A regression-based approach for cool-season storm surge predictions along the New York-New Jersey Coast

A multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October-31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York-New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%-38.0%. A bias-correction technique reduces the average mean absolute error by 10%-15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010-14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model's [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.