Comparison of different global ensemble prediction systems for tropical cyclone intensity forecasting
Many meteorological centers have operationally implemented global model‐based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track foreca...
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Veröffentlicht in: | Atmospheric Science Letters 2024-04, Vol.25 (4), p.n/a |
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Zusammenfassung: | Many meteorological centers have operationally implemented global model‐based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track forecasting. However, recent GEPS upgrades mean that TC intensity predictions from GEPSs are now also becoming of interest. This study focuses on the verification and comparison of the latest generation of GEPSs for TC intensity forecasts, particularly during the rapid intensification (RI) period over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. On average, the National Centers for Environmental Prediction (NCEP) GEPS performed best in predicting both TC intensity and RI across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI.
This article investigates the latest generation of operational Global Ensemble Prediction Systems (GEPSs) for TC intensity and Rapid Intensification (RI) forecasts over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. The result shows that the National Centers for Environmental Prediction (NCEP) GEPS performed best of all the GEPSs for TC intensity and RI predictions across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI. |
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ISSN: | 1530-261X 1530-261X |
DOI: | 10.1002/asl.1207 |