## 2 Materials and Methods
### Model Setup  
This study utilized two-way nesting domains with spatial resolutions of 15 km (D1) and 5 km (D2), as shown in Figure 2. D1 was wide enough to cover the atmospheric circulation synoptic systems that affect the study area. The model defined thirty-nine vertical levels from the surface up to the 100 hPa pressure level. The ECMWF (European Centre for Medium-Range Weather Forecasts) Era-Interim data with 0.75\({}^{\circ}\) spatial horizontal resolution every 6 h were used to derive the initial and boundary conditions. Terrestrial data required for the model, including land use, land mask, albedo, and topography, were obtained from the USGS (United States Geological Service) database. The nudging coefficients in WRF were set to a default value of 3.0 \(\times\) 10\({}^{-4}\) s\({}^{-1}\) for each variable. These values were derived from observation-driven studies that used  
Figure 2: The two defined domains in the WRF model simulations over Iran. The colored scale corresponds to altitude (m). M, Mo and Val stand for the words Mountain, Mourian and Valley, respectively. The designations D1 and D2 refer to the WRF Model Domains. D1 represents the parent domain, covering the Middle East and surrounding areas, while D2 represents the smaller domain specifically focusing on Iran. The data from D2 was utilized in creating the wind atlas. analysis nudging in WRF's predecessors, MM4 and MM5 [67; 68]. In essence, the nudging coefficients represent the reciprocal time scales of the physical processes in the data that the model is nudged towards. The nudging technique can reduce the error of dynamic downscaling simulations and improve the consistency with the driving data [69]. Since dynamical downscaling results usually drift away from large-scale synoptic driving fields, the nudging in domain 1 can be used to balance the performance of dynamical downscaling at large and small scales. Therefore, analysis nudging was used for domain 1 and above the boundary layer for the wind, temperature and specific humidity. Nudging was used to relax the model solution toward the analysis data, and it was proven to be effective in reducing the wind speed error [70; 71]. To ensure that the initial conditions were applied every 48 h, the WRF model was restarted at 00 UTC every 48 h. The first 12 h of each simulation were excluded from the analysis since they account for the model spin-up. To estimate the WRF performance in the simulation of the wind field, a preliminary evaluation was carried out with a model-to-data comparison using five SL and PBL parameterization schemes during cold (January) and warm (July) months in 2013. For this purpose, the simulated wind speed by the model was compared with the wind data obtained by the upper air sounding. Comparisons were performed at 4 heights of 10, 40, 80 and 100 m, considering the sensitivity of each of the parameterizations to the environmental conditions and the stability of the atmosphere. Among the 13 active upper air stations throughout the country, 4 stations (Tehran, Kerman, Kermanshah and Tabriz) had suitable data (wind data below 100 m) for comparison with the model output. Table 1 shows the characteristics of these 4 upper air observation stations. The wind resource maps were developed by simulating weather conditions from a large number of days and for a long-term period [72]. Therefore, after obtaining the best configuration, a long-term simulation over a seventeen-year period (2004-2020) was carried out and a wind dataset with one-hour time steps was prepared for 1,584,840 grid points covering the whole Iranian territory. Then, the calculated average wind speeds for all the grid points were summarized to develop the annually, seasonally and monthly wind maps at different heights. Table 2 shows the details of WRF configuration, including the model parameterization schemes that were used in the simulations.