TY - JOUR
T1 - Ensemble-based storm surge forecasting models
AU - Salighehdar, Amin
AU - Ye, Ziwen
AU - Liu, Mingzhe
AU - Florescu, Ionut
AU - Blumberg, Alan F.
N1 - Publisher Copyright:
© 2017 American Meteorological Society.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Accurate prediction of storm surge is a difficult problem. Most forecast systems produce multiple possible forecasts depending on the variability in weather conditions, possible temperature levels, winds, etc. Ensemble modeling techniques have been developed with the stated purpose of obtaining the best forecast (in some specific sense) from the individual forecasts. In this work a statistical methodology of evaluating the performance of multiple ensemble forecasting models is developed. The methodology is applied to predicting storm surge in the New York Harbor area. Data from three hurricane events collected from multiple locations in the New York Bay area are used. The methodology produces three key findings for the particular test data used. First, it is found that even the simplest possible way of creating an ensemble produces results superior to those of any single forecast. Second, for the data used and the events under study the methodology did not interact with any event at any location studied. Third, based on the methodology results for the data studied selecting the best-performing ensemble models for each specific location may be possible.
AB - Accurate prediction of storm surge is a difficult problem. Most forecast systems produce multiple possible forecasts depending on the variability in weather conditions, possible temperature levels, winds, etc. Ensemble modeling techniques have been developed with the stated purpose of obtaining the best forecast (in some specific sense) from the individual forecasts. In this work a statistical methodology of evaluating the performance of multiple ensemble forecasting models is developed. The methodology is applied to predicting storm surge in the New York Harbor area. Data from three hurricane events collected from multiple locations in the New York Bay area are used. The methodology produces three key findings for the particular test data used. First, it is found that even the simplest possible way of creating an ensemble produces results superior to those of any single forecast. Second, for the data used and the events under study the methodology did not interact with any event at any location studied. Third, based on the methodology results for the data studied selecting the best-performing ensemble models for each specific location may be possible.
KW - Ensembles
KW - Forecasting
KW - Forecasting techniques
KW - Probability forecasts/models/distribution
KW - Statistical forecasting
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U2 - 10.1175/WAF-D-17-0017.1
DO - 10.1175/WAF-D-17-0017.1
M3 - Article
AN - SCOPUS:85032301006
SN - 0882-8156
VL - 32
SP - 1921
EP - 1936
JO - Weather and Forecasting
JF - Weather and Forecasting
IS - 5
ER -