Open AccessOn the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities
Author(s) -
Sharanya J. Majumdar,
Peter M. Finocchio
Publication year - 2010
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/2009waf2222327.1
Subject(s) - predictability , ensemble forecasting , ensemble average , tropical cyclone , range (aeronautics) , meteorology , probabilistic logic , ensemble learning , independence (probability theory) , environmental science , climatology , track (disk drive) , computer science , statistics , mathematics , geography , artificial intelligence , geology , materials science , composite material , operating system
The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated. Ensemble forecasts of up to 5 days issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (UKMET) were evaluated for the 2008 Atlantic and western North Pacific seasons. In the Atlantic, the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models. Dynamic “probability circles” that contained 67% of the ECMWF ensemble captured the best track in ∼67% of all cases for 24–84-h forecasts, and were slightly underdispersive beyond 96 h. In contrast, the Goerss predicted consensus error (GPCE) was overdispersive. The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts. The ECMWF ensemble performed similarly when the size was reduced from 50 to 20. On average, it produced a lower measure of independence between its members than an ensemble comprising different deterministic models. The 67% circles normally captured the best track during straight-line motion, but less so for sharply turning tracks. In contrast to the Atlantic, the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific, in part because of a less skillful ensemble mean (and consensus). Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts, and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities.