View in EDS HTML Full Text PDF Full Text

Tropical Cyclone Inner Core Influence on Intensity Change and Its Forecasting by the LGBM Model in the Northwestern Pacific.

Saved in:
Bibliographic Details
Title: Tropical Cyclone Inner Core Influence on Intensity Change and Its Forecasting by the LGBM Model in the Northwestern Pacific.
Authors: Zhang, Jiali1,2 (AUTHOR), Li, Qinglan1 (AUTHOR) ql.li@siat.ac.cn, Li, Chao3 (AUTHOR), Qu, Ankang1,2 (AUTHOR), Zhou, Junwei1 (AUTHOR), Lyu, Xinyan4 (AUTHOR), Qian, Qifeng4 (AUTHOR), Liu, Changjie5 (AUTHOR), Shufeng, Xi5 (AUTHOR)
Source: Weather & Forecasting. Apr2026, Vol. 41 Issue 4, p695-709. 15p.
Subjects: Cyclone forecasting, Machine learning, Tropical cyclones, Weather, Forecasting, Remote sensing, Ocean
Geographic Terms: Pacific Ocean
Abstract: Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement: This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting. [ABSTRACT FROM AUTHOR]
Copyright of Weather & Forecasting is the property of American Meteorological Society 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.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 193865882
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Tropical Cyclone Inner Core Influence on Intensity Change and Its Forecasting by the LGBM Model in the Northwestern Pacific.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Jiali%22">Zhang, Jiali</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Qinglan%22">Li, Qinglan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ql.li@siat.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Chao%22">Li, Chao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qu%2C+Ankang%22">Qu, Ankang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Junwei%22">Zhou, Junwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lyu%2C+Xinyan%22">Lyu, Xinyan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qian%2C+Qifeng%22">Qian, Qifeng</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Changjie%22">Liu, Changjie</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shufeng%2C+Xi%22">Shufeng, Xi</searchLink><relatesTo>5</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Weather+%26+Forecasting%22">Weather & Forecasting</searchLink>. Apr2026, Vol. 41 Issue 4, p695-709. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Cyclone+forecasting%22">Cyclone forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tropical+cyclones%22">Tropical cyclones</searchLink><br /><searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean%22">Ocean</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Pacific+Ocean%22">Pacific Ocean</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement: This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Weather & Forecasting is the property of American Meteorological Society 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=egs&AN=193865882
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1175/WAF-D-25-0113.1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 695
    Subjects:
      – SubjectFull: Cyclone forecasting
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Tropical cyclones
        Type: general
      – SubjectFull: Weather
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Ocean
        Type: general
      – SubjectFull: Pacific Ocean
        Type: general
    Titles:
      – TitleFull: Tropical Cyclone Inner Core Influence on Intensity Change and Its Forecasting by the LGBM Model in the Northwestern Pacific.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Jiali
      – PersonEntity:
          Name:
            NameFull: Li, Qinglan
      – PersonEntity:
          Name:
            NameFull: Li, Chao
      – PersonEntity:
          Name:
            NameFull: Qu, Ankang
      – PersonEntity:
          Name:
            NameFull: Zhou, Junwei
      – PersonEntity:
          Name:
            NameFull: Lyu, Xinyan
      – PersonEntity:
          Name:
            NameFull: Qian, Qifeng
      – PersonEntity:
          Name:
            NameFull: Liu, Changjie
      – PersonEntity:
          Name:
            NameFull: Shufeng, Xi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 08828156
          Numbering:
            – Type: volume
              Value: 41
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Weather & Forecasting
              Type: main
ResultId 1