Cargando…

Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters

In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typh...

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Caiming, Ren, Fumin, Zhang, Da-Lin, Zhu, Jing, McBride, John Leonard, Chen, Yuxu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533520/
https://www.ncbi.nlm.nih.gov/pubmed/37758776
http://dx.doi.org/10.1038/s41598-023-43415-0
_version_ 1785112200928559104
author Wu, Caiming
Ren, Fumin
Zhang, Da-Lin
Zhu, Jing
McBride, John Leonard
Chen, Yuxu
author_facet Wu, Caiming
Ren, Fumin
Zhang, Da-Lin
Zhu, Jing
McBride, John Leonard
Chen, Yuxu
author_sort Wu, Caiming
collection PubMed
description In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.
format Online
Article
Text
id pubmed-10533520
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-105335202023-09-29 Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters Wu, Caiming Ren, Fumin Zhang, Da-Lin Zhu, Jing McBride, John Leonard Chen, Yuxu Sci Rep Article In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons. Nature Publishing Group UK 2023-09-27 /pmc/articles/PMC10533520/ /pubmed/37758776 http://dx.doi.org/10.1038/s41598-023-43415-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wu, Caiming
Ren, Fumin
Zhang, Da-Lin
Zhu, Jing
McBride, John Leonard
Chen, Yuxu
Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title_full Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title_fullStr Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title_full_unstemmed Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title_short Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
title_sort development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533520/
https://www.ncbi.nlm.nih.gov/pubmed/37758776
http://dx.doi.org/10.1038/s41598-023-43415-0
work_keys_str_mv AT wucaiming developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters
AT renfumin developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters
AT zhangdalin developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters
AT zhujing developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters
AT mcbridejohnleonard developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters
AT chenyuxu developmentofadynamicalstatisticalanalogensembleforecastmodelforlandfallingtyphoondisasters