This study addresses the challenge of integrating ship-based lidar measurements with numerical weather prediction models to improve offshore wind characterisation. Accurate wind measurements are vital for the development of offshore wind energy, yet traditionally used fixed devices, such as meteorological masts and platform- or buoy-based lidars, are expensive and scarce. Ship-based lidar systems offer a flexible, cost-effective alternative by collecting wind data over large areas; however, the non-stationarity of ships results in low data density at any specific location. To overcome this challenge, we propose a novel calibration methodology to assimilate ship-mounted lidar observations into the ERA5 reanalysis by statistically adjusting its wind speed outputs. Inspired by observational nudging, which influences model state variables over time to match observational data, our approach applies a weighted correction directly to the model’s wind speed output, preserving the model’s underlying physics while ensuring computational efficiency and flexibility. The calibration parameters, including calibration strength, temporal window, and spatial radius of influence, were optimised to maximise the impact and accuracy of the calibration process. The comparison between ERA5 before and after the calibration demonstrates that the methodology effectively reduces the systematic underestimation of wind speeds, particularly in coastal regions where ERA5 struggles with complex flow dynamics. The methodology has been validated against independent measurements from a fixed Doppler lidar system deployed on an island in the northern Baltic Sea, demonstrating the calibration’s effectiveness in reducing bias and error spread at this location as well. However, it highlights that the calibration effect is strongly dependent on the distance between the ship and the lidar station, with a bias reduction of 0.2 m s^-1 when the ship is within 60 km, compared to 0.05 m s^-1 when considering data within 90 km, as a consequence of the intermittent influence of the ship-based lidar data.