Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process
A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of intensification and associates each state with a probability distribution of intensity change. Th...
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ftdatacite:10.48550/arxiv.1808.05276 2023-05-15T17:33:40+02:00 Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process Jing, Renzhi Lin, Ning 2018 https://dx.doi.org/10.48550/arxiv.1808.05276 https://arxiv.org/abs/1808.05276 unknown arXiv https://dx.doi.org/10.1175/jcli-d-19-0027.1 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences article-journal Article ScholarlyArticle Text 2018 ftdatacite https://doi.org/10.48550/arxiv.1808.05276 https://doi.org/10.1175/jcli-d-19-0027.1 2022-04-01T09:13:26Z A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of intensification and associates each state with a probability distribution of intensity change. The storm's transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Markov environment-dependent hurricane intensity model (MeHiM) is used to simulate the evolution of storm intensity along the storm track over the ocean, and a simple decay model is added to estimate the intensity change when the storm moves over land. Data for the North Atlantic (NA) basin from 1979-2014 (555 storms) are used for model development and evaluation. Probability distributions of 6-h and 24-h intensity change, lifetime maximum intensity, and landfall intensity based on model simulations and observations compare well. Although the MeHiM is still limited in fully describing rapid intensification, it shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models). Text North Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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Applications stat.AP Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
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Applications stat.AP Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Jing, Renzhi Lin, Ning Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
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Applications stat.AP Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
description |
A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of intensification and associates each state with a probability distribution of intensity change. The storm's transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Markov environment-dependent hurricane intensity model (MeHiM) is used to simulate the evolution of storm intensity along the storm track over the ocean, and a simple decay model is added to estimate the intensity change when the storm moves over land. Data for the North Atlantic (NA) basin from 1979-2014 (555 storms) are used for model development and evaluation. Probability distributions of 6-h and 24-h intensity change, lifetime maximum intensity, and landfall intensity based on model simulations and observations compare well. Although the MeHiM is still limited in fully describing rapid intensification, it shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models). |
format |
Text |
author |
Jing, Renzhi Lin, Ning |
author_facet |
Jing, Renzhi Lin, Ning |
author_sort |
Jing, Renzhi |
title |
Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
title_short |
Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
title_full |
Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
title_fullStr |
Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
title_full_unstemmed |
Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process |
title_sort |
tropical cyclone intensity evolution modeled as a dependent hidden markov process |
publisher |
arXiv |
publishDate |
2018 |
url |
https://dx.doi.org/10.48550/arxiv.1808.05276 https://arxiv.org/abs/1808.05276 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://dx.doi.org/10.1175/jcli-d-19-0027.1 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1808.05276 https://doi.org/10.1175/jcli-d-19-0027.1 |
_version_ |
1766132245248081920 |