Comparison of different global ensemble prediction systems for tropical cyclone intensity forecasting

Abstract

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.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

“…Furthermore, there are still several objective problems, such as the intricate interactions of dynamic and thermodynamic processes of the TCs at multiple spatio-temporal scales, the shortcomings of the framework and physical processes of current forecasting models, the incompleteness of observation systems specific to the TCs, and issues with assimilation algorithms and systems for high-resolution models. Therefore, large errors remain in single deterministic forecasts for the location and intensity of TC precipitation, even in the most advanced assimilation/forecast systems [6][7][8]. Moreover, deterministic forecasts are unable to quantitatively estimate the uncertainty of forecast results.…”