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A global evaluation of multi-model ensemble tropical cyclone track probability forecasts
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- Titley, Helen A. (author)
- Met Off, Weather Sci, Exeter, Devon, England;Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England
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- Bowyer, Rebecca L. (author)
- Met Off, Weather Sci, Exeter, Devon, England
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- Cloke, Hannah L. (author)
- Uppsala universitet,Luft-, vatten- och landskapslära,Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England;Univ Reading, Dept Meteorol, Reading, Berks, England;CNDS, Ctr Nat Hazards & Disaster Sci, Uppsala, Sweden
- (creator_code:org_t)
- 2020-01
- 2020
- English.
- In: Quarterly Journal of the Royal Meteorological Society. - : Wiley. - 0035-9009 .- 1477-870X. ; 146:726, s. 531-545
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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 (ECMWFENS) 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 120 km of a named tropical cyclone at any point in the next 7 days, and also broken down into each 24-hour forecast period. This study 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)
Keyword
- ensembles
- probabilistic forecasting
- tropical cyclones
- uncertainty
- verification
Publication and Content Type
- ref (subject category)
- art (subject category)
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