Dusek, G. and Seim, H., 2013. A probabilistic rip current forecast model
A probabilistic rip current forecast model is created using a logistic regression formulation. Given input variables of nearshore significant wave height, vector mean wave direction, tidal elevation, and whether the forecast occurs in a 72-hour postwave event window, the probabilistic model predicts the likelihood of hazardous rip current occurrence. The model is trained using rip current observations made by lifeguards at Kill Devil Hills (KDH), North Carolina, in 2008–2009. Validation uses a hindcast at KDH over the summers of 2001–2007 and is compared with a hindcast of the National Weather Service (NWS) rip current model presently used at KDH. Using rip current rescues to indicate hazardous rip current occurrence, the probabilistic model has a Brier Score of 0.15 (0 is perfect prediction), compared with a minimum Brier Score of 0.45 for the NWS model, and represents a 67% or better improvement in prediction. Utilizing deepwater wave observations, instead of those collected nearshore, decreases model improvement by roughly half. The probabilistic model also outperforms the NWS model during high rescue instances, particularly on days with large tidal ranges. Functional improvements of the probabilistic model include the output of a true probabilistic forecast compared with a categorical forecast (low-medium-high) and the inclusion of only physically and statistically significant predictors.