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Publications
Salvação, N., Monteiro, M. and Guedes Soares, C. (2025), An assessment of two wind model uncertainties during storm events affecting Portugal, Ocean Engineering, Vol. 333, 121284.
This study evaluates the uncertainties in the wind field produced by two regional mesoscale operational models, WRF and AROME, during three windstorm events that impacted Portugal in the winter seasons of 2015, 2016 and 2017. The goal is to assess forecast errors in near-surface wind predictions under extreme weather conditions and compare model performance across different environments. The storms were characterized by low-pressure systems that caused widespread damage along the Portuguese west coast. Spatial and temporal forecast uncertainties were quantified using statistical measures comparing model outputs against observations. Wind speed data were obtained from multiple sources, including offshore marine buoys, satellite-based IFREMER products, and land-based weather stations from the Portuguese Institute for the Sea and Atmosphere (IPMA). Both models effectively captured general storm characteristics, including structure, track, and intensity, along with the spatial and temporal patterns of wind occurrences with correlation coefficients ranging from 0.72 to 0.94 and MAE values consistently below 2 m/s. However, significant differences in model performance were identified across environments: higher accuracy was noted in oceanic and coastal areas compared to inland regions, where RMSE values frequently exceeded 2 m/s and correlation coefficients were typically below 0.70. Spatial and temporal uncertainties between the two models generally decreased at storm peaks, although these uncertainties were more pronounced over land, suggesting greater difficulties in representing local topographic features. Systematic biases were also observed, with both models underestimating lower wind speeds (<6 m/s) and overestimating higher wind speeds (>10 m/s), possibly due to limitations in surface roughness and boundary-layer parameterizations. These findings provide insights into the strengths and limitations of mesoscale wind modelling, offering guidance for future improvements in operational weather forecasting.
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