Simulation of a persistent medium-term precipitation event over the western Iberian Peninsula

Abstract

Abstract. This study evaluated the performance of the WRF-ARW (Weather Research and Forecasting with Advanced Research) weather prediction model in simulating the spatial and temporal patterns of an extreme rainfall period over a complex orographic region in north-central Portugal. The analysis was performed during the rainy season and, more specifically, the month of December 2009. In this period, the region of interest was under the influence of a sequential passage of low-pressure systems associated with frontal surfaces. These synoptic weather patterns were responsible for long periods of rainfall, resulting in a high monthly precipitation. The WRF model results during the study period were furthermore evaluated with the specific objective to complement gaps in the precipitation recordings of a reference meteorological station (located in Pousadas), the data of which are fundamental for hydrological studies in nearby experimental catchments. Three distinct WRF model runs were forced with initial fields and boundary conditions obtained from a global domain model: (1) a reference experiment with no nudging (RunRef); (2) observational nudging for a specific location, i.e. the above-mentioned Pousadas reference station (RunObsN); and (3) nudging to the analysed field (RunGridN). Model performance was evaluated, using several statistical parameters, against a dataset of 27 rainfall stations that were grouped by elevation. The three model runs had similar performances, even though RunGridN resulted in a slight improvement. Regarding the other two experiments, this improvement justifies its use for complementing the surface measurements at the Pousadas reference station. Overall model accuracy, expressed in root mean square error (RMSE), of the three runs was comparable for the stations of the different elevations classes. Even so, it was slightly better for stations in the lowlands than the highlands. Furthermore, model predictions tended to be less accurate for stations located in rough terrain and deep valleys.