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A Statistical Model for Post‐Tropical Cyclone Hazard Assessment.

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Title: A Statistical Model for Post‐Tropical Cyclone Hazard Assessment.
Authors: Li, Dian‐Yi1 (AUTHOR) dianyi.li@stonybrook.edu, Reed, Kevin A.1 (AUTHOR), Camargo, Suzana J.2,3 (AUTHOR), Lee, Chia‐Ying3 (AUTHOR), Sobel, Adam H.4,5 (AUTHOR), Zarzycki, Colin M.6 (AUTHOR), Bieli, Melanie7 (AUTHOR)
Source: Journal of Geophysical Research. Atmospheres. 3/28/2026, Vol. 131 Issue 6, p1-20. 20p.
Subject Terms: *Ocean, *Climatology, Statistical models, Tropical cyclones, Risk assessment, Cyclone forecasting
Geographic Terms: North Atlantic Ocean, Atlantic Ocean, Europe, North America
Abstract: Post‐tropical cyclones (PTCs) induce significant hazards in the mid‐latitudes, yet are often misrepresented in storm hazard models. This study introduces a new statistical model for assessing PTC hazard in the North Atlantic (NA). The model comprises three components: a logistic regression model for PTC identification, an auto‐regressive model for PTC intensity prediction, and a probability‐based PTC lysis model. The PTC identification component is adapted from a statistical model developed earlier by Bieli et al. (2020), https://doi.org/10.1175/waf‐d‐19‐0045.1, while the PTC intensity and lysis components are newly developed. These components are coupled with synthetic tropical cyclone (TC) tracks and intensities generated by the Columbia Hazard Model (CHAZ) to simulate the full life cycle of PTCs, with flexibility for integration with other TC hazard models. The model reproduces key features of Atlantic PTC climatology, including timing of extratropical transition completion, intensity distributions, and spatial track density, and enables robust estimation of PTC return periods. It reveals lower risks of intense PTCs and higher risks of weak PTCs along the western NA near North America relative to the eastern NA near Europe. In some cases, the return periods of landfalling intense PTCs along the western side can be up to 4.4 times the eastern side, when PTCs stronger than 942 hPa are considered. Notably, comparable return periods between PTC‐only and storms at all phases near Europe and Canada underscore the importance of explicitly representing PTCs in hazard assessments for these regions. The framework is designed to support assessments of future Atlantic PTC hazard and risk. Plain Language Summary: Tropical cyclones that move into higher latitudes can change in structure and become post‐tropical cyclones (PTCs). These storms can be very dangerous, bringing high winds and heavy rain to regions far from the tropics. However, most current storm hazard models do not explicitly represent this phase of the storm life cycle. In this study, we develop a new model that explicitly simulates the life cycle of PTCs over the North Atlantic after they complete their transition from tropical cyclones. Our model uses a combination of existing storm track simulations and new statistical methods to predict when a storm changes phase, how strong it becomes, and when it undergoes lysis. We find that the model performs well in reproducing the observed behavior of PTCs in the Atlantic and can estimate how often PTCs may occur. It shows that intense PTCs are less likely, and weak PTCs more likely, to impact the western side of the Atlantic, near North America, than the eastern side near Europe. The findings also highlight the importance of PTCs in European and Canadian hazard assessments. This new model can also be applied to future climate scenarios to help assess potential changes in Atlantic PTC‐related hazards. Key Points: A new statistical model simulates North Atlantic post‐tropical cyclones (PTCs) using PTC identification, intensity, and lysis componentsThe model captures observed PTC climatology and can assess future hazards using environmental and storm‐scale inputsThe model identifies fewer hazards of intense PTCs along the North American coast than in Europe [ABSTRACT FROM AUTHOR]
Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Statistical Model for Post‐Tropical Cyclone Hazard Assessment.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Dian‐Yi%22">Li, Dian‐Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dianyi.li@stonybrook.edu</i><br /><searchLink fieldCode="AR" term="%22Reed%2C+Kevin+A%2E%22">Reed, Kevin A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Camargo%2C+Suzana+J%2E%22">Camargo, Suzana J.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Chia‐Ying%22">Lee, Chia‐Ying</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sobel%2C+Adam+H%2E%22">Sobel, Adam H.</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zarzycki%2C+Colin+M%2E%22">Zarzycki, Colin M.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bieli%2C+Melanie%22">Bieli, Melanie</searchLink><relatesTo>7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Atmospheres%22">Journal of Geophysical Research. Atmospheres</searchLink>. 3/28/2026, Vol. 131 Issue 6, p1-20. 20p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Ocean%22">Ocean</searchLink><br />*<searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Tropical+cyclones%22">Tropical cyclones</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Cyclone+forecasting%22">Cyclone forecasting</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22North+Atlantic+Ocean%22">North Atlantic Ocean</searchLink><br /><searchLink fieldCode="DE" term="%22Atlantic+Ocean%22">Atlantic Ocean</searchLink><br /><searchLink fieldCode="DE" term="%22Europe%22">Europe</searchLink><br /><searchLink fieldCode="DE" term="%22North+America%22">North America</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Post‐tropical cyclones (PTCs) induce significant hazards in the mid‐latitudes, yet are often misrepresented in storm hazard models. This study introduces a new statistical model for assessing PTC hazard in the North Atlantic (NA). The model comprises three components: a logistic regression model for PTC identification, an auto‐regressive model for PTC intensity prediction, and a probability‐based PTC lysis model. The PTC identification component is adapted from a statistical model developed earlier by Bieli et al. (2020), https://doi.org/10.1175/waf‐d‐19‐0045.1, while the PTC intensity and lysis components are newly developed. These components are coupled with synthetic tropical cyclone (TC) tracks and intensities generated by the Columbia Hazard Model (CHAZ) to simulate the full life cycle of PTCs, with flexibility for integration with other TC hazard models. The model reproduces key features of Atlantic PTC climatology, including timing of extratropical transition completion, intensity distributions, and spatial track density, and enables robust estimation of PTC return periods. It reveals lower risks of intense PTCs and higher risks of weak PTCs along the western NA near North America relative to the eastern NA near Europe. In some cases, the return periods of landfalling intense PTCs along the western side can be up to 4.4 times the eastern side, when PTCs stronger than 942 hPa are considered. Notably, comparable return periods between PTC‐only and storms at all phases near Europe and Canada underscore the importance of explicitly representing PTCs in hazard assessments for these regions. The framework is designed to support assessments of future Atlantic PTC hazard and risk. Plain Language Summary: Tropical cyclones that move into higher latitudes can change in structure and become post‐tropical cyclones (PTCs). These storms can be very dangerous, bringing high winds and heavy rain to regions far from the tropics. However, most current storm hazard models do not explicitly represent this phase of the storm life cycle. In this study, we develop a new model that explicitly simulates the life cycle of PTCs over the North Atlantic after they complete their transition from tropical cyclones. Our model uses a combination of existing storm track simulations and new statistical methods to predict when a storm changes phase, how strong it becomes, and when it undergoes lysis. We find that the model performs well in reproducing the observed behavior of PTCs in the Atlantic and can estimate how often PTCs may occur. It shows that intense PTCs are less likely, and weak PTCs more likely, to impact the western side of the Atlantic, near North America, than the eastern side near Europe. The findings also highlight the importance of PTCs in European and Canadian hazard assessments. This new model can also be applied to future climate scenarios to help assess potential changes in Atlantic PTC‐related hazards. Key Points: A new statistical model simulates North Atlantic post‐tropical cyclones (PTCs) using PTC identification, intensity, and lysis componentsThe model captures observed PTC climatology and can assess future hazards using environmental and storm‐scale inputsThe model identifies fewer hazards of intense PTCs along the North American coast than in Europe [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=192556319
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1029/2025JD044936
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 1
    Subjects:
      – SubjectFull: Ocean
        Type: general
      – SubjectFull: Climatology
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Tropical cyclones
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Cyclone forecasting
        Type: general
      – SubjectFull: North Atlantic Ocean
        Type: general
      – SubjectFull: Atlantic Ocean
        Type: general
      – SubjectFull: Europe
        Type: general
      – SubjectFull: North America
        Type: general
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      – TitleFull: A Statistical Model for Post‐Tropical Cyclone Hazard Assessment.
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            NameFull: Li, Dian‐Yi
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            NameFull: Reed, Kevin A.
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              M: 03
              Text: 3/28/2026
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              Y: 2026
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