ABSTRACT
An extreme rainfall event occurred on 8 - 11 December 2018 along the coast of central Vietnam. The maximum rainfall amount in 72 hours observed was over 900 mm, and the associated heavy losses both made it a record-breaking and significant event (hereafter, abbreviate is the D18 event). To improve the ability to forecast extreme rainfall for central Vietnam by time-lagged cloud-resolving ensemble quantitative precipitation forecasts approach, this study focused on the analysis of the D18 event and assessed its predictability in the high-resolution time-lagged ensemble prediction system using the Cloud Resolving Storm Simulator (CReSS) model. Some major findings of this research can be summarized as follows:
Several atmospheric disturbances played significant roles in this extreme rainfall: the northeasterly winds originating in northern China, propagated southward to reach the South China Sea (SCS). It interacted with the low-level easterlies wind and then blew into central Vietnamese and was prevented the Annamite Range. Furthermore, strong easterly winds over the central of the SCS appeared, in between the cold surge and the southeasterly wind anomaly. These three branches of flows led to strong low-level convergence. The strong easterly and strong southeasterly anomaly winds also played an important role in transporting moisture from the tropical ocean across the SCS toward central Vietnamese.
Evaluation of the predictability of the D18 event by the high-resolution time-tagged ensemble prediction system using the CReSS model indicated that CReSS well-predicted the D18 event at the lead-times of day 1 (0 h), day 2 (6-24 h), and day 3 (30 -48 h) for both 24-h accumulated rainfall and 72-h accumulated rainfall during the D18 event. However, the prediction skill is reduced at the extended lead time beyond 3 days. Besides, results show that it is challenging to achieve predictive skill in QPFs for rainfall thresholds greater than 100 mm with lead time longer than 3 days. Such a limitation exists owing to the rapid changes in atmospheric disturbances with time linked to the unique location of Vietnam in the tropics.
This is the first time a cloud-resolution model (CRM) is applied to forecast extreme rainfall in Vietnam, and the results are encouraging. Therefore, this result will provide the motivation to carry out further research on the predictability of the extreme rainfall in Vietnam by using the CReSS model.
Keywords: Heavy rainfall, Ensemble forecast, CReSS model

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