Evaluation and Challenges Using the Nearshore Wave Prediction System on Oahu, Hawaii, through the 2016–2017 Winter Season

Abstract for the 16^th Symposium on the Coastal Environment, 98^th AMS Annual Meeting, Austin, TX, Jan 7-11, 2018

Evaluation and challenges using the Nearshore Wave Prediction System on Oahu, Hawaii through the 2016-2017 winter season

Alex Gibbs, Andre van der Westhuysen, Stylianos Flampouris, Patrick Caldwell, Pablo Santos, Roberto Padilla-Hernandez

An evaluation of the Nearshore Wave Prediction System (NWPS, Van der Westhuysen et al. 2016) in the Hawaiian Islands during a winter season was a critical step in determining the validity of the system in a swell-dominated region. It also aimed to distinguish any trends or biases to take into account during operational marine forecasting. The wave model SWAN (Booij et al. 1999), was used as the nearshore wave model within NWPS, and included the global operational multi-gridded WAVEWATCH III® wave model (WW3, Tolman et al. 2002; Chawla et al. 2013) for boundary conditions. The model was run on-demand and used wind input from the National Weather Service (NWS) Forecast Office in Honolulu, HI to ensure wind and wave consistency in operational marine forecasts through the period. Nearshore observations from the Pacific Islands Ocean Observing System (PacIOOS) buoy (51201), positioned near Waimea Bay along the North Shore of Oahu, were used through the 2016-2017 winter season to evaluate the modeling system. The validation results show that the model performs sufficiently well overall, with a mean absolute error (MAE) in significant wave height of 0.25 m and a mean error of -0.02 m. However, the analysis also revealed systematic problem areas related to model biases with large swell events. Considering the nested nature of the high-resolution NWPS domain over Hawaii, these biases are in turn strongly related to the accuracy of the offshore wave boundary conditions received from the global WW3 model. This talk will present a seasonal overview for 2016-2017, the results of the validation period, and it will discuss the potential solutions to the swell biases identified, including the ongoing wave data assimilation efforts at the NWS.