## 3 Results and Discussion
### Sensitivity Analysis
The primary sources of error are due to (i) systematic errors inherent in the modeling process of meteorological quantities, (ii) time step differences between model data (one hour) and synoptic data (three hours), (iii) a spatial step of about 5 km between the model and station data in complex topographical areas, and (iv) the effects of urban development in some stations, which may modify the local wind regime. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Synoptic Station** & **Mean Bias** & **Diff Elevation** \\ \hline Khalfkal & 0.718 & 20 \\ \hline Songhor & 0.242 & 26 \\ \hline Koohin & 1.068 & 69 \\ \hline Sheikh Tape & 1.392 & 114 \\ \hline Haji-Abad & 2.526 & 496 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The elevation difference between the model grid cell elevation and actual elevation at SATBA sites and the mean bias in the wind speed simulation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline BE (m/s) & \(-0.69\leq\) BE \(<\) 0 & \(0\leq\) BE \(<\) 1 & \(1\leq\) BE \(<\) 2 & \(2\leq\) BE \(<\) 3 & \(3\leq\) BE \(\leq\) 3.71 \\ \hline percentage of stations (\%) & 4.55 & 35.45 & 37.27 & 18.18 & 4.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The classification of the range of bias errors and the percentage of stations belonging in each class. The spatial distribution of the annual mean wind speed at 10 m shows that the highest wind intensities occur in the eastern part of Iran, through the Sistan Basin and in regions north of it, where the strong and persistent Levar wind blows during the warm season. This finding is consistent with the results obtained by several previous works [104; 105; 106; 107]. All other parts of Iran present much lower wind speeds, which are generally increased along the Zagros and Alborz mountain ranges, since stronger winds occur at higher altitudes. The Iranian Plateau and coastal areas are characterized by rather weak winds, as well as the southwest part of Iran (Khuzestan Province), which is highly affected by Shomal dust storms mostly during the warm period of the year [108; 109; 110]. On an annual mean basis, the effect of \"Qibla wind\" [103] over the central Iranian Plateau seems to have a rather limited impact on the wind regime over the region. The Global Wind Atlas (GWA) version 3 provides wind climatological data estimated on a 250 m grid at five heights (10 m, 50 m, 100 m, 150 m, and 200 m) for the period between 2008 and 2017. While the latest and most accurate available observations have been used to produce this wind dataset, in many developing countries, the lack of ground measurements from high-precision meteorological towers and LiDARs means that the data cannot be fully confirmed ([[https://globalwindatlas.info](https://globalwindatlas.info)]([https://globalwindatlas.info](https://globalwindatlas.info)), accessed on 12 January 2024). To compare the results of the WRF wind simulations with the DTU microscale wind data of the GWA, the differences between their 50 m and 100 m winds were calculated and are presented in Figure 4. The results indicate that the difference between the WRF model and the wind atlas data is small in areas with high wind speeds, showing a good agreement between the two datasets. However, in mountainous and high-altitude areas, particularly along the Zagros Mountains, the WRF model tends to overestimate the wind speeds (Figure 4e,f). Overall, it is expected that there will be differences between mesoscale wind data (grid spacing 5 km) and high-resolution wind data (grid spacing 250 m). Therefore, this comparison demonstrates that the generalized wind climate technique applied in the GWA reduced the error of DTU data in mountainous areas. However, the assessment of wind climate using the Global Wind Atlas (such as the data available in GWA website) is not as suitable because it represents the average of a long climatic period (2008-2017) and does not include measurements of wind speed throughout that entire period.