# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 3 Materials and Methods  
The experimental design carries out an exploration of historical data from the European Center for Medium range Weather Forecast (ECMWF) reanalysis 5 th generation (ERA5) and Global Wind Atlas (GWA) for investigating climatological variability in relation to wind power production. ERA5 signifies the latest climate reanalysis products for atmospheric, land surface, and sea-state data based on observations and modelled outputs. The horizontal resolution is about 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and vertical heights are about 37 levels covering the globe. The global data is then extracted to Bangladesh and the chosen regions. Even though ERA5 makes extractions for sub-regions possible, subsets for each division are prepared by rectangular grids, i.e., Cartesian grids from the global domain. For instance, in order to obtain datasets for Dhaka division, coordinate variables are one dimensional for latitude and longitude from 24 to 25.5\({}^{\circ}\) N and 90 to 91\({}^{\circ}\) E, i.e., start and end coordinate arrays, averaged over a rectangular sub-domain. Similarly, the other coordinate subscripts are extracted for each region (shown in Figure 1). This method might result in two possible uncertainties: (1) underestimations in intensity and (2) coarse representation for geopolitical boundaries due to averaging effects and horizontal grids, respectively. As Bangladesh does not have high resolution operational numerical weather prediction (NWP), ERA5 is still a resourceful data-store for investigating climatological variability. Meteorological variables namely monthly wind speed (m/s) and mean boundary layer height (BLH) in meters at various pressure levels are explored. The chosen heights for wind components (U and V) are 10 m (10 m), 1000, 975, 950, 925, and 900 hecta-Pascale (hPa). For both variables, time series, mean trends, and anomalies are scrutinized to explore variability and deviation over a historical period for 40 years from 1970 to 2018, with a climate base of 20 years from 1979 to 2000. From ERA5, hourly wind data is also extracted for 2018 and utilized in estimating power (Kilowatt) and annual energy production (Megawatt). Furthermore, Bangladesh does not publicly provide wind power data from operational wind farms (if any). GWA data are incorporated to develop prediction models. GWA is a  
Figure 1: Study site: eight divisions and major cities in Bangladesh with coordinates for sub-domains. free web-based application providing downloadable datasets for preliminary calculations. This modelling considers high resolution global maps, country and first administrative provinces, and physical parameters such as surface roughness, topography, obstacles, and weather information. The power datasets are reasonable replacements for historical information in statistical applications. The detailed information for GWA and ERA5 are given in Table 1.  
Wind speeds are the key variable and have strong correlations to power density, and this relationship provides strong insights to utilize as power predictions for wind turbines [20; 21; 22; 23; 24]. In polynomial curve-fitting, wind power is estimated based on operational power and wind data by least square methods [25]. In the present study, wind speed and power data are analyzed to establish a simple power predictive model (SPPM) using polynomial and exponential nonlinear regression. The general polynomial and exponential regression equations are given in Equations (1) and (2), respectively. Finally, wind power is also calculated using Equation (3) as a physical model to compare SPPM results for further assessments. Equations (1)-(3) are as follows:  
\[P=a\;e^{V} \tag{1}\]  
\[P=a_{0}+a_{1}V+a_{2}V^{2}+\dots+a_{n}V^{n} \tag{2}\]  
\[P=\frac{1}{2}\;\rho\;V^{3}A \tag{3}\]  
From Equations (1)-(3), \(P\) represents power in Kilowatt (KW) and \(V\) is wind speed (m/s). Here, \(a\) stands for model parameters in nonlinear regressions and \(n\) refers to the order of the polynomial.