Ensemble-based storm surge forecasting models

Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1921-1936
Number of pages16
JournalWeather and Forecasting
Volume32
Issue number5
DOIs
StatePublished - 1 Oct 2017

Keywords

  • Ensembles
  • Forecasting
  • Forecasting techniques
  • Probability forecasts/models/distribution
  • Statistical forecasting

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