Comparative Assessment of Short-Term Near-Surface Wind Prediction of Variable-Resolution MPAS and Nested WRF Models Across Varying Terrain Complexity

The Weather Research and Forecasting (WRF) model has been widely utilized for regional weather and climate prediction. However, variable-resolution models like the Model for Prediction Across Scales (MPAS) offer promising alternatives due to computational advantages, particularly their ability to achieve higher resolution in regions of interest without the nested domains required by WRF. This study evaluates short-term (12-36 hour) surface wind speed predictions from regional MPAS and WRF configurations against 10 m wind observations from 510 meteorological stations across Turkey. Both models employ nearly identical physics parameterizations and use 00 UTC initialization of 0.25° Global Forecast System (GFS) data for lateral boundary conditions. WRF simulations use one-way and two-way nested configurations (12 km and 4 km domains), while MPAS employs variable-resolution meshes with quasi-uniform 4 km resolution at the elliptical core. The high-resolution regions of both models cover approximately the same area, covering all of Türkiye. Model performance is assessed for individual stations using hourly means over two months (January and June 2023) and stratified by three terrain complexity categories (low, medium, high) defined by the Terrain Ruggedness Index (TRI). Results demonstrate that MPAS generally outperforms WRF across all metrics, including mean error (ME), root mean square error (RMSE), and correlation coefficient. Both models overpredict wind speeds, with mean errors of 0.76 m/s (MPAS) and 0.99 m/s (WRF). Overall correlation coefficients are approximately 0.54 for both models. As expected, all metrics deteriorate with increasing terrain complexity. However, MPAS significantly reduces bias, particularly at high-complexity sites, showing 38% lower mean error and 10% lower RMSE compared to WRF's 4 km predictions. Two-way nesting provides limited improvement in WRF's fine-resolution domain, while unexpectedly, the coarse domain (12 km) achieves lower bias and RMSE than the fine domain. These findings suggest that MPAS's unstructured spatial discretization is well-suited for simulating surface winds over complex terrain. Further investigation is needed to understand the physical mechanisms underlying MPAS's superior performance and to assess whether high resolution is necessary over regions with low terrain complexity.