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A global evaluation of multi-model ensemble tropical cyclone track probability forecasts

Titley, H. A., Bowyer, R. L. and Cloke, H. L. (2020) A global evaluation of multi-model ensemble tropical cyclone track probability forecasts. Quarterly Journal of the Royal Meteorological Society, 146 (726). pp. 531-545. ISSN 1477-870X

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To link to this item DOI: 10.1002/qj.3712

Abstract/Summary

At the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G), the European Centre for Medium-Range Weather Forecasts Ensemble (ECMWF ENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post-processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108-member multi-model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120km of a named tropical cyclone at any point in the next 7-days, and also broken down in to each 24-hour forecast period. This paper presents the verification of these named-storm track probabilities over a two-year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi-model ensemble is found to increase the skill and value of the track probability forecasts over the best-performing individual ensemble (ECMWF ENS), for both overall 7-day track probability forecasts and 24-hour track probabilities. Basin-based and storm-based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi-model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high-profile case studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic-focused forecasting environment to one where the probabilistic situation-based uncertainty information provided by the dynamic multi-model ensembles can be incorporated into operational forecasts and warnings.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:87470
Publisher:Wiley

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