Numerical Prediction Research Articles

Machine Learning based Wildfire Area Estimation Leveraging Weather Forecast Data

Wildfires are increasingly destructive natural disasters, annually consuming millions of acres of forests and vegetation globally. The complex interactions among fuels, topography, and meteorological factors, including temperature, precipitation, humidity, and wind, govern wildfire ignition and spread. This research presents a framework that integrates satellite remote sensing and numerical weather prediction model data to refine estimations of final wildfire sizes. A key strength of our approach is the use of comprehensive geospatial datasets from the IBM PAIRS platform, which provides a robust foundation for our predictions. We implement machine learning techniques through the AutoGluon automated machine learning toolkit to determine the optimal model for burned area prediction. AutoGluon automates the process of feature engineering, model selection, and hyperparameter tuning, evaluating a diverse range of algorithms, including neural networks, gradient boosting, and ensemble methods, to identify the most effective predictor for wildfire area estimation. The system features an intuitive interface developed in Gradio, which allows the incorporation of key input parameters, such as vegetation indices and weather variables, to customize wildfire projections. Interactive Plotly visualizations categorize the predicted fire severity levels across regions. This study demonstrates the value of synergizing Earth observations from spaceborne instruments and forecast data from numerical models to strengthen real-time wildfire monitoring and postfire impact assessment capabilities for improved disaster management. We optimize an ensemble model by comparing various algorithms to minimize the root mean squared error between the predicted and actual burned areas, achieving improved predictive performance over any individual model. The final metric reveals that our optimized WeightedEnsemble model achieved a root mean squared error (RMSE) of 1.564 km2 on the test data, indicating an average deviation of approximately 1.2 km2 in the predictions.

Enhancing Biomass Productivity by Forecast-Informed Pond Operations.

Microalgal cultivation for biofuels and proteins holds significant promise but faces challenges in achieving economically viable biomass productivity under variable environmental conditions. This study introduces a forecast-informed pond operation (FIPO) system that uses numerical weather prediction (NWP) ensemble forecasts and the biomass assessment tool (BAT) to optimize daily dilution rates for enhanced biomass production. In contrast to the current practice, where fixed dilution rates are based on operator experience, the FIPO system determines the optimal dilution rate based on future weather forecasts and biomass growth conditions. Our experiments validate the effectiveness of FIPO in both short- and long-term growth scenarios. In short-term experiments, FIPO increased biomass production by 21.3% compared to batch growth and 7.4% over fixed dilution (60% every 3 days) operations. The NWP forecast-informed operations achieved biomass production nearly identical to that using perfect weather forecasts, highlighting the accuracy of current NWP forecasts for guiding pond operations. In long-term experiments, FIPO resulted in biomass production increases of 13.3% and 17.8% compared to two fixed dilution rates (60% every 3 days and 20% daily). These findings underscore the viability of using NWP forecasts to optimize microalgal cultivation systems. By adjusting daily dilution rates in response to forecasted weather, operators can achieve higher biomass yields and mitigate risks associated with environmental variability. This study provides a foundation for future research and practical applications in commercial-scale microalgal production.

Gene-Based Model To Predict Heading Date In Wheat Based On Allelic Characterization And Environmental Drivers.

While numerous wheat phenology prediction models are available, most of them are constrained to using variety-dependent coefficients. The overarching objective of this study was to calibrate a gene-based model to predict wheat heading date that allows breeders to select specific gene combinations that would head within the optimal window for a given environment independently of varietal genetic background. A dataset with a total of 49 Argentine wheat cultivars and two recombinant inbred lines was chosen to cover a wide range of allelic combinations for major vernalization, photoperiod, and earliness per-se genes. The model was validated using independent data from an Argentine wheat trial network that includes sites from a wide latitudinal range. Ultimately, using this gene-based model, simulations were made to identify optimal gene combinations (ideotypes) × site combinations in contrasting locations. The selected model accurately predicted heading date with an overall median error of 4.6 days. This gene-based crop model for wheat phenology allowed the identification of groups of gene combinations predicted to head within a low-risk window and can be adapted to predict other phenological stages based on accessible climatic information and publicly available molecular markers, facilitating its adoption in wheat-growing regions worldwide.

Developed a solar still unit for saltwater desalination: numerical prediction and performance verification

In the globe, there is a rise in water demand for agricultural, industrial, and domestic purposes. Single-basin solar stills (SBSS) have been a subject of research in various countries, particularly in regions with water scarcity or limited access to clean drinking water. In this work, SBSS for desalinating high-salinity water were developed, tested, and evaluated based on a developed numerical model using MATLAB R2021a program to predict the best productivity through the best selection of raw materials used to develop the SBSS. A four-inclined SBSS was fabricated and examined experimentally according to numerical model findings for best design parameters at Marsa Matrouh, 31° 21′ 10.44″N, 27°14′14.10″ E, Agricultural Station—Agricultural Research Center (ARC), Egypt. The hourly experimental results are compared with the numerical results. A good correlation between the numerical and the experimental results with variations in water, and glass temperatures of 9, and 18% respectively, and a variation in cumulative productivity by 11%. The results clearly showed that instantaneous productivity increases by decreasing water depth to 10 mm and using the SBSS unit partially insulated from the bottom of the basin. Adding insulation in front of the sides and back of tempered glass increases the shading area and decreases water temperature hence the cumulative productivity by 15%. The cumulative productivity reached 3 L for the SBSS unit partially insulated from the bottom of the basin with an area of 0.6 m2 for only 12 h working system at a water depth of 10 mm.

Swirling Instability Mediated by Elastic and Hydrodynamic Couplings in Cytoplasmic Streaming

Cytoplasmic streaming is driven by molecular motors that move along the cytoskeleton and entrain the surrounding fluid. In certain cell types, the direction of cytoplasmic streaming is not predefined but rather stochastic. For example, in meiotic cytoplasmic streaming of zygotes, the direction of the swirl reverses over time [Kimura , ]. While a mean-field theory explained the reversal, the structure of flows in three-dimensional space and the mechanical role of the endoplasmic reticulum (ER) remains unknown. To test the hypothesis that the elastic ER bonds and hydrodynamic interactions between microtubules mediate the orientational order of microtubules, leading to directional cytoplasmic streaming, we computationally investigated fluid-structure interactions in cytoplasmic flow, ER networks, and microtubule structures in the presence of microtubule dynamic instability. The results indicate that the occasionally reversing swirls emerge within a certain range of ER elasticity, and our experimental measurements of the ER elasticity agree with the numerical predictions. Mass transport analysis further demonstrates that the reversing swirls are suitable for intracellular particle mixing. These findings illustrate the delicate balance of meiotic cytoplasmic streaming and provide insights into the physiology of the embryogenesis. Published by the American Physical Society 2025

Inclusion of tumor periphery in radiomics analysis of magnetic resonance images does not improve predictionsof preoperative therapy response in patients with rectal cancer.

To evaluate the advantages of including versus excluding the tumor periphery and combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2w) for outcome predictions of preoperative radio(chemo)therapy in rectal cancer. Four analysis strategies, based on two segmentation methods and two magnetic resonance imaging (MRI) sequences, were evaluated in 106 patients examined with pretreatment MRI. One segmentation method included the tumor periphery in the region of interest (ROI) encompassing the whole tumor (wROI), considered as the reference segmentation approach, and one included only the central part (cROI). Relevant radiomics imaging features were extracted from either T2w alone or from both T2w and DWI and used by a machine learning algorithm for the prediction of pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and disease recurrence. The area under the curve (AUC) was the performance measure. AUCs were compared with a bootstrapping method based on 104 bootstraps. cROI applied to both T2w and DWI provided the highest numerical prediction of pCR (AUC 0.76), however, not significantly superior to the other strategies (p ≥ 0.138). cROI applied to both T2w and DWI also yielded the highest numerical prediction of NAR score (AUC 0.84), showing advantages over wROI-based analysis strategies (AUC 0.66 and 0.69; p ≤ 0.008). When compared to cROI applied to T2w alone (AUC 0.73), the benefit was borderline statistically significant (p = 0.053). For prediction of disease recurrence, no differences were found between the analysis strategies. Inclusion of the tumor periphery in radiomic analysis of magnetic resonance images does not improve predictions of the preoperative therapy response in patients with rectal cancer. Excluding tumor periphery while adding DWI to T2w improves prediction of the NAR score, although it does not affect pCR or recurrence prediction.

The Impact of Capillary Heterogeneity on CO 2 plume migration at the Endurance CO 2 storage Site in the UK

Predictive modelling of subsurface CO,- 2. flow is used for optimising the design of geological CO,- 2. storage projects e.g. with respect to mass stored and long term security. However several field scale projects have reported plume dynamics that do not match numerical predictions. Previous work has indicated that upscaling capillary heterogeneity results in different plume dynamics in synthetic 2D cross-sections and at early times (<0.2PV injected). Here we extend the workflow to 3D and analyse the impacts of longer term flow behaviour in an industrial scale geological carbon storage site with multi-point CO ,-2 . injection, structural relief and realistic flow rates. The models reveal that capillary heterogeneity in horizontally layered lithologies enhances the formation of preferential flow paths, increases swept volumes and areas, and reduces vertical migration speed near the injectors, all of which improve storage efficiency. At distances far from the injectors, the plume travels faster as a result of the heterogeneity. The results also show that simulations in 3D have qualitatively distinct results from those in 2D due to the increase in flow pathways.

The synergy of assimilating visible and infrared radiances and radar observations

AbstractCloud‐affected satellite observations in the visible and infrared spectrum contain vast and largely complementary information on clouds and atmospheric convection and thereby constitute a promising data source for convective‐scale data assimilation. Preceding studies have demonstrated that the assimilation of either one of these observation types can lead to improved convective‐scale weather forecasts, but research on the combined assimilation of cloud‐affected visible and infrared satellite data as well as radar observations is still very scarce. In this article, we investigate the combined assimilation of multiple infrared and visible satellite channels as well as radar observations, and evaluate their analysis and forecast impact with a primary focus on clouds and precipitation. We assimilate visible (0.6‐) and thermal infrared (6.2‐ and 7.3‐ ) satellite channels in observing‐system simulation experiments (OSSEs) with a perfect‐model forecast for an idealized weather scenario. Observations are simulated synthetically and assimilated by the ensemble adjustment Kalman filter (EAKF). The forecasts used the Weather Research and Forecasting (WRF) model at 2‐km grid resolution. Results show that assimilating satellite channels in addition to radar reflectivity can improve forecasts of cloudiness and precipitation, while improvements in temperature, humidity, and wind fields are about the same as in the radar experiment. The evaluation of the analysis error for different cloud conditions revealed that the combined assimilation can mitigate the ambiguity of individual visible and infrared channels. Assimilating visible reflectance before infrared brightness temperature can remove pre‐existing erroneous water clouds and avoid the introduction of erroneous clouds by the following assimilation of infrared channels. It follows that the combined assimilation of visible and infrared radiances could be crucial to avoid shortcomings of assimilating only visible or infrared radiances in convective‐scale numerical weather prediction.

Numerical simulation and prediction of fast pyrolysis behavior of biomass pellet based on the coupling of heat and mass transfer characteristics and component reaction kinetics

Numerical simulation and prediction of fast pyrolysis behavior of biomass pellet based on the coupling of heat and mass transfer characteristics and component reaction kinetics

Characterizing Model Uncertainties in Simulated Coast-to-Offshore Wind over the Northeast U.S. Using Multi-platform Measurements from the TCAP Field Campaign

Numerical weather prediction models, such as the Weather Research and Forecasting model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multiscale physical processes. Here we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast United States by varying sea surface temperature (SST) forcings and surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project experiment, are used to constrain the model results, and facilitate quantitative comparisons. Our analysis indicates that while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in the wind speed’s spatial correlation, which underscores the impact of initial and boundary conditions on simulated winds. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the third Wind Forecast Improvement Project.

Convection-Permitting Ensembles of an Isolated Mountain Thunderstorm during RELAMPAGO/CACTI

Abstract The north–south-oriented Sierras de Córdoba (SDC) ridge in central Argentina is noted for initiating thunderstorms that may grow into intense mesoscale convective systems (MCSs). It also initiates more isolated, shorter-lived cells under weaker synoptic forcing. These cells are less impactful than MCSs but may be difficult to predict in convective-scale numerical weather prediction (NWP) due to their strong sensitivities to subgrid and partially resolved processes. To study the mechanisms and predictability of such cells, convection-permitting ensemble simulations were conducted of an isolated, diurnally forced SDC thunderstorm during Cloud, Aerosol, and Complex Terrain Interactions (CACTI)/Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO). The rich observational data facilitated detailed ensemble verification, where dry biases in the surface energy balance and soil moisture were identified. These biases promoted rapid removal of convective inhibition and an early onset of precipitating cells over the SDC that were shallower and weaker than the observed cell. Correction, and then overcorrection, of the soil moisture bias in two successive ensembles was required to rectify the surface energy balance and improve the representation of the SDC cell. Nevertheless, substantial ensemble variability in convective precipitation was found, with some members producing more widespread convection than observed and others producing no deep convection at all. This variability was largely explained by a combination of thermodynamic and dynamic mechanisms, dominated by a positive sensitivity of convective precipitation to preconvective moist instability over the ridge. Secondary sensitivities were found to low-level upward mass flux and midlevel cross-barrier winds, the latter of which caused gravity waves with elevated downdrafts that tended to suppress incipient clouds.

Effect of projectile nose shape on ballistic resistance of multi-layered explosively welded plates

In the present study, the ballistic perforation resistance of steel/titanium/aluminum (STA) multilayer protective systems impacted by spherical, ogival, conical, and blunt projectiles was investigated experimentally, numerically, and analytically. The targets were manufactured via explosive welding technique to achieve a strong interfacial strength. The projectile nose shape was found to significantly affect the failure modes and ballistic limit velocities of the STA composite plate. Detailed three-dimensional finite element simulations were performed to provide insights into the penetration process and energy absorption characteristics of the STA composite plate. An analytical model was developed to predict the entry and exit penetration phases of a rigid projectile of different nose shapes into the STA target through ductile hole expansion. The model simplified the STA composite plate to be an equivalent monolithic based on the weighting of material resistance and specific cavitation energy in each layer. The analytical and numerical predictions of the residual velocity were in excellent agreement with the experimental data. The predicted evolution of projectile velocity with penetration depth was found to be in satisfactory correlation with those from the numerical simulation. The proposed analytical model shall be useful for designers of multilayer metallic protective structures against fragments from improvised explosive devices.

Numerical prediction of strength and temperature changes within cemented paste backfill considering barricade stability

Numerical prediction of strength and temperature changes within cemented paste backfill considering barricade stability

Numerical Prediction of Fracture and Perforation Behaviours of Recycled Aluminium Alloy AA6061 Using Taylor Cylinder Impact and Perforation Tests

Numerical Prediction of Fracture and Perforation Behaviours of Recycled Aluminium Alloy AA6061 Using Taylor Cylinder Impact and Perforation Tests

Modeling and Evaluation of Forecasting Models for Energy Production in Wind and Photovoltaic Systems

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.

Characterisation and collapse analysis of composite egg-shaped pressure housings: experimental and numerical insights

ABSTRACT This study examined the collapse performance of composite egg-shaped pressure housings made of carbon fiber reinforced plastic (CFRP) layers and a nylon liner, fabricated using filament winding and 3D printing. Two types of housings—nylon and composite—were designed, fabricated, tested, and analyzed, with experimental results aligning with numerical predictions. The composite housings had an average ultimate strength of 5.98 MPa, 6.10 times greater than the nylon housings. Matrix compression damage was observed in the CFRP layer before reaching the ultimate strength, starting from the innermost CFRP layer. The composite housings outperformed other materials, showing a performance ratio 1.95 times higher than nylon, 2.75 times higher than resin, 1.31 times higher than Q235 mild steel, and 1.17 times higher than 304 stainless steel. These findings offer valuable insights for designing composite pressure housings and preventing collapse, highlighting their potential for deep-sea submersibles.

Impact of introducing electric vehicles on ground-level O3 and PM2.5 in the Greater Tokyo Area: yearly trends and the importance of changes in the urban heat island effect

Abstract. Battery electric vehicles (BEVs) are considered a solution for global warming and air pollution, and several countries have announced they will shift to BEVs in the 2030s. Even though previous studies have shown the effects of reducing vehicular emissions on the formation of tropospheric ozone (O3), no studies have evaluated the effect of decreasing anthropogenic heat, which is expected to mitigate urban heat island (UHI) effect, on air quality issues. We used a numerical weather prediction to estimate changes in the UHI effect in the Greater Tokyo Area (GTA) of Japan by introducing BEVs. The results indicated that the introduction of BEVs would lead to a maximum local temperature decrease of 0.25 °C in the GTA. The effects of introducing BEVs on O3 and fine particulate matter (PM2.5) were estimated using a regional chemical transport model. The results indicated that mitigating the UHI effect would lead to a reduction in ground-level O3 formation. This is due to the increased NO titration effect caused by the lowered planetary boundary layer height and due to the degradation of photochemistry related to O3 formation caused by a decrease in temperature and biogenic volatile organic compounds (BVOCs). The mitigation of UHI would result in enhanced particle coagulation, with an increase in ground-level PM2.5. Furthermore, a decrease in BVOC emissions would result in increased PM2.5 owing to enhancement of the OH + SO2 reaction. A total of 175 and 77 annual premature deaths would be prevented from changes in O3 and PM2.5, respectively.

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Abstract. The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance but remains a challenge due to their inherent complexity, which is reflected in input–output relationships often characterised by multiple interactions between the parameters, thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis for complex models, guiding the user through the different steps and allowing for a simplification and automatisation of the process.

China Aerosol Raman Lidar Network (CARLNET)—Part I: Water Vapor Raman Channel Calibration and Quality Control

Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP.

Numerical assessment of tool geometry for improving productivity in milling stainless steel 316 L

Improving the material removal rate (MRR) can significantly enhance the efficiency of the milling operations during machining. However, increasing MRR develops a larger degree of stress and eventual wear at the cutting edge, reducing the tool’s lifetime, in particular for hard metals like stainless steel. Therefore, it is important to optimize the tool geometry to enhance the stress-carrying capacity under extreme cutting conditions. Considering a four-fluted tungsten carbide milling tool for cutting stainless steel, we propose in this study a procedure for reducing tool stresses by modifying the tool geometry. Using a systematic set of finite element simulations, we showed that the degree of stresses on the cutting edge can be reduced by optimizing three geometrical parameters, i.e., helix angle, rake angle, and cutting edge radius. To validate the simulation results, we manufactured 18 four-fluted milling tools with varying geometries and tested them by milling stainless steel 316 L under identical cutting conditions. The performance of each tool was ranked based on microscopic inspections of their cutting edges, showing a close agreement with the numerical simulation predictions. This study presents a procedure for modifying milling tool geometry to enhance performance under extreme machining conditions.