Sea state reconstruction based on the Spearman rank correlation using full-scale data from a containership

Sea state estimation plays a fundamental role in the maritime sector, as it provides essential information for the onboard decision support system. The employment of the wave buoy analogy to estimate the sea state conditions experienced by a ship is a widely investigated topic. The paper applies a recently developed parametric algorithm to a ship motion dataset, measured onboard a 2800 TEU containership. After a brief review of the procedure, the dataset is preliminarily analysed to discard all time-series not fulfilling the stationarity criterion. Subsequently, the sea state assessment procedure is applied to the post-processed dataset and compared with the reference sea state parameters provided by the onboard wave radar and the ERA5 data. The statistics of errors are determined to investigate the effectiveness of the wave buoy analogy method. A reasonable agreement is recognized between the sea state parameters obtained by the sea state reconstruction algorithm and corresponding ones provided by the onboard wave radar and ERA5. Moreover, the robustness of the method is tested by perturbing the entire transfer function dataset with an amplitude variability from ±1 to ±20 %. Finally, a novel criterion is presented to classify unimodal and bimodal sea state conditions.