Search Results (265)

Traditional methods for crop data collection are labor-intensive, inefficient and, more costly compared to remote sensing (RS) techniques. This study aims to identify key climatic variables influencing maize and wheat yields and develop predictive models while also evaluating the performance of the CropWatch cloud yield prediction model (CW_YPM) in major agricultural regions of Ethiopia. Climate data from 54 meteorological stations spanning 2000–2021 were analyzed. RS data, including NDVI from MODIS at 250 m resolution, agroecological zones, and observed crop yield data, were utilized for model prediction and validation. Correlation analysis and a stepwise modeling approach with multiple regression models were applied. The results revealed regional variations in the effects of climatic parameters on yields, with vapor pressure deficits showing negative correlations and rainfall exhibiting positive correlations. Non-linear models generally outperformed linear models in yield prediction—using both climate-only (CO) and combined climate-NDVI data. The best CO model for maize in the Horo Guduru area achieved an RMSE of 0.392 tons/ha, an R² of 0.94, and an index of agreement (d) of 0.984. Incorporating NDVI improved accuracy, with the best maize model in the Illu Ababor area achieving an RMSE of 0.477 tons/ha, an R² of 0.91, and d of 0.976. CW_YPM also performed effectively across the study area. This research highlights the value of integrating critical climatic variables with the NDVI to enhance crop yield forecasting in Ethiopia, thereby-supporting agricultural planning and food security initiatives. Full article

The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production. Full article

(1) The aliasing of radial velocities from weather radars is a known challenge in meteorology. It may also occur during bird migration if the unambiguous velocity threshold is below the birds’ ground speed. High variability in birds’ radial velocities and high flight speeds lead to multiple aliasing (folding) and challenge meteorological dealiasing approaches. Unfolded radial velocities are essential for calculating flight directions and speed and derived migration traffic rates for aeroecological applications. (2) We study the occurrence of aliasing in measurements of different pulse repetition frequencies (PRF) in C-band weather radars in bird and insect cases and test the efficiency of a dealiasing algorithm widely used in biological weather radar software. We use dual-PRF measurements as a reference to avoid the folding of radial velocities in quantitative and qualitative bird migration outputs. (3) The dealiasing algorithm performed poorly in single-PRF measurements during bird migration, though not in insect and precipitation cases. In contrast, dual-PRF velocities yielded proper flight speeds, flight directions and migration traffic rates. (4) The study unveils severe biases in aeroecological analyses of C-band weather radars from imperfectly dealiased single-PRF radial velocities. Dual-PRF measurements with appropriate dealiasing postprocessing offer a valid alternative to single PRF and should be preferred whenever available. Full article

This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series. Full article

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. Full article

This study presents the first comprehensive assessment of air pollution by ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ in the city of Cusco, aiming to determine atmospheric pollution levels, characterize air quality, and develop predictive models. The research, conducted during 2017–2020, systematically evaluated particulate matter (PM) contamination using a high-volume sampler (HiVol ECOTEC 3000) installed at 18 monitoring sites distributed across five urban districts. Multiple linear regression (MLR) models were developed and evaluated, incorporating meteorological, seasonal, and temporal variables under two approaches: direct linear (Model 1) and logarithmic transformation (Model 2). The model evaluation employed R², RMSE, MAE, MAPE, IOA, and CV statistical indicators. The results revealed concentrations significantly exceeding WHO guideline values, with ${\mathrm{PM}}_{2.5}$ ranging between 41.10 ± 3.2 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) and 82.01 ± 5.1 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2018), while ${\mathrm{PM}}_{10}$ values ranged from 45.07 ± 2.8 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2020) to 72.35 ± 4.3 $\mathsf{\mu }{\mathrm{g}/\mathrm{m}}^3$ (2017). A notable reduction was observed during 2020, attributable to COVID-19 pandemic restrictions. The Air Quality Index (AQI) indicated predominantly “Unhealthy” and “Very Unhealthy” levels during 2017–2018, improving to “Unhealthy for Sensitive Groups” in 2020. MLR models achieved maximum efficiency using logarithmic transformation, obtaining R² = 0.98 (p < 0.001) for ${\mathrm{PM}}_{2.5}$ in the 2020 rainy season and R² = 0.44 (p < 0.001) for ${\mathrm{PM}}_{10}$ in the 2018 annual model. These findings demonstrate the existence of nonlinear relationships between pollutants and predictor variables in Cusco’s atmospheric basin. Full article

Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap impedes effective regional planning and environmental management in this ecologically sensitive area. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. Results show a significant drying trend in the IRV, starting in 1995, with frequent drought events from 2018 onwards, and no notable transition year observed from wet to dry conditions. The overall drought rate was −0.09 per decade, indicating milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters compared to regional averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in the drying seasons occurred later in the IRV than in Xinjiang, with delays of 21 years for summer, and over 17 and 35 years for spring and autumn, respectively, indicating a lagged response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. The study also reveals significant differences in drought cycles, which are longer than those in Xinjiang, with distinct wet–dry phases observed across multiple time scales and seasons, emphasizing the complexity of drought variability in the IRV. In conclusion, the valley exhibits unique drought characteristics, including milder intensity, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings underscore the need for region-specific drought management strategies, as broader approaches may not be effective at the subregional scale. Full article

Background: Dengue is a viral disease transmitted by the mosquitoes Aedes, which is characterized by fever, myalgia and arthralgia. In some cases, it can be fatal. For many years, dengue fever has been endemic to Mexico; however, few studies have investigated the historical and current extents of dengue fever at the national level or considered the effects of variables such as temperature, precipitation and elevation on its occurrence. Methods: An ecological study was carried out to compare the incidence rates of different types of dengue fever per hundred thousand inhabitants with temperature, precipitation and elevation between 1985 and 2023 in Mexico. The sources of information were the public records of the Ministry of Health and the National Meteorological Service. Multiple linear regression analysis was performed with Pearson and Spearman correlation coefficients at an alpha of <0.05. Results: The global linear regression presented an R² of 0.68 between the mean temperature and the cases of haemorrhagic dengue/severe/with warning signs. The degree of rainfall was not strongly correlated with the incidence rate, except in the eastern part of the country, where average temperature was also strongly correlated with the incidence rate. Nonsevere/classic dengue was most common from 1501 to 2000 m elevation, whereas severe forms of the disease were more prevalent at elevations greater than 2000 m. Full article

Air pollution causes millions of premature deaths per year due to its strong association with several diseases and respiratory afflictions. Consequently, air quality monitoring and forecasting systems have been deployed in large urban areas. However, those systems forecast outdoor air quality while people living in relatively large cities spend most of their time indoors. Hence, this work proposes an indoor air quality forecasting system, which was trained with data from Mexico City, and that is supported by deep learning architectures. The novelty of our work is that we forecast an indoor air quality index, taking into account seasonal data for multiple horizons in terms of minutes; whereas related work mostly focuses on forecasting concentration levels of pollutants for a single and relatively large forecasting horizon, using data from a short period of time. To find the best forecasting model, we conducted extensive experimentation involving 133 deep learning models. The deep learning architectures explored were multilayer perceptrons, long short-term memory neural networks, 1-dimension convolutional neural networks, and hybrid architectures, from which LSTM rose as the best-performing architecture. The models were trained using (i) outdoor air pollution data, (ii) publicly available weather data, and (iii) data collected from an indoor air quality sensor that was installed in a house located in a central neighborhood of Mexico City for 17 months. Our empirical results show that deep learning models can forecast an indoor air quality index based on outdoor concentration levels of pollutants in conjunction with indoor and outdoor meteorological variables. In addition, our findings show that the proposed method performs with a mean squared error of 0.0179 and a mean absolute error of 0.1038. We also noticed that 5 months of historical data are enough for accurate training of the forecast models, and that shallow models with around 50,000 parameters have enough predicting power for this task. Full article

To improve radar-based quantitative precipitation estimation (QPE) methods, this study investigated the relationship between radar reflectivity (Z) and hourly rainfall intensity (R) using data from 289 precipitation events in Shanghai between September 2020 and March 2024. Two Z-R relationship models were compared in terms of their fitting performance: Z = 270.81 R^1.09 (empirically fitted relationship) and Z = 300 R^1.4 (standard relationship). The results show that the Z = 270.81 R^1.09 model outperforms the Z = 300 R^1.4 model in terms of fitting accuracy. Specifically, the Z = 270.81 R^1.09 model more effectively captures the nonlinear relationship between radar reflectivity and rainfall intensity, with a higher degree of agreement between the fitted curve and the observed data points. This model demonstrated superior performance across all 289 precipitation events. This study evaluated the performance of four machine learning approaches while incorporating five meteorological features: specific differential phase shift (KDP), echo-top height (ET), vertical liquid water content (VIL), differential reflectivity (ZDR), and correlation coefficient (CC). Nine QPE models were constructed using these inputs. The key findings are as follows: (1) For models with a single-variable input, the KAN deep learning model outperformed Random Forest, Gradient Boosting Decision Trees, Support Vector Machines, and the traditional Z-R relationship. (2) When six features were used as inputs, the accuracy of the machine learning models improved significantly, with the KAN deep learning model outperforming other machine learning methods. Compared to using only radar reflectivity, the KAN deep learning model reduced the MRE by 20.78%, MAE by 4.07%, and RMSE by 12.74%, while increasing the coefficient of determination (R²) by 18.74%. (3) The integration of multiple meteorological features and machine learning optimization significantly enhanced QPE accuracy, with the KAN deep learning model performing best under varying meteorological conditions. This approach offers a promising method for improving radar-based QPE, particularly considering seasonal, weather system, and precipitation stage differentiation. Full article

Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East. Full article

Background and Objectives: The incidence of hip fractures is increasing, and there have been reports linking cold weather to a higher risk of fractures. This study aimed to evaluate clinical variables in hip fracture patients who may predispose them to such fractures under different temperatures. Materials and Methods: This is a cross-sectional study conducted at a single medical center, enrolling older adults (≥60 years) who had experienced a hip fracture. Comprehensive clinical histories and detailed information regarding each patient’s hip fracture were obtained. All meteorological data were extracted from the Taiwan Central Weather Bureau database. Multiple clinical parameters that may have a close connection with the temperature at which the hip fracture occurred were screened. Statistical analysis involved using the Pearson correlation test or the independent Student’s t test, followed by generalized estimating equation analysis. Results: The cohort comprised 506 older adults with hip fractures. Initial univariate analysis revealed that a history of past cerebrovascular diseases, Charlson Comorbidity Index, patient age, and preinjury Barthel Index were significantly related to the temperature at which the hip fractures occurred. The generalized estimating equation analysis indicated that only the Charlson Comorbidity Index had a considerably inverse association with temperature. This finding suggests that for older adults with a higher Charlson Comorbidity Index, hip fractures tend to occur at lower temperatures and vice versa. Conclusions: Comorbidities are the only clinical concern that predisposes older adults to hip fractures under colder temperatures. This epidemiological finding could guide future patient education and hip fracture prevention programs. Full article

Environmental pollution indicated by the presence of $P{M}_{2.5}$ particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and $P{M}_{2.5}$ particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between $P{M}_{2.5}$ and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between $P{M}_{2.5}$ and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with $P{M}_{2.5}$. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. $P{M}_{2.5}$ exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between $P{M}_{2.5}$ levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of $P{M}_{2.5}$ on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and $P{M}_{2.5}$ concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R²) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing $P{M}_{2.5}$ levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts. Full article

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends. Full article

This research explores a new methodological framework that blends the TOPSIS (technique for order of preference by similarity to ideal solution) and Mahalanobis Distance methods, allowing for the prioritization of nine major watersheds in China based on the integration of multi-dimensional drought indicators. This integrated approach offers a robust prioritization model by accounting for spatial dependencies between indices, a feature not commonly addressed in traditional multi-criteria decision-making applications in drought studies. This study utilized three drought indices—the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Health Index (VHI), and Palmer Drought Severity Index (PDSI). Over years of significant drought prevalence, three types of droughts occurred simultaneously across various watersheds in multiple years, particularly in 2001, 2002, 2006, and 2009, with respective counts of 16, 17, 19, and 18 concurrent episodes. The weights derived from Shannon’s entropy emphasize the importance of the Potential Drought Severity Index (PDSI) in evaluating drought conditions, with PDSI-D (drought duration) assigned the highest weight of 0.267, closely followed by VHI-D (Vegetation Health Index under drought conditions) at 0.232 and SPEI-F (drought frequency) at 0.183. The results demonstrated considerable spatial variability in drought conditions across the watersheds, with Watersheds 1 and 4 exhibiting the highest drought vulnerability in terms of meteorological and agricultural droughts, while Watersheds 6 and 3 showed significant resilience to hydrological drought after 2012. In particular, the severe meteorological drought conditions at Watershed 1 highlight the urgent need for rainwater harvesting and strict water use policies, and in contrast, the conditions at Watershed 4 show the need for the modernization of irrigation to mitigate agricultural drought impacts. This integrated framework allows for targeted drought management solutions that directly relate to the specific contexts of the watersheds, while being more conducive to planning and prioritizing resource allocations for regions facing the highest drought vulnerability. Full article