IBM Research - Ireland Internship Project: Ensemble based forecasting of wave conditions - overview


Ensemble techniques have been demonstrated to outperform individual models in operational forecasting and minimising prediction errors (Mallet and Sportisse, 2006). This is particularly relevant for forecasting wave conditions in coastal ocean regions subject to model errors arising from incorrect forcing data, model parametrizations and model structural errors (Rogers et al., 2005).


In this study we aim to combine physics models of near-shore circulation and wave characteristics with ensemble forecasting methods to generate optimal forecasts with defined uncertainty. The approach is applied to a case-study site, Santa Cruz, California. The system involves a coupled wave model and circulation model. Circulation patterns are resolved by EFDC, a 3D circulation model, while wave information is computed using SWAN, a third-generation wave model that computes wind-generated waves in coastal and inland waters. Input data includes a high-resolution meteorological field with predictions highly sensitive to the accuracy of wind fields.


We aim to investigate methodologies to optimally combine multiple forecasts of wave characteristics. We investigate different linear combinations of models to improve performance of model-data comparisons. The weights attached to these models are investigated and techniques to select and forecast optimum weights evaluated


Required Skills:

The ideal candidate will have experience in numerical modelling and the Linux/unix environment. In addition, the ability to analyse large datasets combing basic statistics with choice of analysis software (R, Python, etc.) is useful.



Mallet, V., Sportisse, B., 2006. Ensemble-based air quality forecasts: A multimodel approach applied to ozone. J. Geophys. Res. Atmospheres 111.

Rogers, W.E., Wittmann, P.A., Wang, D.W., Clancy, R.M., Hsu, Y.L., 2005. Evaluations of Global Wave Prediction at the Fleet Numerical Meteorology and Oceanography Center*. Weather Forecast. 20, 745–760.