Editorial Type:
Article
Article Type:
Research Article

A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast

Keith J. Roberts School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York

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Brian A. Colle School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York

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Nickitas Georgas Davidson Laboratory, Stevens Institute of Technology, Hoboken, New Jersey

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Stephan B. Munch Southwest Fisheries Science Center, La Jolla, California

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0314.1
Page(s):
1773–1791
Restricted access

Abstract

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.

Corresponding author address: Keith Roberts, School of Marine and Atmospheric Sciences, Stony Brook University/SUNY, Stony Brook, NY 11794-5000. E-mail: keithrbt0@gmail.com

Abstract

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.

Corresponding author address: Keith Roberts, School of Marine and Atmospheric Sciences, Stony Brook University/SUNY, Stony Brook, NY 11794-5000. E-mail: keithrbt0@gmail.com
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