Ensemble forecasting of tropical cyclone motion: Comparison between regional bred modes and random perturbations

Kevin K W Cheung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Random perturbations (RPs) and a modified version for breeding of growing modes are used with a regional baroclinic mesoscale model to perform ensemble forecasting of tropical cyclone motion. Based on a sample of six cases, similar conclusions are found as in previous barotropic modeling studies. Even after introducing a larger spatial correlation into the RPs using a multi-quadric analysis scheme, the skill of this ensemble mean track prediction is almost always lower than that of the control forecast in the cases considered. The track prediction performance of the ensemble using regional bred modes (RBMs) as perturbations has a higher average skill. At nearly all forecast intervals except less than 24h when the initial position error still dominates, the ensemble mean tracks in all six cases are improved over the control forecast. In the 6h-24h range, the success rate (ratio of the cases with a forecast improvement to the total number of cases) has a value of 10/24. In the 30h-48h range, the success rate increases to 20/24, but drops to 18/24 in the 54h-72h range. A relative skill score (RSS) is used to compare the skills of the two perturbation methodologies. It is found that the average RSSs of using RBMs are significantly higher than the corresponding ones of RPs at the 99% confidence level in all three 24-h periods. Note that the above conclusion is only based on ensemble mean forecasts. All of the possibilities from an ensemble-based probabilistic track distribution are not explored in this paper. The ensemble spreads in these RBM ensembles are large enough to include the verifying tracks in all the cases considered. It is also found that the ensemble spread is well correlated with the average error in an ensemble when using RBMs, but not with the ensemble mean forecast error in both methodologies.

Original languageEnglish
Pages (from-to)23-34
Number of pages12
JournalMeteorology and Atmospheric Physics
Volume78
Issue number1-2
DOIs
Publication statusPublished - 2001
Externally publishedYes

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