The Impact of Taiwan Topography on the Predictability of Typhoon Morakot's Record-breaking Rainfall: A High-resolution Ensemble Simulation

From August 6 to 10, 2009, Typhoon Morakot brought record-breaking rainfall over southern Taiwan, causing the greatest human casualty and property damage in 50 years. In this study, we examine the impact of Taiwan topography on the extreme rainfall of Typhoon Morakot and its predictability through a high-resolution (4-km) Advanced Research WRF (ARW) model ensemble simulation. Sensitive experiments with and without terrain over Taiwan show that Taiwan topography plays a key role in this heavy rainfall event, through the interaction between the typhoon circulation and the Central Mountain Range (CMR). The CMR is a mesoscale mountain range, with many peaks exceeding 3,000 m, and a dimension of about 200 km x 100 km. The simulated rainfall from most members of the ARW-based ensemble system with Taiwan topography closely resembles the observed torrential rainfall features, with most of the precipitation concentrated along the windward side of the mountain. The existence of the mesoscale mountain range in Taiwan greatly increases rainfall rate and the total amount of precipitation from this case. When the terrain of Taiwan is removed, the mesoscale features associated with the rainfall distribution completely disappear and the peak rainfall amount is reduced to less than 25% of the experiments with Taiwan topography. The mesoscale mountain range and its interaction with the circulation of Typhoon Morakot generates significant variability in terms of storm central pressure, storm tracks, the secondary lows, and the timing and location of typhoon landfall, which in turn creates significant variability in rainfall amount and distributions. The significant variability of precipitation due to typhoon-mountain interaction suggest that the forecasting of a heavy rainfall event such as Typhoon Morakot would benefit strongly from the use of a high-resolution mesoscale ensemble forecast system, which would provide valuable information on the probability of heavy precipitation.