, which are relatively flat. Among the machine learning methods, neural networks tend to amplify the observational noise, whereas kernel-tuning methods better accommodate nonlinear relationships across different SPI time scales. The prediction performance of all methods decreases from the 12- to 3-month drought indices, but the extent of the decline varies. The Random Forest and Radial Basis Function methods give the smallest reduction in performance, while the Backpropagation and Backpropagation-Adaboost methods produce the largest drop in performance. Full article

This study evaluates the capability of multi-temporal vegetation indices derived from Sentinel-2 imagery to indicate groundwater discharge in a forested mountainous sector of the Northern Apennines (Italy). The NDVI was computed from Level-2A surface reflectance data (10 m resolution) and analyzed over five growing seasons (2017–2021), encompassing recurrent summer droughts. Aridity conditions were quantified using the Standardized Precipitation–Evapotranspiration Index (SPEI) derived from long-term meteorological records. The methodological framework integrates cloud-masked satellite observations, drought characterization, and spatial statistical comparison between known spring discharge zones and randomly distributed forested control points. NDVI values extracted within 100 m radius buffers, centered on spring outlets, were systematically compared with those from control areas located outside the shallow-water-table influence zone. During periods of negative SPEI (moderate-to-severe drought), spring-centered buffers consistently exhibited higher NDVI values than control sites, with the NDVI contrast increasing under severe arid conditions. This pattern indicates enhanced vegetation resilience supported by shallow groundwater availability. The results demonstrate that vegetation health anomalies, when constrained by homogeneous land cover and a consistent hydrogeological setting, can serve as indicators of the groundwater discharge likelihood. The proposed workflow provides a reproducible and cost-effective tool to support hydrogeological reconnaissance and spring inventorying in rugged mountainous environments where field-based surveys are logistically demanding. Full article