Search Results (119)

Large-scale climatic oscillations (COs) modulate extreme climate events (ECEs) globally and can trigger the Indian summer monsoons and associated ECEs. In this study, we introduced a Time-dependent Intrinsic Correlation (TDIC) analysis to quantify teleconnections between five major COs—the El Niño–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO)—and multiple extreme climate indices (ECIs) over the southern Indian Peninsula. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to decompose COs and ECIs into intrinsic mode functions across varying timescales, enabling a dynamic TDIC assessment. The results revealed statistically significant correlations between COs and ECIs, with the strongest influences in low-frequency modes (>10 years). Distinct COs predominantly modulate specific ECIs (e.g., ENSO with monsoon rainfall extremes; AMO and PDO with temperature extremes). These findings advance the understanding of Indian climate system dynamics and support the development of improved ECE forecasting models. Full article

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values. Full article

Agriculture is a major source of greenhouse gas emissions, yet predicting temperature increases associated with specific CO₂ sources remains challenging due to the heterogeneity of agri-environmental systems. In response, this study presents a machine learning framework that adopts an agri-food system boundary (production to retail) and combines systematic model benchmarking, interpretability, and a multi-scale perspective. Seven regression models, including tree ensembles and deep learning architectures, are evaluated on a harmonized dataset covering 236 countries over the 1990–2020 period to forecast annual temperature increases. Results show that gradient-boosted decision trees consistently outperform deep learning models in predictive accuracy and offer more stable feature attributions. Interpretability analysis reveals that spatio-temporal variables are the dominant drivers of global temperature variation, while environmental and sector-specific factors play more localized roles. A country-level case study on Iran illustrates how the framework captures national deviations from global patterns, highlighting intensive rice cultivation and on-farm energy use as key influential factors. By integrating high-performance predictions with interpretable insights, the proposed framework supports the design of both global and country-specific climate mitigation strategies. Full article

Accurate prediction of photovoltaic power generation is a pivotal factor for enhancing the operational efficiency of electrical grids and facilitating the stable integration of solar energy. This study introduces a holistic forecasting framework that achieves seamless integration of dual-stage decomposition, deep learning architectures, and an advanced metaheuristic algorithm, thereby significantly improving the prediction precision of PV power generation. Initially, the raw PV power sequences are processed using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to capture multi-scale temporal characteristics. The derived components are subsequently categorized into high-, medium-, and low-frequency groups through K-means clustering to manage complexity. To address residual noise and non-stationary behaviors, the high-frequency constituents are further decomposed via Variational Mode Decomposition (VMD). The refined subsequences are then input into a TCN_BiGRU_Attention network, which employs temporal convolutional operations for hierarchical feature extraction, bidirectional gated recurrent units to model temporal correlations, and a multi-head attention mechanism to prioritize influential time steps. For hyperparameter optimization of the forecasting model, an Improved Crested Porcupine Optimizer (ICPO) is developed, integrating Chebyshev chaotic mapping for initialization, a triangular wandering strategy for local search, and Lévy flight to strengthen global exploration and accelerate convergence. Validation on real-world PV datasets indicates that the proposed model attains a Mean Squared Error (MSE) of 0.3456, Root Mean Squared Error (RMSE) of 0.5879, Mean Absolute Error (MAE) of 0.3396, and a determination coefficient (R²) of 99.59%, surpassing all benchmark models by a significant margin. This research empirically demonstrates the efficacy of the dual decomposition methodology coupled with the optimized hybrid deep learning network in elevating both the accuracy and stability of predictions, thereby offering a reliable and stable forecasting framework for PV power systems. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

Tropospheric delay is recognized as one of the main error sources affecting Global Navigation Satellite System (GNSS) positioning accuracy. Previous studies have only employed artificial intelligence-based weather models with low temporal resolution for comprehensive assessments. Therefore, this study proposes an ensemble forecasting approach based on multiple initial conditions from the Pangu-Weather model to obtain hourly resolution tropospheric delays. The ZTD data from 250 Crustal Movement Observation Network of China (CMONOC) GNSS stations across China in 2020 are used to validate the accuracy of the Pangu-Weather model. The findings show that the Pangu-Weather model exhibits strong performance under both forecast lead times compared to the traditional Global Forecast System (GFS) product, particularly in southern China. However, the Pangu-Weather model provides slightly inferior forecast accuracy compared to the GFS product in dry, low-humidity regions at stations located between 2 and 4 km in altitude, and for forecast lead times of less than 9 h. Nevertheless, a lower error accumulation trend is exhibited by the Pangu-Weather model, as its RMSE is larger than that of the Global Pressure and Temperature 3 (GPT3) empirical model after 240 h (10 days), demonstrating more stable accuracy over longer forecast periods. In summary, the Pangu-Weather model shows significant advantages in Chinese regions with complex climates and terrains, and it is of great potential in GNSS real-time positioning and meteorological monitoring. Full article

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

In an ensemble prediction system, each member performs differently from each other for individual cases. To adaptively (not only statistically) calibrate or post-process raw ensemble forecasts and produce more reliable and accurate forecast products case by case, it is necessary to understand how individual ensemble members behave inside an ensemble cloud. For example, how (randomly or orderly) does an individual member’s relative performance (including the best and worst members) vary with location and time? To quantify and understand these variations, this study proposes the “Performance Rank Variation Score (PRVS)” to measure the degree of ensemble member’s relative performance variation (the “motion” of members). The PRVS was applied to four real cases (representing the winter, spring, summer, and fall seasons) from the NCEP global ensemble forecast system (GEFS). Many interesting results were observed, which are otherwise hard to elucidate without this new score. At the same time, based on the revealed results, possible ensemble post-processing strategies are discussed for future developments, where a new concept of “transformed ensemble” was demonstrated as an example. Full article

As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems. Full article

This paper explores the potential and implications arising from the convergence of virtual reality, the metaverse, and digital twins in translating a real-world commemorative event into a virtual environment. It emphasizes how such integration influences digital transformation processes, particularly in reshaping models of social interaction. Virtual reality is conceptualized as an immersive technology, enabling advanced multisensory experiences within persistent virtual spaces, such as the metaverse. Furthermore, this study delves into the concept of digital twins—high-fidelity virtual representations of physical systems, processes, and objects—highlighting their application in simulation, analysis, forecasting, prevention, and operational enhancement. In the context of virtual events, the convergence of these technologies is examined as a means to create interactive, adaptable, and scalable environments capable of accommodating diverse social groups and facilitating global accessibility. As a practical application, a digital twin of the Ferrándiz and Carbonell buildings—the most iconic architectural ensemble on the Alcoi campus—was developed to host a virtual event commemorating the 50th anniversary of the integration of the Alcoi School of Industrial Technical Engineering into the Universitat Politècnica de València in 1972. The virtual environment was subsequently evaluated by a sample of users, including students and faculty, to assess usability and functionality, and to identify areas for improvement. The digital twin achieved a score of 88.39 out of 100 on the System Usability Scale (SUS). The findings underscore the key opportunities and challenges associated with the adoption of these emerging technologies, particularly regarding their adaptability in reconfiguring digital environments for work, social interaction, and education. Using this case study as a foundation, this paper offers insights into the strategic role of the metaverse in extending environmental perception and its transformative potential for the future digital ecosystem through the implementation of digital twins. Full article

Air pollution poses a significant global challenge, impacting human health and environmental sustainability worldwide. Accurate air quality forecasting is essential for effective mitigation strategies, particularly in rapidly urbanizing regions. This study focuses on Shenyang, China, as a representative case to analyze air quality dynamics and develop a high-precision forecasting tool. Using a comprehensive six-year dataset (2020–2025) of daily air quality and meteorological measurements, a rigorous preprocessing pipeline was applied to ensure data integrity. Five gradient-boosted decision-tree models were trained and combined through a ridge-regularized stacking ensemble to enhance the predictive accuracy. The ensemble achieved an R² of 94.17% and a mean absolute percentage error of 7.79%, outperforming individual models. The feature importance analysis revealed that ozone, PM₁₀, and PM_2.5 concentrations are the dominant drivers of daily air quality fluctuations. The resulting forecasting system delivers robust, interpretable predictions across seasonal variations, offering a valuable decision support tool for urban air quality management. This framework demonstrates how advanced machine learning techniques can be applied in a Chinese urban context to inform global air pollution mitigation efforts. Full article

This study explores the historical evolution and short-term predictive modeling of the U.S. 10-year Treasury bond yield, a critical indicator in global financial markets. Recognizing its sensitivity to macroeconomic conditions, the research integrates economic variables, including the federal funds rate, core Consumer Price Index (CPI), real Gross Domestic Product (GDP) growth rate, and the U.S. federal debt growth rate, to assess their influence on yield movements. Four forecasting models are employed for comparative analysis: linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural networks. Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R² value of 0.5760. The results highlight the superiority of ensemble-based nonlinear models in capturing complex interactions between economic indicators and yield dynamics. This research not only provides empirical support for using machine learning in economic forecasting but also offers practical implications for bond traders, system developers, and financial institutions aiming to enhance predictive accuracy and risk management. Full article