Ensemble forecasting of tropical cyclone motion: comparisonbetween regional bred modes and random perturbations |
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| Authors: | K K W Cheung |
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| Institution: | (1) Department of Meteorology, Naval Postgraduate School, Monterey, CA, CA |
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| Abstract: | Summary
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 24 h when the initial position error still dominates,
the ensemble mean tracks in all six cases are improved over the control forecast. In the 6 h–24 h 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 30 h–48 h range,
the success rate increases to 20/24, but drops to 18/24 in the 54 h–72 h 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.
Received February 7, 2001/Revised April 18, 2001 |
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