Abstract
In this study, offshore wind climate assessments are carried out by using mesoscale model Weather Research and Forecasting (WRF) and validated by measurement at a demonstration site located 3.1 km offshore of Choshi. An optimal nudging method is investigated by using offshore and meteorological observations. The land-use datasets are then created from a higher-resolution land-use data by using a maximum area sampling scheme according to the horizontal resolution of the mesoscale model. Finally, the sea surface temperature datasets are corrected by observation data. It is found that the relative error of annual wind speed is reduced from 7.3% to 2.2% and the correlation coefficient between predicted and measured wind speed is improved from 0.80 to 0.84 by considering the effects of land-use and sea surface temperature.
Highlights
Offshore wind energy has been rapidly growing as a renewable energy resource worldwide.Offshore wind climate assessment is important to evaluate a prospective offshore wind project
The predicted wind profile is compared with the observation, the following conclusions are
The predicted wind profile is compared with the observation, and the following conclusions are obtained
Summary
Offshore wind energy has been rapidly growing as a renewable energy resource worldwide. Atmosphere 2020, 11, 379 to investigate the effects of land-use and sea surface temperature on the wind climate assessment by using the mesoscale modeling technique for offshore projects in Japan, since the most of the promised areas for fixed-bottom offshore wind farms are located in the coastal regions about 3–5 km far from the coast. The resolution of the innermost domain was set as 2 km based on the previous study by Ishihara et al [12], where the sensitivity of grid resolution with 2 km, 666 m, and 222 m was conducted at Choshi with the same turbulence model and parameters They mentioned that the predicted monthly average wind speed was almost the same between 2 km and 222 m resolution, which indicates that the larger relative error does not come from the grid resolution but may come from the land-use and sea surface temperature datasets. The the predicted predicted annual annual and and seasonal seasonal average average wind wind speeds speeds are are evaluated
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