Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model
Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical do...
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ftdoajarticles:oai:doaj.org/article:ec8b08fa72fb4e3ba6730d41a51c6927 2023-05-15T17:41:30+02:00 Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model Wagner Costa Déborah Idier Jérémy Rohmer Melisa Menendez Paula Camus 2020-12-01T00:00:00Z https://doi.org/10.3390/jmse8121028 https://doaj.org/article/ec8b08fa72fb4e3ba6730d41a51c6927 EN eng MDPI AG https://www.mdpi.com/2077-1312/8/12/1028 https://doaj.org/toc/2077-1312 doi:10.3390/jmse8121028 2077-1312 https://doaj.org/article/ec8b08fa72fb4e3ba6730d41a51c6927 Journal of Marine Science and Engineering, Vol 8, Iss 1028, p 1028 (2020) statistical downscaling weather types storm surge fully supervised classification Xynthia storm Joachim storm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 article 2020 ftdoajarticles https://doi.org/10.3390/jmse8121028 2022-12-31T05:40:24Z Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%). Article in Journal/Newspaper Northeast Atlantic Directory of Open Access Journals: DOAJ Articles Journal of Marine Science and Engineering 8 12 1028 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
statistical downscaling weather types storm surge fully supervised classification Xynthia storm Joachim storm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
spellingShingle |
statistical downscaling weather types storm surge fully supervised classification Xynthia storm Joachim storm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Wagner Costa Déborah Idier Jérémy Rohmer Melisa Menendez Paula Camus Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
topic_facet |
statistical downscaling weather types storm surge fully supervised classification Xynthia storm Joachim storm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
description |
Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%). |
format |
Article in Journal/Newspaper |
author |
Wagner Costa Déborah Idier Jérémy Rohmer Melisa Menendez Paula Camus |
author_facet |
Wagner Costa Déborah Idier Jérémy Rohmer Melisa Menendez Paula Camus |
author_sort |
Wagner Costa |
title |
Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
title_short |
Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
title_full |
Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
title_fullStr |
Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
title_full_unstemmed |
Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model |
title_sort |
statistical prediction of extreme storm surges based on a fully supervised weather-type downscaling model |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/jmse8121028 https://doaj.org/article/ec8b08fa72fb4e3ba6730d41a51c6927 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_source |
Journal of Marine Science and Engineering, Vol 8, Iss 1028, p 1028 (2020) |
op_relation |
https://www.mdpi.com/2077-1312/8/12/1028 https://doaj.org/toc/2077-1312 doi:10.3390/jmse8121028 2077-1312 https://doaj.org/article/ec8b08fa72fb4e3ba6730d41a51c6927 |
op_doi |
https://doi.org/10.3390/jmse8121028 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
8 |
container_issue |
12 |
container_start_page |
1028 |
_version_ |
1766143105790115840 |