Quantifying impacts of forecast uncertainties on predicted storm surges

Donald T. Resio (), Nancy Powell, Mary Cialone, Himangshu S. Das and Joannes J. Westerink
Additional contact information
Donald T. Resio: University of North Florida
Nancy Powell: ARCADIS
Mary Cialone: Engineering Research & Development Center
Himangshu S. Das: Jackson State University
Joannes J. Westerink: University of Notre Dame

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 88, issue 3, No 7, 1423-1449

Abstract: Abstract In this paper, we propose a framework for quantifying risks, including (1) the effects of forecast errors, (2) the ability to resolve critical grid features that are important to accurate site-specific forecasts, and (3) a framework that can move us toward performance-based/cost-based decisions, within an extremely fast execution time. A key element presently lacking in previous studies is the interrelationship between the effects of combined random errors and bias in numerical weather prediction (NWP) models and bias and random errors in surge models. This approach examines the number of degrees of freedom in present forecasts and develops an equation for the quantification of these types of errors within a unified system, given the number of degrees of freedom in the NWP forecasts. It is shown that the methodology can be used to provide information on the forecasts and along with the combined uncertainty due to all of the individual contributions. A potential important benefit from studies using this approach would be the ability to estimate financial and other trade-offs between higher-cost “rapid” evacuation methods and lower-cost “slower” evacuation methods. Analyses here show that uncertainty inherent in these decisions depends strongly on forecast time and geographic location. Methods based on sets of surge maxima do not capture this uncertainty and would be difficult to use for this purpose. In particular, it is shown that surge model bias can play a dominant role in distorting the forecast probabilities.

Keywords: Storm surge; Forecasting; Quantifying risk; Uncertainty (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-017-2924-1

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