Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

Abstract

This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992-2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999-2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one's understanding of the wind variability over the area. The subregions identified with the simulation during the 1992-2005 period are similar to those identified with observations (1999)(2000)(2001)(2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

“…It employs an Eulerian mass-coordinate solver with a nonhydrostatic approach, and a terrain-following eta-coordinate system in the vertical. It is a state-of-the-art mesoscale model used in a variety of studies also for hindcast simulations (Kwun et al, 2009;Jiménez et al, 2010Awan et al, 2011;García-Díez et al, 2013;Jiménez and Dudhia, 2013;Santos-Alamillos et al, 2013;Menendez et al, 2014, among others). A first decision in regional climate modelling concerns the selection of the domain to be simulated.…”

“…For this, the simulation result at the closest grid point to the observational site is selected. Although this can lead to representativeness errors (Jiménez et al, 2010), such errors are systematic in all simulations, and do not play a significant role in the assessment of the relative model performance across model configurations.…”

“…Besides, they are used in the so-called hindcast simulations, which combine the reliability of reanalysis products with the high resolution provided by RCMs. Studies focusing specifically on wind have been one of the applications of such types of simulations (Jiménez et al, 2010;Jerez and Trigo, 2013;Etienne et al, 2013;García-Díez et al, 2013Menendez et al, 2014;Lorente-Plazas et al, 2015;Draxl et al, 2014).…”

Surface Wind Regionalization Based on Similarity of Time-series Wind Vectors

“…Second approach evaluates the ability of the regionalization to replicate the topographic features. For example, wind regions derived in Jimenez et al (2008) and Jimenez et al (2010) agree with the topographic features. Surface wind regionalization is then applied to Busan, which has complex terrain, and compared to the topographic features of the terrain.…”

“…In Iberian Peninsula, Jimenez et al (2010) evaluated the performance of the WRF simulation over complex terrain by comparing the wind variability of observations and the model prediction. It was identified that the sub-regions derived from the simulation data are similar to those from observations.…”