Knowledge of the probability distribution of surface wind speed (SWS) is essential for surface flux estimation, wind power estimation, and wind risk assessments that are required to be analysed through physical or statistical approach. This paper presented a study on application of 2-parameter Weibull (WB2) distribution based on statistical approach for modelling surface wind speed (SWS) of north Indian region covering Delhi-National Capital Region and adjoining areas. The parameters of WB2 were determined by method of moments (MoM), maximum likelihood estimation (MLE), method of L-Moments (LMO), empirical method (EPM), graphical method (GPM) and weighted least squares method (WLS), and used for further analysis. The adequacy of fitting of all six methods of WB2 were evaluated by Goodness-of-Fit (viz., Kolmogorov-Smirnov (KS)) test while the selection was made through model performance analysis using various indicators such as correlation coefficient (CC), Nash-Sutcliffe model efficiency (NSE) and root mean squared error (RMSE). The KS test results confirmed the applicability of MoM, MLE, EPM, GPM and WLS of WB2 for modelling SWS. The study showed that there is a good correlation between the observed and predicted SWS by all six methods of WB2, and the CC values vary between 0.981 and 0.983. The study also showed that NSE given by MLE and GPM are about 96% whereas about 95% for MoM and EPM, 93.6% for WLS and about 70% for LMO. Based on RMSE values, the MLE was adjudged as better suited amongst all six methods of WB2 distribution considered in the study for modelling SWS. The paper presented that the results will be useful for the stakeholders while planning and design of offshore wind structures in the study region.