arXiv subjects

Abdelatif Merabtine

Publications and source records attributed to Abdelatif Merabtine.

2 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↗