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Cited by 265Meteorological Phenomena and SimulationsClimate variability and modelsTropical and Extratropical Cyclones Research

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The TIGGE Project and Its Achievements

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Swinbank, Richard, Kyouda, Masayuki, Buchanan, Piers, Froude, Lizzie, Hamill, Thomas M., Hewson, Tim D., Keller, Julia H., Matsueda, Mio, Methven, John, Pappenberger, Florian, Scheuerer, Michael, Titley, Helen A., Wilson, Laurence, Yamaguchi, Munehiko·2016·Bulletin of the American Meteorological Society
DOI 10.1175/bams-d-13-00191.1
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Abstract

The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.

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  • The TIGGE archive provides cyclone tracks estimated with the operational tracker, from HRES's forecasts at 0.1° resolution.

    “For the HRES baseline, we used the TIGGE archive, which provides cyclone tracks estimated with the operational tracker, from HRES's forecasts at 0.1° resolution [8, 46].”

    From Learning skillful medium-range global weather forecasting (2023) · cites this paper as [46]

    The TIGGE Project and Its Achievements describes TIGGE as an archive of ensemble forecasts, and its Dynamics and Predictability section reports cyclone tracks derived from TIGGE data using a tracking method employed operationally by ECMWF. It does not establish that the archive provides tracks from HRES forecasts at 0.1° resolution; the tracking passage concerns global ensembles, not HRES. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Generally, verifying a model against its own analysis gives the best skill estimates.

    “Generally, verifying a model against its own analysis gives the best skill estimates [45].”

    From Learning skillful medium-range global weather forecasting (2023) · cites this paper as [45]

    The Verification section reports an example in low-level tropical regions where the model verified against its own analysis appeared most accurate. It does not establish this as a general rule; instead, it warns that skill rankings depend strongly on the analysis used and recommends verifying against more than one analysis. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Metadata

Authors
Swinbank, R., Kyouda, M., Buchanan, P., Froude, L., Hamill, T. M., Hewson, T. D., Keller, J. H., Matsueda, M., Methven, J., Pappenberger, F., Scheuerer, M., Titley, H. A., Wilson, L., & Yamaguchi, M.
Affiliations
  • Met Office (GB)
  • Japan Meteorological Agency (JP)
  • University of Reading (GB)
  • National Oceanic and Atmospheric Administration (US)
  • European Centre for Medium-Range Weather Forecasts (GB)
  • Deutscher Wetterdienst (DE)
  • University of Tsukuba (JP)
  • Environment and Climate Change Canada (CA)
  • Meteorological Research Institute (JP)
ORCID
Venue
Bulletin of the American Meteorological Society
Publisher
American Meteorological Society
Published
2016-01-01
Volume
97
Issue
1
Pages
49-67
ISSN
  • 0003-0007 (ISSN-L)
  • 1520-0477
DOI
10.1175/bams-d-13-00191.1
OpenAlex
W2068146025
Cited by
265
References
0
Citations by year
2026: 15 · 2025: 21 · 2024: 30 · 2023: 30 · 2022: 30 · 2021: 36 · 2020: 26 · 2019: 19 · 2018: 26 · 2017: 16 · 2016: 12
FWCI
12.34
Type
journal article
Language
en
Open access
Locations
  • Bulletin of the American Meteorological Society (publishedVersion)
  • CentAUR (University of Reading) (submittedVersion)
  • Institutional Repositories DataBase (IRDB) (submittedVersion)
  • NOAA Institutional Repository (submittedVersion)
  • Terrestrial Environment Research Center (University of Tsukuba) (submittedVersion, cc-by-nc-nd)
Funders
  • European Commission (282915)
  • Natural Environment Research Council (NE/I005196/1)
  • Japan Society for the Promotion of Science (26887007, 26282111)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Tropical and Extratropical Cyclones Research
Keywords
TIGGE, THORPEX, ensemble forecasting, forecast evaluation, bias correction, reforecasts, tropical cyclones, extratropical transition, extratropical cyclones, storm tracks, tropical cyclone track forecasting, heavy rainfall forecasting, flood forecasting, predictability, ensemble data assimilation

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