Spatial Data Fusion and Machine Learning Bias Correction for Convection-Permitting Atmospheric Downscaling and Multi-Criteria Site Feasibility Mapping in Complex Coastal Terrains

Abstract

High-resolution atmospheric modeling over topographically complex coastal zones requires multi-source spatial data fusion to bridge the scale gap between satellite-assimilated global reanalysis and localized boundary layer dynamics. This study presents a spatial data fusion framework coupling convection-permitting Weather Research and Forecasting (WRF) simulations (3 km grid spacing) with machine learning calibration and GIS multi-criteria decision analysis across continuous computational grids (24,871 cells). Focusing on a high-resolution seasonal case study during the peak Caribbean Low-Level Jet (CLLJ) and Trade Winds regime along the Colombian Caribbean coast, predictive algorithms were benchmarked across day-grouped and Leave-One-Station-Out (LOSO) spatial transfer protocols against classical meteorological baselines. Support Vector Regression (SVR) with an RBF kernel achieved superior residual calibration (R2=0.9523, RMSE=0.4601 m/s) under day-grouped cross-validation, and an average Leave-One-Station-Out (LOSO) spatial transfer RMSE of 1.1030 m/s under blind geographic transfer to unmonitored stations, corresponding to a >76% RMSE reduction relative to raw, uncalibrated WRF output in that same spatial-transfer setting. Chronological forward-forecasting tests further showed that this seasonally augmented kernel model does not extrapolate reliably to calendar months absent from training (R2=−0.09), so a reduced 8-variable configuration is recommended for genuine forward forecasting into unseen seasons. Scalar calibrations were transferred to horizontal wind vectors preserving simulated flow direction, and vertically scaled to 80 m hub height using WRF prognostic shear. Calibrated fields were integrated into a Boolean GIS-MCDA model incorporating wind power density, slope limits, environmental/indigenous reserves, and infrastructure proximity. The framework delineated an 8.9% high-feasibility macro-corridor (2214 cells; ≈19,926 km2) in northern La Guajira, while substantially reducing uncalibrated mesoscale overestimation in the sheltered wake of the Sierra Nevada de Santa Marta. The methodology provides a robust screening-level tool for coastal renewable resource assessment.