# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 4 Results and Discussions
The time series for wind speeds also highlights that Barishal (Figure 4b) and Rajshahi (Figure 4d) have stronger wind blows in Southern and Northern areas, respectively than others. Regardless speed's hierarchy, most cities meet the minimum threeholds i.e., cut-in wind speeds for rotating turbines at around 80 to 100 m except Sylhet. Overall, the ERA5 datasets at pressure levels from 1000 to 900 hPa provide a reference for wind intensity unlike station observations. Several previous studies refer to the higher intensity for observed wind speeds of about 5 to 8 m/s [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14] than the ERA5 datasets. As the BMD stations have observations at lower heights, i.e., 10 m, this height neither shows strong wind nor conditions appropriate for installing wind turbines. Though ERA5 uses reanalysis datasets, it is worth investigating the mean windiest areas for finding suitable locations to place turbines and to reduce costs for multiple stations. Similar suggestions are emphasized for exploring wind speeds from 10 to 120 m above terrain for turbine installations [14]. Furthermore, ERA5 contains diverse sets of variables for investigating strong or weak relations in numerical modeling. By studying ERA5 datasets, either NWP or data-driven models can further be developed in numerical applications. The analysis for wind at various heights encourages the installation of high-resolution observations in spatial-temporal resolutions for particular regions to investigate more evidence on wind speed durations. For installing highly equipped observations, the above results also help to figure out possible locations such as Rajshahi and Rangpur regions, which are closer to riversides. The obtained wind speed also supports installing small-scale or rooftop-based turbines on tall buildings for both regions. The atmospheric boundary layer height (BLH) changes by surface roughness, thermal variations, and weather systems that significantly affect wind intensity and duration. The mean BLH is analyzed for understanding the thermal conditions and turbulence effects, which would influence turbine rotations and plant operations. The significance of BLH is discussed in [14], but the study did not provide any estimated values. The current paper finds that the BLH (m) demonstrates decreasing trends and variations for the southern and northern regions given in Figure 5a,c, respectively. The comparisons in anomalies show  
Figure 4: Time series of averaged wind speed (m/s) at 975 hPa, i.e., \(\sim\)85 m for the southern (left panels) and northern regions (right panels) from 1979 to 2018 with a climatic base period from 1979 to 2000.  
that the decreasing trends in southern areas (Figure 5a) are slower than norther (Figure 5c) regions. Similar to wind anomalies, BLH also illustrates a comparable pattern for each cities i.e., variations are closer to each other. In the southern areas, Dhaka shows the lowest BLH (416.36 m) while Barishal is the highest (537.13 m) in 40 years (shown in Figure 5b). For the northern regions in Figure 5d, Sylhet has the lowest BLH (351 m) whereas Rajshahi has the highest (414.72 m) in 40 years. This eventually indicates that the minimum and maximum mean BLH in Bangladesh are Sylhet and Barishal, respectively. The result implies that a moderate or stronger wind might result in higher BLH, i.e., supports wind intensity for southern regions. The variations of the BLH infer local vertical mixing, circulation, and turbulence. After assessing the BLH for each city, these results demonstrate that the optimized height would not experience heavy turbulence. After meeting atmospheric conditions, stable infrastructures, i.e., wind turbines, can function and provide desired outputs in renewable energy. Global Wind Atlas (GWA) provides detailed information for wind and power for each country including main administrative cities. As GWA considers the roughness length, ruggedness index, and terrain effects, the power datasets are incorporated as historical datasets for Bangladesh.