The DOI matches its Crossref record.
The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data
Abstract
The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.
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In the citing paper
“Recently MLWP has helped improve on NWP-based forecasting in regimes where traditional NWP is relatively weak, for example sub-seasonal heat wave prediction [16] and precipitation nowcasting from radar images [32, 33, 29, 8]”
From Learning skillful medium-range global weather forecasting (2023) · cites this paper as [29]
In the citing paper
“For our analysis of tropical cyclone forecasting, we used the IBTrACS [28, 29, 31, 30] archive to construct the ground truth dataset. This includes historical cyclone tracks from around a dozen authoritative sources.”
From Learning skillful medium-range global weather forecasting (2023) · cites this paper as [29]
In the citing paper
“In their 2017 work, the authors of ref. 66 optimized the hyperparameters of TempestExtremes so that when applied to 6-hourly reanalysis datasets the resulting tracks closely match the observed tracks from the IBTrACS dataset63,64.”
From Probabilistic weather forecasting with machine learning (2025) · cites this paper as [63]
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Metadata
- Authors
- Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J.
- Corresponding author
- Knapp, Kenneth R.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2010-03
- Volume
- 91
- Issue
- 3
- Pages
- 363-376
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W1645858368
- Cited by
- 3,237
- References
- 0
- Citations by year
- 2026: 507 · 2025: 461 · 2024: 401 · 2023: 322 · 2022: 302 · 2021: 256 · 2020: 198 · 2019: 142 · 2018: 124 · 2017: 106 · 2016: 99 · 2015: 68 · 2014: 81 · 2013: 57 · 2012: 64
- FWCI
- 21.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bulletin of the American Meteorological Society (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Tropical and Extratropical Cyclones Research
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- IBTrACS, tropical cyclones, best track data, tropical cyclone climatology, tropical cyclone intensity, historical storm records, data integration
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