A multivariate extreme wave and storm surge climate emulator based on weather patterns. (August 2016) - Rueda, A.; Camus, P.; Tomás, A.; Vitousek, S.; Méndez, F.J.
Highlights: A new climate-based approach for multivariate extreme events is presented. The model is based on a predictor-to-predictand weather types (WTs) classification. Non-stationarity is considered through changes in the occurrence probabilities of each WT. A Gaussian copula is used to account for the statistical dependence between variables. The model allows the identification of WTs related to extreme coastal flooding events. Abstract: Coastal floods often coincide with large waves, storm surge and tides. Thus, joint probability methods are needed to properly characterize extreme sea levels. This work introduces a statistical downscaling framework for multivariate extremes that relates the non-stationary behavior of coastal flooding events to the occurrence probability of daily weather patterns. The proposed method is based on recently-developed weather-type methods to predict extreme events (e.g., significant wave height, mean wave period, surge level) from large-scale sea-level pressure fields. For each weather type, variables of interest are modeled using Generalized Extreme Value (GEV) distributions and a Gaussian copula for modelling the interdependence between variables. The statistical dependence between consecutive days is addressed by defining a climate-based extremal index for each weather type. This work allows attribution of extreme events to specific weather conditions, enhancing the knowledge of climate-driven coastal flooding.