arXiv subjects

Reda Snaiki

Publications and source records attributed to Reda Snaiki.

12 recordsLinked to original sources

arXiv2026

Multiscale Dynamics of Heatwave Persistence and Intensity Under Climate Change

Climate change is expected to increase heatwave risk, but exceedance frequency alone cannot explain why some regions show stronger amplification in event persistence. This study develops an integrated event-dynamical workflow to diagnose changes in warm-season heatwaves and link them to coherent, multiscale structures of temperature variability. Heatwaves are identified over southern Canada using a fixed historical 90th percentile threshold (2001-2010 reference, 15-day moving window) and a minimum-duration criterion. Events are summarized using frequency (HWF, HWN), persistence (HWMD, HWD), and intensity (HWI, HWM) metrics. The daily mean temperature field is analyzed using multiresolution dynamic mode decomposition (mrDMD). Event and dynamical perspectives are connected through heatwave-conditioned mode participation ratios and spatial alignment analyses between mode-energy footprints and gridded heatwave metrics using hotspot overlap and Spearman rank association. The workflow is applied to CORDEX-NAM12 regional simulations (CRCM5 downscaling of CanESM5) under SSP5-8.5 for 2016-2025, 2051-2060, and 2091-2100. Results show a clear shift toward persistence-dominated heatwave regimes in the continental interior. By the late century, increases in seasonal heatwave days are accompanied by much longer events, with regional HWMD reaching about 26.66 days/event and HWD about 69 days, together with stronger above-threshold intensity, with HWI reaching about 6.88 K. Dynamical diagnostics indicate a redistribution of dominant activity toward lower-frequency levels and weaker effective damping in interior regions, while coastal and maritime regions show smaller changes. Heatwave-relevant low-frequency modes remain active during long events and align with persistence and intensity hotspots, supporting a process-informed interpretation of regional heatwave amplification under climate change.

physics.ao-ph↗

arXiv2026

Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints

Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis, but practical deployments are limited by sensor budgets and spacing constraints. This study proposes an uncertainty-aware graph neural network (GNN) framework for reconstructing daily maximum temperature fields from sparse sensors while supporting distance-constrained sensor placement and probabilistic exceedance mapping. The model predicts both the temperature field and a spatially varying predictive uncertainty field using a graph-attention-based mean-residual architecture trained with a Gaussian negative log-likelihood. Sensor placement is addressed using a Proper Orthogonal Decomposition with QR factorization (POD-QR) strategy with a 4 km minimum inter-sensor distance constraint and is compared with random feasible placement and farthest-point sampling. The framework is evaluated over a Montreal-area polygon using Daymet v4.1 daily temperature data (1 km resolution) under a strict temporal hold-out protocol (training: 2020-2023; testing: 2024). Across sensor budgets (10-40 sensors), the proposed GNN consistently outperforms inverse distance weighting and ordinary kriging in RMSE and MAE on unobserved nodes. Sensor-placement effects are most pronounced at low budgets and diminish at higher budgets, with a practical saturation regime emerging around 30 sensors under the imposed spacing constraint. Probabilistic evaluation further shows improved uncertainty calibration with increasing sensor density and a better sharpness-calibration trade-off than kriging. These results support the proposed framework as an effective tool for uncertainty-aware temperature field reconstruction and decision-oriented heat-risk mapping.

physics.app-ph↗

arXiv2026

Sensor-Outage-Aware Spatio-Temporal Graph Reconstruction of High-Rise Facade Pressure Fields

Time-resolved facade pressure fields are essential for the wind-resistant design and aerodynamic assessment of high-rise buildings. However, dense instrumentation is costly and often impractical, and sensor outages can further reduce data availability. This study proposes a sensor-outage-aware spatio-temporal graph reconstruction framework for completing facade pressure fields from sparse measurements. The method couples temporal feature extraction with graph-based spatial propagation on a unified facade-domain representation and uses an explicit observation-availability indicator to handle temporarily unavailable sensor signals while reconstructing both missing instrumented channels and non-instrumented locations. The framework is evaluated using wind-tunnel pressure coefficient data for a high-rise building across windward, lateral, and leeward facades under multiple wind directions. The results show reliable outage-tolerant reconstruction at instrumented sensors and accurate full-field completion at non-instrumented nodes, with reconstruction generally most accurate on the windward facade and more challenging on the lateral and leeward facades. Time-domain, spectral, and spatial validations further show that the framework preserves the dominant temporal evolution, principal dynamic content, and coherent large-scale pressure-field organization, while the largest residual discrepancies remain localized in higher-frequency or intermittent components. A two-stage predictive extension is also outlined, in which future sensor signals are forecast at available instrumented locations and then mapped to future full-field pressure estimates through the proposed reconstruction model.

eess.SP↗

arXiv2025

A Hierarchical Deep Learning Model for Predicting Pedestrian-Level Urban Winds

Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as sharp gradients and peak wind speeds. This study proposes a hierarchical approach for accurately predicting pedestrian-level urban winds, which adopts a two-stage predictor-refiner framework. In the first stage, a U-Net architecture generates a baseline prediction from urban geometry. In the second stage, a conditional Generative Adversarial Network (cGAN) refines this baseline by restoring the missing high-frequency content. The cGAN's generator incorporates a multi-scale architecture with stepwise kernel sizes, enabling simultaneous learning of global flow structures and fine-grained local features. Trained and validated on the UrbanTALES dataset with comprehensive urban configurations, the proposed hierarchical framework significantly outperforms the baseline predictor. With a marked qualitative improvement in resolving high-speed wind jets and complex turbulent wakes as well as wind statistics, the results yield quantitative enhancement in prediction accuracy (e.g., RMSE reduced by 76% for the training set and 60% for the validation set). This work presents an effective and robust methodology for enhancing the prediction fidelity of surrogate models in urban planning, pedestrian comfort assessment, and wind safety analysis. The proposed model will be integrated into an interactive web platform as Feilian Version 2.

physics.ao-ph↗

arXiv2025

Projecting Hurricane Risk in Atlantic Canada under Climate Change

Atlantic Canada faces significant hurricane threats from damaging winds and coastal flooding that are projected to intensify under climate change. This study adopts a two-stage framework. First, the evolution of wind and coastal-flood hazards is quantified from a historical baseline (1979-2014) to two future periods: a near future (2024-2059) and a far future (2060-2095). Hazard fields are constructed from large ensembles of physics-informed synthetic hurricane tracks, and changes are evaluated in return-period wind speeds and in inundation depth and extent, with sea-level rise included for flood projections. The second stage estimates hurricane risk using wind as an operational proxy for total loss, combining the simulated wind fields with exposure data and a vulnerability relationship to compute expected damages. This design clarifies how physical drivers change and how those shifts translate into loss potential without requiring fully coupled compound-loss modeling. Results indicate an intensification of wind extremes and a substantial amplification of coastal inundation, yielding higher wind-proxy risk for many coastal communities. Spatial patterns show a heterogeneous escalation of risk concentrated along exposed shorelines and urban corridors. This comprehensive analysis of both hazard evolution and proxy risk provides decision-ready evidence on where and by how much hurricane losses are likely to grow. The approach clarifies the link between physical drivers and loss potential, ensuring compatibility with standard wind-centric workflows used in engineering and insurance practice.

physics.ao-ph↗

arXiv2025

A Hybrid Analytical Framework for Asymmetric Pressure and Boundary Layer Wind Simulation in Nor'easters

Nor'easters frequently impact the North American East Coast, bringing hazardous precipitation, winds, and coastal flooding. Accurate simulation of their pressure and wind fields is essential for forecasting, risk assessment, and infrastructure planning, yet remains challenging due to their complex, asymmetric structure. This study introduces a novel hybrid analytical-data-driven model designed to efficiently simulate Nor'easter pressure and boundary layer wind fields. The pressure field is modeled using an adapted Holland-type formulation, with azimuthally varying parameters estimated through Kriging surrogate models informed by sensitivity analysis of reanalysis data. The wind field is then derived analytically from the momentum equations by decomposing the wind flow into gradient and frictional components. Model performance is assessed against ERA-Interim reanalysis data and surface wind observations from a historical event. Results show that the proposed pressure model accurately reproduces elliptical isobars and key asymmetrical patterns, while the wind model captures the fundamental structure and intensity of the boundary layer flow, including the presence of supergradient winds. Owing to its physical basis, computational efficiency, and ability to represent critical storm asymmetries, the model offers a valuable alternative to computationally expensive numerical simulations for hazard assessment and scenario analysis of extreme Nor'easters.

physics.ao-ph↗

arXiv2025

A Novel Dynamic Bias-Correction Framework for Hurricane Risk Assessment under Climate Change

Conventional hurricane track generation methods typically depend on biased outputs from Global Climate Models (GCMs), which undermines their accuracy in the context of climate change. We present a novel dynamic bias correction framework that adaptively corrects biases in GCM outputs. Our approach employs machine learning to predict evolving GCM biases, allowing dynamic corrections that account for changing climate conditions. By combining dimensionality reduction with data-driven surrogate modeling, we capture the system's underlying dynamics to produce realistic spatial distributions of environmental parameters under future scenarios. Using the empirical Weibull plotting approach, we calculate return periods for wind speed and rainfall across coastal cities. Our results reveal significant differences in projected risks with and without dynamic bias correction, emphasizing the increased threat to critical infrastructure in hurricane-prone regions. This work highlights the necessity of adaptive techniques for accurately assessing future climate impacts, offering a critical advancement in hurricane risk modeling and resilience planning.

physics.ao-ph↗

arXiv2024

Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks

Coastal regions in North America face major threats from storm surges caused by hurricanes and nor'easters. Traditional numerical models, while accurate, are computationally expensive, limiting their practicality for real-time predictions. Recently, deep learning techniques have been developed for efficient simulation of time-dependent storm surge. To resolve the small scales of storm surge in both time and space over a long duration and a large area, these simulations typically need to employ oversized neural networks that struggle with the accumulation of prediction errors over successive time steps. To address these challenges, this study introduces a hierarchical deep neural network (HDNN) combined with a convolutional autoencoder (CAE) to accurately and efficiently predict storm surge time series. The CAE reduces the dimensionality of storm surge data, streamlining the learning process. HDNNs then map storm parameters to the low-dimensional representation of storm surge, allowing for sequential predictions across different time scales. Specifically, the current-level neural network is utilized to predict future states with a relatively large time step, which are passed as inputs to the next-level neural network for smaller time-step predictions. This process continues sequentially for all time steps. The results from different-level neural networks across various time steps are then stacked to acquire the entire time series of storm surge. The simulated low-dimensional representations are finally decoded back into storm surge time series. The proposed model was trained and tested using synthetic data from the North Atlantic Comprehensive Coastal Study. Results demonstrate its excellent performance to effectively handle high-dimensional surge data while mitigating the accumulation of prediction errors over time, making it a promising tool for advancing storm surge prediction.

physics.ao-ph↗

arXiv2024

Performance-based ice engineering: a data-driven multi-scale approach

Ice storms are one of the most devastating natural hazards which have the potential to inflict significant damage to the built environment. The multi-hazard nature of ice events complicates the analysis of their induced risk due to their inherent nonlinear interactions. In addition, the concurrent and interacting hazards are often responsible for several aerodynamical/dynamical instabilities such as the galloping mechanism. Moreover, the existing risk approaches are usually not suited for large-scale risk evaluation over extended geographical regions due to the involved high-computational costs. Therefore, in this study, a novel data-driven multi-scale performance-based ice engineering (PBIE) framework is developed to support the design of new structures subjected to ice storms or the rehabilitation of existing ones. In addition, the proposed PBIE is capable of rapidly estimating the real-time risk over an extended region due to an ice event. Specifically, it leverages the superior capabilities of state-of-the-art data-driven techniques (e.g., machine learning) to efficiently generate the corresponding risk maps and identify the high-risk areas. The proposed PBIE framework is applied to a simplified example in which the galloping-induced risk on iced conductors, in terms of the galloping amplitude, is evaluated for both local and regional scales. The resulting PBIE framework can be readily applied for design or retrofitting purposes or integrated within an emergency response management system to inform preventive actions that can mitigate the ice storm-induced damages and save lives.

physics.ao-ph↗

arXiv2024

Real-Time Dynamic Layout Optimization for Floating Offshore Wind Farm Control

Downstream wind turbines operating behind upstream turbines face significant performance challenges due to reduced wind speeds and increased turbulence. This leads to decreased wind energy production and higher dynamic loads on downwind turbines. Consequently, real-time monitoring and control have become crucial for improving wind farm performance. One promising solution involves optimizing wind farm layouts in real-time, taking advantage of the added flexibility offered by floating offshore wind turbines (FOWTs). This study explores a dynamic layout optimization strategy to minimize wake effects in wind farms while meeting power requirements. Two scenarios are considered: power maximization and power set-point tracking. The methodology involves a centralized wind farm controller optimizing the layout, followed by wind turbine controllers to meet the prescribed targets. Each FOWT employs model predictive control to adjust aerodynamic thrust force. The control strategy integrates a dynamic wind farm model that considers floating platform motion and wake transport in changing wind conditions. In a case study with a 1x3 wind farm layout of 5 MW FOWTs, the results show a 25% increase in stable energy production compared to a static layout in one hour for the first scenario. In the second scenario, desired power production was swiftly and consistently achieved.

eess.SY↗

arXiv2024

A Physics-informed machine learning model for time-dependent wave runup prediction

Wave runup is a critical factor affecting coastal flooding, shoreline changes, and damage to coastal structures. Climate change is also expected to amplify wave runup's impact on coastal areas. Therefore, fast and accurate wave runup estimation is essential for effective coastal engineering design and management. However, predicting the time-dependent wave runup is challenging due to the intrinsic nonlinearities and non-stationarity of the process, even with the use of the most advanced machine learning techniques. In this study, a physics-informed machine learning-based approach is proposed to efficiently and accurately simulate time-series wave runup. The methodology combines the computational efficiency of the Surfbeat (XBSB) mode with the accuracy of the nonhydrostatic (XBNH) mode of the XBeach model. Specifically, a conditional generative adversarial network (cGAN) is used to map the image representation of wave runup from XBSB to the corresponding image from XBNH. These images are generated by first converting wave runup signals into time-frequency scalograms and then transforming them into image representations. The cGAN model achieves improved performance in image-to-image mapping tasks by incorporating physics-based knowledge from XBSB. After training the model, the high-fidelity XBNH-based scalograms can be predicted, which are then employed to reconstruct the time-series wave runup using the inverse wavelet transform. The simulation results underscore the efficiency and robustness of the proposed model in predicting wave runup, suggesting its potential value for applications in risk assessment and management.

physics.flu-dyn↗

arXiv2024

A Novel Hybrid Machine Learning Model for Rapid Assessment of Wave and Storm Surge Responses Over an Extended Coastal Region

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a low-dimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge.

physics.ao-ph↗