Ensemble-Based Storm Surge Forecasting Models

Ensemble-Based Storm Surge Forecasting Models AbstractAccurate 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Ensemble-Based Storm Surge Forecasting Models

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0017.1
Publisher site
See Article on Publisher Site

Abstract

AbstractAccurate 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.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Oct 3, 2017

References

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