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

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Titley, Helen A.Met Off, Weather Sci, Exeter, Devon, England;Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England (författare)

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

Artikel/kapitelEngelska2020

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2020-01
Wiley,2020
electronicrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:uu-410128
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-410128URI
https://doi.org/10.1002/qj.3712DOI

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Språk:engelska
Sammanfattning på:engelska

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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.

Ämnesord och genrebeteckningar

NATURVETENSKAP Geovetenskap och miljövetenskap Meteorologi och atmosfärforskning hsv//swe
NATURAL SCIENCES Earth and Related Environmental Sciences Meteorology and Atmospheric Sciences hsv//eng
ensembles
probabilistic forecasting
tropical cyclones
uncertainty
verification

Biuppslag (personer, institutioner, konferenser, titlar ...)

Bowyer, Rebecca L.Met Off, Weather Sci, Exeter, Devon, England (författare)
Cloke, Hannah L.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(Swepub:uu)hancl168 (författare)
Met Off, Weather Sci, Exeter, Devon, England;Univ Reading, Dept Geog & Environm Sci, Reading, Berks, EnglandMet Off, Weather Sci, Exeter, Devon, England (creator_code:org_t)

Sammanhörande titlar

Ingår i:Quarterly Journal of the Royal Meteorological Society: Wiley146:726, s. 531-5450035-90091477-870X

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Av författaren/redakt...: Titley, Helen A.; Bowyer, Rebecca ...; Cloke, Hannah L.

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NATURVETENSKAP: NATURVETENSKAP; och Geovetenskap och ...; och Meteorologi och ...

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Av lärosätet: Uppsala universitet

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