Improving wind and power predictions via four-dimensional data assimilation in the WRF model

Abstract

Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore.