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Fast Ensemble Forecast of Storm Surge along the Coast of China

Xuelin Ding, Yongping Chen, Yi Pan, Dominic Reeve Orcid Logo

Journal of Coastal Research, Volume: 75, Issue: sp1, Pages: 1077 - 1081

Swansea University Author: Dominic Reeve Orcid Logo

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DOI (Published version): 10.2112/SI75-216.1

Abstract

The uncertainties in typhoon wind field forecasts may introduce significant errors in storm surge forecasts. The common method to tackle this problem is based on ensemble forecasting of a typhoon wind field by using different initial and/or boundary conditions in the adopted weather forecast model....

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Published in: Journal of Coastal Research
ISSN: 0749-0208 1551-5036
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa30243
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first_indexed 2016-09-28T13:00:15Z
last_indexed 2018-02-09T05:16:01Z
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spelling 2016-09-28T09:29:15.9119656 v2 30243 2016-09-28 Fast Ensemble Forecast of Storm Surge along the Coast of China 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2016-09-28 CIVL The uncertainties in typhoon wind field forecasts may introduce significant errors in storm surge forecasts. The common method to tackle this problem is based on ensemble forecasting of a typhoon wind field by using different initial and/or boundary conditions in the adopted weather forecast model. However, this method demands very high computational costs and therefore may not always be acceptable for operational use. In order to improve time efficiency, this paper introduces a new method which mainly relies on the forecast results from different weather forecast centres. With the bias modification, the control typhoon forecast is first generated by the weighted averaging of forecast results from individual forecast centres. The weighted factor for each centre is calculated under a dynamic training scheme. The ensemble typhoon forecasts are then generated by combination of five different typhoon tracks and three different wind speeds around the control forecast. The ensemble storm surge forecasts are conducted by running a well-validated storm surge model driven by the wind fields obtained from the above ensemble typhoon forecasts. Since each storm surge forecast can be calculated independently, the ensemble storm surge forecast can be fast conducted without significant increase in computational time. The above method is applied to the forecasting of storm surge in 2013 along the coast of China. By comparison with the traditional forecast, the control forecast exhibits a higher accuracy, and the ensemble forecasts provide more types of forecast results, such as the occurrence probability of storm surge over a certain surge level, which are useful for the probabilistic decision of protection measures against storm surge. Journal Article Journal of Coastal Research 75 sp1 1077 1081 0749-0208 1551-5036 31 12 2016 2016-12-31 10.2112/SI75-216.1 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2016-09-28T09:29:15.9119656 2016-09-28T09:28:10.5628742 College of Engineering Engineering Xuelin Ding 1 Yongping Chen 2 Yi Pan 3 Dominic Reeve 0000-0003-1293-4743 4
title Fast Ensemble Forecast of Storm Surge along the Coast of China
spellingShingle Fast Ensemble Forecast of Storm Surge along the Coast of China
Dominic Reeve
title_short Fast Ensemble Forecast of Storm Surge along the Coast of China
title_full Fast Ensemble Forecast of Storm Surge along the Coast of China
title_fullStr Fast Ensemble Forecast of Storm Surge along the Coast of China
title_full_unstemmed Fast Ensemble Forecast of Storm Surge along the Coast of China
title_sort Fast Ensemble Forecast of Storm Surge along the Coast of China
author_id_str_mv 3e76fcc2bb3cde4ddee2c8edfd2f0082
author_id_fullname_str_mv 3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
author Dominic Reeve
author2 Xuelin Ding
Yongping Chen
Yi Pan
Dominic Reeve
format Journal article
container_title Journal of Coastal Research
container_volume 75
container_issue sp1
container_start_page 1077
publishDate 2016
institution Swansea University
issn 0749-0208
1551-5036
doi_str_mv 10.2112/SI75-216.1
college_str College of Engineering
hierarchytype
hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
document_store_str 0
active_str 0
description The uncertainties in typhoon wind field forecasts may introduce significant errors in storm surge forecasts. The common method to tackle this problem is based on ensemble forecasting of a typhoon wind field by using different initial and/or boundary conditions in the adopted weather forecast model. However, this method demands very high computational costs and therefore may not always be acceptable for operational use. In order to improve time efficiency, this paper introduces a new method which mainly relies on the forecast results from different weather forecast centres. With the bias modification, the control typhoon forecast is first generated by the weighted averaging of forecast results from individual forecast centres. The weighted factor for each centre is calculated under a dynamic training scheme. The ensemble typhoon forecasts are then generated by combination of five different typhoon tracks and three different wind speeds around the control forecast. The ensemble storm surge forecasts are conducted by running a well-validated storm surge model driven by the wind fields obtained from the above ensemble typhoon forecasts. Since each storm surge forecast can be calculated independently, the ensemble storm surge forecast can be fast conducted without significant increase in computational time. The above method is applied to the forecasting of storm surge in 2013 along the coast of China. By comparison with the traditional forecast, the control forecast exhibits a higher accuracy, and the ensemble forecasts provide more types of forecast results, such as the occurrence probability of storm surge over a certain surge level, which are useful for the probabilistic decision of protection measures against storm surge.
published_date 2016-12-31T03:41:56Z
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score 10.898073