Application of Time-Lagged Ensemble Quantitative Precipitation Forecasts for Typhoon Morakot (2009) in Taiwan by a Cloud-Resolving Model

Typhoon Morakot (2009) struck Taiwan during 7-9 August and brought extreme rainfall to the southern mountainous regions of the island, with a peak of 2635 mm in 48 h (and most heavy rainfall observed on 8 August), subsequently causing widespread damage. In this study, the time-lagged ensemble method is applied to this unique case to examine its effectiveness and usefulness for the prediction of heavy rainfall events. A series of hindcasts experiments was carried out using a cloud-resolving model with a grid size of 2.5 km at 6-h intervals from 0000 UTC 1 August to 0000 UTC 9 August 2009, with real-time global model products as initial and boundary conditions. It was found that most of the hindcast members initialized at and after 0600 UTC 6 August in the short range (i.e., within 72 h) could capture the magnitude of the rainfall to a reasonable extent, with a peak 48-h amount near or over 2500 mm, under the condition that the track errors were adequately small. With longer lead times, however, the predictability was limited due to larger track errors, and reliable rainfall predictions were impossible for runs with an initial time before or on 5 August. Thus, the probabilities derived from lagged members for extreme rainfall increased dramatically starting from 6 August, when a >80-90% likelihood was indicated that the southern mountainous regions of Taiwan would receive a 48-h rainfall in excess of 1000 mm, and >= 80% in parts of the area to receive over 1500 mm. Therefore, the time evolution of the probability may be useful in decision making and hazard mitigation. The limitations of such a time-lagged system and the potential to make further improvements are also discussed.

Automated summary

This study applies the time-lagged ensemble method to predict the heavy rainfall brought by Typhoon Morakot and finds that the predictions within 48 hours are relatively accurate, but the predictability decreases with longer lead times.