![]() Models forced with remotely sensed predictors showed a slightly better performance (average correlation of 0.69) than models forced with predictors obtained from reanalysis products (average correlation of 0.68). For extreme events, the average correlation decreases to 0.54 (0.33) and RMSE increases to 14.5 (13.1) cm for extratropical (tropical) regions. Data-driven models simulate daily maximum surge better in extratropical and sub-tropical regions, than in the tropics (average correlation and RMSE of 0.45 and 5.3 cm, respectively). ![]() A multitude of predictors (obtained from remote sensing and climate reanalysis) along with predictands (from tide gage observations and storm surge reanalysis) are utilized to train and validate data-driven models to simulate daily maximum surge for the global coastline. This study explores the potential of data-driven models to simulate storm surges globally. Storm surges can be simulated using numerical models that are based on the underlying physical processes, or by using data-driven models that quantify the relationship between the predictand (storm surge) and relevant predictors (wind speed, mean sea-level pressure, etc.). In many areas, storm surges caused by tropical or extratropical cyclones are the main contributors to critical extreme sea level events.
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