Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm.
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| Title: | Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm. |
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| Authors: | Lin, Xiajing1 (AUTHOR), Huang, Guohe1 (AUTHOR) huangg@uregina.ca, Song, Tangnyu1,2 (AUTHOR) |
| Source: | Journal of Climate. Aug2025, Vol. 38 Issue 15, p3661-3675. 15p. |
| Subjects: | Tropical cyclones, Cyclone tracking, European Centre for Medium-Range Weather Forecasts (Organization), Dynamic programming, Bayesian analysis, Optimization algorithms, Risk assessment of climate change |
| Abstract: | In this study, the Bayesian Inference and Dynamic Programming Tracking (BIDTrack) algorithm is developed for enhanced global tropical cyclone (TC) tracking in reanalysis datasets, particularly the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack addresses challenges like trajectory discontinuities and parameter sensitivity in traditional methods by combining Bayesian inference with dynamic programming. The algorithm is optimized through a Bayesian interval optimization (BIO) process, which refines the parameters to retain cyclone candidates that are statistically significant and physically meaningful. Results indicate a strong spatial correlation between BIDTrack-derived trajectories and International Best Track Archive for Climate Stewardship (IBTrACS) observations, especially in cyclone-prone regions like the North Atlantic and western Pacific. BIDTrack captures both major hurricanes and weak storms, providing a reliable tool for cyclone path reconstruction and climate impact assessments. This research demonstrates BIDTrack's potential in improving TC tracking and enhancing the understanding of cyclone dynamics in ERA5. Significance Statement: Tropical cyclones, such as hurricanes, are powerful storms that pose significant risks to coastal communities. Tracking their paths accurately is crucial for understanding their behavior and mitigating their impacts. In this study, an emerging method, Bayesian Inference and Dynamic Programming Tracking (BIDTrack), is introduced by combining Bayesian inference with dynamic programming to enhance cyclone tracking in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack generates cyclone paths with probability estimates, providing a more precise assessment of whether a given track point corresponds to the actual cyclone. This algorithm is effective in tracking both strong hurricanes and weaker storms, making it a valuable tool for researchers and decision-makers interested in cyclone behavior and climate impacts. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Climate 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186914344 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Xiajing%22">Lin, Xiajing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Guohe%22">Huang, Guohe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huangg@uregina.ca</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Tangnyu%22">Song, Tangnyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Climate%22">Journal of Climate</searchLink>. Aug2025, Vol. 38 Issue 15, p3661-3675. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Tropical+cyclones%22">Tropical cyclones</searchLink><br /><searchLink fieldCode="DE" term="%22Cyclone+tracking%22">Cyclone tracking</searchLink><br /><searchLink fieldCode="DE" term="%22European+Centre+for+Medium-Range+Weather+Forecasts+%28Organization%29%22">European Centre for Medium-Range Weather Forecasts (Organization)</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment+of+climate+change%22">Risk assessment of climate change</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this study, the Bayesian Inference and Dynamic Programming Tracking (BIDTrack) algorithm is developed for enhanced global tropical cyclone (TC) tracking in reanalysis datasets, particularly the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack addresses challenges like trajectory discontinuities and parameter sensitivity in traditional methods by combining Bayesian inference with dynamic programming. The algorithm is optimized through a Bayesian interval optimization (BIO) process, which refines the parameters to retain cyclone candidates that are statistically significant and physically meaningful. Results indicate a strong spatial correlation between BIDTrack-derived trajectories and International Best Track Archive for Climate Stewardship (IBTrACS) observations, especially in cyclone-prone regions like the North Atlantic and western Pacific. BIDTrack captures both major hurricanes and weak storms, providing a reliable tool for cyclone path reconstruction and climate impact assessments. This research demonstrates BIDTrack's potential in improving TC tracking and enhancing the understanding of cyclone dynamics in ERA5. Significance Statement: Tropical cyclones, such as hurricanes, are powerful storms that pose significant risks to coastal communities. Tracking their paths accurately is crucial for understanding their behavior and mitigating their impacts. In this study, an emerging method, Bayesian Inference and Dynamic Programming Tracking (BIDTrack), is introduced by combining Bayesian inference with dynamic programming to enhance cyclone tracking in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5). BIDTrack generates cyclone paths with probability estimates, providing a more precise assessment of whether a given track point corresponds to the actual cyclone. This algorithm is effective in tracking both strong hurricanes and weaker storms, making it a valuable tool for researchers and decision-makers interested in cyclone behavior and climate impacts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Climate 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=186914344 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/JCLI-D-24-0484.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 3661 Subjects: – SubjectFull: Tropical cyclones Type: general – SubjectFull: Cyclone tracking Type: general – SubjectFull: European Centre for Medium-Range Weather Forecasts (Organization) Type: general – SubjectFull: Dynamic programming Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Risk assessment of climate change Type: general Titles: – TitleFull: Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Xiajing – PersonEntity: Name: NameFull: Huang, Guohe – PersonEntity: Name: NameFull: Song, Tangnyu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08948755 Numbering: – Type: volume Value: 38 – Type: issue Value: 15 Titles: – TitleFull: Journal of Climate Type: main |
| ResultId | 1 |
