Journal Article The TIGGE Project and Its Achievements

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

97 ( 1 )  , pp.49 - 67 , 2016-01 , American Meteorological Society
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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