Assessing and correcting the effects of biased sampling on Weibull parameter estimation for wind speed data modelling

Abstract In India, routine surface wind observations are generally measured in decimal knots and later disseminated in integer kilometres per hour after operational unit conversion and rounding. Although this reporting procedure is convenient for dissemination, it introduces quantization-induced sampling bias into the released wind speed records. Such discretization can systematically distort the estimation of continuous probability distribution parameters used in probabilistic wind speed modelling. In the present study, the Weibull distribution is adopted as an illustrative framework to quantify this effect and to develop an inverse analytical correction technique for recovering unbiased Weibull shape and scale parameters from disseminated biased estimates. A large Monte Carlo simulation campaign is performed over a wide range of Weibull parameter combinations, and second-order polynomial response surfaces are constructed to map biased estimates back to their unbiased counterparts. Independent out-of-sample validation confirms high reconstruction accuracy with R 2 = 0.9998 for the shape parameter and R 2 = 0.9723 for the scale parameter, accompanied by low RMSE and MAPE values. The practical usefulness of the proposed correction framework is further demonstrated through wind power density estimation over representative Indian wind regimes, where dissemination-induced bias is shown to produce substantial underestimation in low-shape-parameter conditions. The study establishes that operational dissemination-stage rounding, though seemingly minor, can propagate into significant statistical distortion in downstream wind resource assessment unless appropriately corrected.