Experimental Wave Predictions

The Great Lakes Experimental Wave Model was developed based off of the operational Great Lakes Wave Unstructured v2.1 (GLWUv2.1) model, built using WAVEWATCH III, and available at https://www.weather.gov/greatlakes (Abdolali et al. 2024).

This modified experimental model configuration incorporates an ice-wave damping scheme intended to provide more accurate and comprehensive estimates of wave conditions in the presence of ice cover, and makes minor changes to the model mesh (Hu et al. 2025). Wind forcing is from the HRRR for the first 48 hours and GFS for the remainder of the forecast window. Ice forcing is based off of the US NIC Great Lakes Ice Charts and held constant through the forecast window.

The Great Lakes Experimental Wave Model runs two times per day, each producing 3-day forecasts of wave heights throughout the Great Lakes. Recent nowcast data is available for a 2-week rolling window, and recent forecast data is available for a 3-day rolling window. Because this is not an operational system, we are unable to provide model archives outside of these rolling real-time windows.

Abdolali, A., Banihashemi, S., Alves, J.H., Roland, A., Hesser, T.J., Anderson Bryant, M. and McKee Smith, J., 2024. Great Lakes wave forecast system on high-resolution unstructured meshes. Geoscientific Model Development, 17(3), pp.1023-1039. https://doi.org/10.5194/gmd-17-1023-2024

Hu, H., Titze, D., Fujisaki-Manome, A., Mroczka, B., Wang, J., Hawley, N., Orendorf, S., Frank, K. and Ruberg, S., 2025. Winter ice‐wave modeling with WAVEWATCH III in Lake Erie. Journal of Geophysical Research: Oceans, 130(1). https://doi.org/10.1029/2024JC021146

The Great Lakes Experimental Machine Learning Wave Forecast is a point-based machine learning forecast, trained using NDBC Buoy Data with the eXtreme Gradient Boost (XGBoost) library, as described by Hu et al. 2021. The machine learning forecast has been shown to have high efficiency and skill, and is now available for buoy locations around the Great Lakes with sufficient historical records for model training.

Hu, H., van der Westhuysen, A.J., Chu, P. and Fujisaki-Manome, A., 2021. Predicting Lake Erie wave heights and periods using XGBoost and LSTM. Ocean Modelling, 164, p.101832. https://doi.org/10.1016/j.ocemod.2021.101832