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Using remotely sensed data to modify wind forcing in operational storm surge forecasting

Using remotely sensed data to modify wind forcing in operational storm surge forecasting
Using remotely sensed data to modify wind forcing in operational storm surge forecasting
Storm surges are abnormal coastal sea level events caused by meteorological conditions such as tropical cyclones. They have the potential to cause widespread loss of life and financial damage and have done so on many occasions in the past. Accurate and timely forecasts are necessary to help mitigate the risks posed by these events. Operational forecasting models use discretisations of the governing equations for fluid flow to model the sea surface, which is then forced by surface stresses derived from a model wind and pressure fields. The wind fields are typically idealised and generated parametrically. In this study, wind field datasets derived from remotely sensed data are used to modify the model parametric wind forcing and investigate potential improvement to operational forecasting. We examine two methods for using analysis wind fields derived from remotely sensed observations of three hurricanes. Our first method simply replaces the parametric wind fields with its corresponding analysis wind field for a period of time. Our second method does this also but takes it further by attempting to use some of the information present in the analysis wind field to estimate future wind fields. We find that our methods do yield some forecast improvement, most notably for our second method where we get improvements of up to 0.29 m on average. Importantly, the spatial structure of the surge is changed in some places such that locations that were previously forecast small surges had their water levels increased. These results were validated by tide gauge data.
0921-030X
275–293
Byrne, David
e5a86e6b-c93b-4fbf-91a7-2200b5e67dbf
Horsburgh, Kevin
ae10571e-7441-4cfa-b44d-e8fa7bce7cce
Zachry, Brian
7bf5ec37-0341-4d6b-9515-e0041a891a74
Cipollini, Paolo
276e356a-f29e-4192-98b3-9340b491dab8
Byrne, David
e5a86e6b-c93b-4fbf-91a7-2200b5e67dbf
Horsburgh, Kevin
ae10571e-7441-4cfa-b44d-e8fa7bce7cce
Zachry, Brian
7bf5ec37-0341-4d6b-9515-e0041a891a74
Cipollini, Paolo
276e356a-f29e-4192-98b3-9340b491dab8

Byrne, David, Horsburgh, Kevin, Zachry, Brian and Cipollini, Paolo (2017) Using remotely sensed data to modify wind forcing in operational storm surge forecasting. Natural Hazards, 89 (1), 275–293. (doi:10.1007/s11069-017-2964-6).

Record type: Article

Abstract

Storm surges are abnormal coastal sea level events caused by meteorological conditions such as tropical cyclones. They have the potential to cause widespread loss of life and financial damage and have done so on many occasions in the past. Accurate and timely forecasts are necessary to help mitigate the risks posed by these events. Operational forecasting models use discretisations of the governing equations for fluid flow to model the sea surface, which is then forced by surface stresses derived from a model wind and pressure fields. The wind fields are typically idealised and generated parametrically. In this study, wind field datasets derived from remotely sensed data are used to modify the model parametric wind forcing and investigate potential improvement to operational forecasting. We examine two methods for using analysis wind fields derived from remotely sensed observations of three hurricanes. Our first method simply replaces the parametric wind fields with its corresponding analysis wind field for a period of time. Our second method does this also but takes it further by attempting to use some of the information present in the analysis wind field to estimate future wind fields. We find that our methods do yield some forecast improvement, most notably for our second method where we get improvements of up to 0.29 m on average. Importantly, the spatial structure of the surge is changed in some places such that locations that were previously forecast small surges had their water levels increased. These results were validated by tide gauge data.

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More information

Accepted/In Press date: 8 June 2017
e-pub ahead of print date: 18 July 2017
Published date: 1 October 2017

Identifiers

Local EPrints ID: 413246
URI: https://eprints.soton.ac.uk/id/eprint/413246
ISSN: 0921-030X
PURE UUID: 63e339a4-df3d-4bb1-8948-3f5a8e640f85

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Date deposited: 18 Aug 2017 16:31
Last modified: 13 Mar 2019 19:32

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Contributors

Author: David Byrne
Author: Kevin Horsburgh
Author: Brian Zachry
Author: Paolo Cipollini

University divisions

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