High-resolution quantitative precipitation forecasts and simulations by the Cloud-Resolving Storm Simulator (CReSS) for Typhoon Morakot (2009)

Abstract

Summary Typhoon Morakot (2009) struck Taiwan during 7–9 August 2009, and brought extreme rainfall up to 2855 mm and the worst damages in the past 50 years. The operational models showed deficiency and serious under-prediction in rainfall amount at real time. This study demonstrates that the Cloud-Resolving Storm Simulator (CReSS), a state-of-the-art, high-resolution model, at a grid size of 3 km and starting as early as 0000 UTC 4 August, can successfully simulate and reproduce the event with high accuracy, including the distribution and timing of heavy rainfall in Taiwan. In the simulation starting at 0000 UTC 6 August, for example, the threat scores for 24-h rainfall for 8 August (with extreme amounts >1450 mm) reach 0.8–0.4 even at thresholds of 100–500 mm. This result is only possible due to small track error and the phase-locking mechanism of the Taiwan topography to heavy rainfall. Furthermore, real-time forecast and hindcast integrations of the CReSS model show that high-quality quantitative precipitation forecasts (QPFs) with peak total amount 67–80% of the true value are also obtained from initial conditions at 0000 UTC 6 August, which is about 2 days prior to the beginning of the heaviest rainfall in southern Taiwan. In these integrations, typhoon track errors in the global model forecasts used as boundary conditions are the major error source that prevent more ideal QPF results before and at 1200 UTC 5 August. When properly configured, it is believed that other similar cloud-resolving models can achieve comparable performance. Thus, the importance of and potential benefits from deterministic high-resolution forecasts is stressed, which may give an extended lead-time when the track error is small. With potentially longer time window for emergency action just prior to extreme rainfall events when it matters the most, such forecasts may ultimately lead to reduced losses in lives and properties.