Hasil untuk "Meteorology. Climatology"

A. Chatzipavlis, D. Trogu, A. Ruju et al.

<p>This study presents an Early Warning System (EWS) for coastal flooding that integrates wind, wave, and sea level forecasts which are validated using in situ records. The system employs the SWAN spectral wave model to simulate nearshore hydrodynamics while an empirical approach is used to assess Total Watel Level (TWL) exceedances over a user-defined morphological threshold, deriving from repeated topographic surveys. This approach utilizes widely used empirical methods for wave run-up estimation and makes use of the most effective one after calibration. The performance of the EWS is assessed through seven monitored surge events of varying magnitude and hydrodynamic conditions, demonstrating strong agreement between projected TWL exceedances over predefined morphological thresholds, particularly under high-energy wave conditions. Minor discrepancies are noted during events with marginal TWL exceedances over short durations. Results underline the system's potential as a valuable tool for coastal hazard assessment and risk management, with future improvements focusing on appropriate updates of the beach morphology and the integration of suitable numerical techniques and machine learning algorithms.</p>

D. Elsbury, D. Elsbury, F. Serva et al.

<p>This study uses an ensemble of climate model experiments coordinated by the Quasi-Biennial Oscillation initiative (QBOi) to analyze the Madden-Julian Oscillation (MJO) in the presence of either perpetual El Niño or La Niña sea surface temperatures during boreal winter. In addition to the prescribed El Niño Southern Oscillation (ENSO) conditions, the nine models internally generate QBOs, meaning each may influence the MJO. Objectives of our analyses are to assess the response of the MJO to strong idealized ENSO forcing and look for evidence of a QBO influence on the MJO in a multi-model context. The diagnostics used include wavenumber-frequency spectra of tropical convective and dynamical fields, measures of MJO lifetime, an evaluation of MJO diversity and visualization of MJO vertical structure, as well as an assessment of QBO morphology and the QBO's impact on tropical convection. Kelvin wave spectral power increases in the El Niño simulations whereas equatorial Rossby waves power is stronger in the La Niña simulations. All models simulate faster MJO propagation under El Niño conditions. This change in speed is<span id="page318"/> corroborated by the MJO diversity analysis, which reveals that models better reproduce the observed “fast propagating” and “standing” MJO archetypes given perpetual El Niño and La Niña, respectively. Regardless of ENSO, QBO descent into the lower stratosphere is underestimated and we detect little QBO influence on tropical tropopause stability and MJO activity. With little influence from the QBO on the MJO activity in these runs, we can be confident that the aforementioned changes in the MJO indeed arise from the different ENSO boundary conditions.</p>

Shreyas Rajeev, Karthik Mudenahalli Ashoka, Amit Mallappa Tiparaddi

Accurately forecasting long-term atmospheric variables remains a defining challenge in meteorological science due to the chaotic nature of atmospheric systems. Temperature data represents a complex superposition of deterministic cyclical climate forces and stochastic, short-term fluctuations. While planetary mechanics drive predictable seasonal periodicities, rapid meteorological changes such as thermal variations, pressure anomalies, and humidity shifts introduce nonlinear volatilities that defy simple extrapolation. Historically, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model has been the standard for modeling historical weather data, prized for capturing linear seasonal trends. However, SARIMA operates under strict assumptions of stationarity, failing to capture abrupt, nonlinear transitions. This leads to systematic residual errors, manifesting as the under-prediction of sudden spikes or the over-smoothing of declines. Conversely, Deep Learning paradigms, specifically Long Short-Term Memory (LSTM) networks, demonstrate exceptional efficacy in handling intricate time-series data. By utilizing memory gates, LSTMs learn complex nonlinear dependencies. Yet, LSTMs face instability in open-loop forecasting; without ground truth feedback, minor deviations compound recursively, causing divergence. To resolve these limitations, we propose a Hybrid SARIMA-LSTM architecture. This framework employs a residual-learning strategy to decompose temperature into a predictable climate component and a nonlinear weather component. The SARIMA unit models the robust, long-term seasonal trend, while the LSTM is trained exclusively on the residuals the nonlinear errors SARIMA fails to capture. By fusing statistical stability with neural plasticity, this hybrid approach minimizes error propagation and enhances long-horizon accuracy.

Eamonn Organ, Maeve Upton, Denis Allard et al.

Accurate estimates of wind speeds at wind turbine hub heights are crucial for both wind resource assessment and day-to-day management of electricity grids with high renewable penetration. In the absence of direct measurements, parametric models are commonly used to extrapolate wind speeds from observed heights to turbine heights. Recent literature has proposed extensions to allow for spatially or temporally varying vertical wind gradients, that is, the rate at which wind speed changes with height. However, these approaches typically assume that reference height and hub height measurements are available at the same locations, which limits their applicability in operational settings where meteorological stations and wind farms are spatially separated. In this paper, we develop a two-step spatio-temporal framework to estimate turbine height wind speeds using only open-access observations from sparse meteorological stations. First, a non-parametric generalized additive model is trained on reanalysis data to perform vertical height extrapolation. Second, a spatial Gaussian process model interpolates these hub-height estimates to wind farm locations while explicitly propagating uncertainty from the height extrapolation stage. The proposed framework enables the construction of high-resolution, sub-hourly turbine-height wind speed time series and spatial wind maps using data available in real time, capabilities not provided by existing reanalysis products. We further provide calibrated uncertainty estimates that account for both vertical extrapolation and spatial interpolation errors. The approach is validated using hub-height measurements from seven operational wind farms in Ireland, demonstrating improved accuracy relative to ERA5 reanalysis while relying solely on real-time, open-access data.

Yong Chang, Nan Mu, Yaoyong Qi et al.

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets.

Jiangang Wang, Jiaqi Du, Kai Jin et al.

The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m³/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application.

Devabrat Sharma, Santu Das, B. N. Goswami

Zhixin Geng, Xu Fan, Xiqiao Lu et al.

Air pollution has emerged as a major public health challenge in megacities. Numerical simulations and single-site machine learning approaches have been widely applied in air quality forecasting tasks. However, these methods face multiple limitations, including high computational costs, low operational efficiency, and limited integration with observational data. With the rapid advancement of artificial intelligence, there is an urgent need to develop a low-cost, efficient air quality forecasting model for smart urban management. An air quality forecasting model, named FuXi-Air, has been constructed in this study based on multimodal data fusion to support high-precision air quality forecasting and operated in typical megacities. The model integrates meteorological forecasts, emission inventories, and pollutant monitoring data under the guidance of air pollution mechanism. By combining an autoregressive prediction framework with a frame interpolation strategy, the model successfully completes 72-hour forecasts for six major air pollutants at an hourly resolution across multiple monitoring sites within 25-30 seconds. In terms of both computational efficiency and forecasting accuracy, it outperforms the mainstream numerical air quality models in operational forecasting work. Ablation experiments concerning key influencing factors show that although meteorological data contribute more to model accuracy than emission inventories do, the integration of multimodal data significantly improves forecasting precision and ensures that reliable predictions are obtained under differing pollution mechanisms across megacities. This study provides both a technical reference and a practical example for applying multimodal data-driven models to air quality forecasting and offers new insights into building hybrid forecasting systems to support air pollution risk warning in smart city management.

Yangze Zhou, Guoxin Lin, Gonghao Zhang et al.

Meteorological factors (MF) are crucial in day-ahead load forecasting as they significantly influence the electricity consumption behaviors of consumers. Numerous studies have incorporated MF into the load forecasting model to achieve higher accuracy. Selecting MF from one representative location or the averaged MF as the inputs of the forecasting model is a common practice. However, the difference in MF collected in various locations within a region may be significant, which poses a challenge in selecting the appropriate MF from numerous locations. A representation learning framework is proposed to extract geo-distributed MF while considering their spatial relationships. In addition, this paper employs the Shapley value in the graph-based model to reveal connections between MF collected in different locations and loads. To reduce the computational complexity of calculating the Shapley value, an acceleration method is adopted based on Monte Carlo sampling and weighted linear regression. Experiments on two real-world datasets demonstrate that the proposed method improves the day-ahead forecasting accuracy, especially in extreme scenarios such as the "accumulation temperature effect" in summer and "sudden temperature change" in winter. We also find a significant correlation between the importance of MF in different locations and the corresponding area's GDP and mainstay industry.

Roy Elkayam

Accurate prediction of effluent temperature in recharge basins is essential for optimizing the Soil Aquifer Treatment (SAT) process, as temperature directly influences water viscosity and infiltration rates. This study develops and evaluates predictive models for effluent temperature in the upper recharge layer of a Shafdan SAT system recharge basin using ambient meteorological data. Multiple linear regression (MLR), neural networks (NN), and random forests (RF) were tested for their predictive accuracy and interpretability. The MLR model, preferred for its operational simplicity and robust performance, achieved high predictive accuracy (R2 = 0.86-0.87) and was used to estimate effluent temperatures over a 10-year period. Results highlight pronounced seasonal temperature cycles and the importance of topsoil temperature in governing the thermal profile of the infiltrating effluent. The study provides practical equations for real-time monitoring and long-term planning of SAT operations.

Hugo FM Milan, Aline Q Alves, Thatiane AT Souza et al.

Performance ratio (PR) is a established measure of efficiency of photovoltaic (PV) systems. While previous research demonstrated the effects of meteorological and technical variables on PR, a gap persists in the literature on which variables strongly influence PR in large-scale, real-world, heterogeneous PV systems. This paper aims to fill this gap, applying data-driven models to PV systems located in Rondônia State, Brazil, to identify which variables strongly influence annual PR, and, hence, should be the target for optimization. Surprisingly, only negligible effects were found between meteorological and technical variables on annual PR, indicating that human-factors (such as installation, monitoring, and maintenance quality) might have a stronger effect. These findings indicates that, to improve performance of PV systems, policy makers could focus on creating educational programs to teach PV installers and technicians how to properly install, monitor, and maintain modern PV systems. Through estimating the probability density functions of PR, its peak value was found as 78.85% (mean 77.52%, 95% confidence interval of 76.12% to 78.84%, and 95% prediction interval of 58.83% to 92.70%). A map of annual final yield was developed for Rondônia State and can be used by entrepreneurs to quickly and cheaply estimate energy production.

Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO₂ emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

Tim Hill, Gwenn Elizabeth Flowers, Matthew James Hoffman et al.

Subglacial hydrology models struggle to reproduce seasonal drainage patterns that are consistent with observed subglacial water pressures and surface velocities. We modify the standard sheet-flow parameterization within a coupled sheet–channel subglacial drainage model to smoothly transition between laminar and turbulent flow based on the locally computed Reynolds number in a physically consistent way (the ‘transition’ model). We compare the transition model to standard laminar and turbulent models to assess the role of the sheet-flow parameterization in reconciling observed and modelled water pressures under idealized and realistic forcing. Relative to the turbulent model, the laminar and transition models improve seasonal simulations by increasing winter water pressure and producing a more prominent late-summer water pressure minimum. In contrast to the laminar model, the transition model remains consistent with its own internal assumptions across all flow regimes. Based on the internal consistency of the transition model and its improved performance relative to the standard turbulent model, we recommend its use for transient simulations of subglacial drainage.

Dominic Clements, Matthew Coburn, Simon J. Cox et al.

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM_2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM_2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM_2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM_2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM_2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

Stephen Jun Villejo, Sara Martino, Finn Lindgren et al.

This work aims to combine two primary meteorological data sources in the Philippines: data from a sparse network of weather stations and outcomes of a numerical weather prediction model. To this end, we propose a data fusion model which is primarily motivated by the problem of sparsity in the observational data and the use of a numerical prediction model as an additional data source in order to obtain better predictions for the variables of interest. The proposed data fusion model assumes that the different data sources are error-prone realizations of a common latent process. The outcomes from the weather stations follow the classical error model while the outcomes of the numerical weather prediction model involves a constant multiplicative bias parameter and an additive bias which is spatially-structured and time-varying. We use a Bayesian model averaging approach with the integrated nested Laplace approximation (INLA) for doing inference. The proposed data fusion model outperforms the stations-only model and the regression calibration approach, when assessed using leave-group-out cross-validation (LGOCV). We assess the benefits of data fusion and evaluate the accuracy of predictions and parameter estimation through a simulation study. The results show that the proposed data fusion model generally gives better predictions compared to the stations-only approach especially with sparse observational data.

Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon et al.

The impact of meteorological observations on weather forecasting varies with sensor type, location, time, and other environmental factors. Thus, quantitative analysis of observation impacts is crucial for effective and efficient development of weather forecasting systems. However, the existing impact analysis methods are difficult to be widely applied due to their high dependencies on specific forecasting systems. Also, they cannot provide observation impacts at multiple spatio-temporal scales, only global impacts of observation types. To address these issues, we present a novel system called ``CloudNine,'' which allows analysis of individual observations' impacts on specific predictions based on explainable graph neural networks (XGNNs). Combining an XGNN-based atmospheric state estimation model with a numerical weather prediction model, we provide a web application to search for observations in the 3D space of the Earth system and to visualize the impact of individual observations on predictions in specific spatial regions and time periods.

Gilbert Green, Naomi Watanabe

Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the discharges were analyzed by merging the video images with lightning flash data from the National Detection Lightning Network (NLDN) and the Geostationary Lightning Mapper (GLM). Spider lightning discharges primarily occurred during the later stages of the overall lightning activity when there was a decrease in the flash count and flash locations were drifting apart. The propagation path of the spider discharge was predominantly luminous and exhibited an extended duration, ranging from 300 ms to 1720 ms, with most of the path remaining continuously illuminated. Occasionally, observed discharges produced cloud-to-ground flashes (CG) along their propagation paths. This study represents the first attempt to utilize video images, NLDN, and GLM data to investigate the correlation between visual observed spider lightning events and detection networks. These combined datasets facilitated the characterization of the observed spider lightning discharges.

Darwin Raudez-Centeno

Antecedentes: Las hortalizas representan parte importante de la economía de nuestro país, son rubros de alto riesgo, dado su alto costo productivo y su inestabilidad de mercado. Este estudio se focalizo en evaluar la sustentabilidad de 33 fincas comparándolos con sistemas de manejos alternativos. Metodología: la evaluación se realizó aplicando el marco para la evaluación de sistemas de manejo de recursos naturales incorporando indicadores, MESMIS, compuestos por las variables productividad, estabilidad, adaptabilidad, equidad y autodependencia, los datos se recopilaron mediante la aplicación de una encuesta semiestructurada, en la cual se midieron 19 indicadores de sustentabilidad. Resultados: los resultados evidencian que de manera global las 33 fincas de los asociados, reflejaron porcentajes entre los 29 y los 59% de puntajes, indica que son sistemas con transición hacia la sustentabilidad o medianamente sustentables. Los indicadores que marcaron los mayores puntajes fueron, rendimientos (50%), mano de obra (50%), integración de nuevas prácticas agrícolas (50%), frecuencia de capacitaciones (48%), relación con las personas, igualdad de género y bajo endeudamiento. Dentro de los indicadores más bajos de estos sistemas hortícolas encontramos, conservación de suelos y aguas (10%), cargos desempeñados en la organización de la cooperativa (15%), precios justos (15%) y alta dependencia de insumos (10%). Conclusión: en la comparación de indicadores entre los sistemas de los socios de la cooperativa con un sistema de referencia encontramos que todos los indicadores están entre el 37.5% al 80%, únicamente superados los sistemas alternativos por el de referencia en el indicador de organización.  

Guillaume Jouvet, Denis Cohen, Emmanuele Russo et al.

Our limited knowledge of the climate prevailing over Europe during former glaciations is the main obstacle to reconstruct the past evolution of the ice coverage over the Alps by numerical modelling. To address this challenge, we perform a two-step modelling approach: First, a regional climate model is used to downscale the time slice simulations of a global earth system model in high resolution, leading to climate snapshots during the Last Glacial Maximum (LGM) and the Marine Isotope Stage 4 (MIS4). Second, we combine these snapshots and a climate signal proxy to build a transient climate over the last glacial period and force the Parallel Ice Sheet Model to simulate the dynamical evolution of glaciers in the Alps. The results show that the extent of modelled glaciers during the LGM agrees with several independent key geological imprints, including moraine-based maximal reconstructed glacial extents, known ice transfluences and trajectories of erratic boulders of known origin and deposition. Our results highlight the benefit of multiphysical coupled climate and glacier transient modelling over simpler approaches to help reconstruct paleo glacier fluctuations in agreement with traces they have left on the landscape.

Stefan Noll, Carsten Schmidt, Wolfgang Kausch et al.

The nocturnal mesopause region of the Earth's atmosphere radiates chemiluminescent emission from various roto-vibrational bands of hydroxyl (OH), which is therefore a good tracer of the chemistry and dynamics at the emission altitudes. Intensity variations can, e.g., be caused by the general circulation, gravity waves, tides, planetary waves, and the solar activity. While the basic OH response to the different dynamical influences has been studied quite frequently, detailed comparisons of the various individual lines are still rare. Such studies can improve our understanding of the OH-related variations as each line shows a different emission profile. We have therefore used about 90,000 spectra of the X-shooter spectrograph of the Very Large Telescope at Cerro Paranal in Chile in order to study 10 years of variations of 298 OH lines. The analysis focuses on climatologies of intensity, solar cycle effect, and residual variability (especially with respect to time scales of hours and about 2 days) for day of year and local time. For a better understanding of the resulting variability patterns and the line-specific differences, we applied decomposition techniques, studied the variability depending on time scale, and calculated correlations. As a result, the mixing of thermalized and nonthermalized OH level populations clearly influences the amplitude of the variations. Moreover, the local times of the variability features shift depending on the effective line emission height, which can mainly be explained by the propagation of the migrating diurnal tide. This behavior also contributes to remarkable differences in the effective solar cycle effect.