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J. Mar. Sci. Eng. 2017, 5(4), 59; https://doi.org/10.3390/jmse5040059

Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

Davidson Laboratory, Stevens Institute of Technology, Hoboken 07030, NJ, USA
Received: 15 October 2017 / Revised: 27 November 2017 / Accepted: 28 November 2017 / Published: 15 December 2017
(This article belongs to the Special Issue Coastal Hazards Related to Water)
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Abstract

In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts. View Full-Text
Keywords: ensemble forecasting; coastal flood forecasting; cluster analysis; forecast skill; Hurricane Sandy; bimodal forecast ensemble forecasting; coastal flood forecasting; cluster analysis; forecast skill; Hurricane Sandy; bimodal forecast
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Schulte, J.A. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy. J. Mar. Sci. Eng. 2017, 5, 59.

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