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Wind speed is an important meteorological factor and is a parameter used to determine the air pollution assessment and the economic feasibility of wind energy as a renewable resource. In particular, the measurement of long-term average wind speed plays a role in the estimating of chronic exposure of air pollution and the siting of wind turbines; thus this study focuses on annual and monthly wind speed. However, due to spatially sparse and irregular meteorological stations, interpolation methods are used to predict unsampled areas. Therefore, the purpose of this study is to determine the optimal interpolation method among deterministic and geostatistical interpolation techniques. While most interpolation methods rely on spatial coverage and do not give an accurate characterization of small-area variations, cokriging and regression kriging (RK) can resolve the drawback by containing regional heterogeneity as auxiliary variables. In addition, this study applied isotropic and anisotropic approaches to geostatistical interpolations. This study showed that among the sampled methods, anisotropic RK with regional heterogeneous terrains and solar insolation was most likely to produce the best estimate for the highest accuracy for annual and monthly wind speeds with relatively unbiased spatial errors. In addition, while extreme wind speed decreases the accuracy of wind speed prediction, the number, range, and variance of observed wind speed significantly influence the accuracy of wind speed prediction. |
