Preliminary Investigations on Uncertainty Analysis of Wind-Wave Predictions in Lake Michigan

TL;DR

This study assesses how local wind variations impact the accuracy of wave predictions in Lake Michigan using the SWAN model, highlighting the importance of accounting for small-scale wind effects to reduce uncertainty.

Contribution

It investigates the influence of local wind effects on wave prediction accuracy and emphasizes the need to consider input uncertainties in numerical wave models.

Findings

Small-scale wind effects can increase prediction errors by up to 35%.

Wave models can accurately reveal wave variation time scales.

Insufficient meteorological stations lead to discrepancies in predictions.

Abstract

With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data were analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects were compared during a period where wind had a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase error in predictions up to 35%. Wave frequency and coherence analysis showed that both models are able to reveal the time scale of the wave variation with same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.