A Decade after Typhoon Morakot (2009): What Have We Learned about Its Physics and Predictability?

Abstract

Typhoon Morakot struck Taiwan during 7-9 August 2009 and became the deadliest tropical cyclone (TC) in five decades by producing up to 2635 mm of rain in 48 h, breaking the world record. The extreme rainfall of Morakot resulted from the strong interaction among several favorable factors that occurred simultaneously. These factors from large scale to small scale include: (1) weak environmental steering flow linked to the evolution of the monsoon gyre and consequently slow TC motion; (2) a strong moisture surge due to low-level southwesterly flow; (3) asymmetric rainfall and latent heating near southern Taiwan to further reduce the TC’s forward motion as its center began moving away from Taiwan; (4) enhanced rainfall due to steep topography; (5) atypical structure with a weak inner core, enhancing its susceptibility to the latent heating effect; and (6) cell merger and back building inside the rainbands associated with the interaction between the low-level jet and convective updrafts. From a forecasting standpoint, the present-day convective-permitting or cloud-resolving regional models are capable of short-range predictions of the Morakot event starting from 6 August. At longer ranges beyond 3 days, larger uncertainty exists in the track forecast and an ensemble approach is necessary. Due to the large computational demand at the required high resolution, the time-lagged strategy is shown to be a feasible option to produce useful information on rainfall probabilities of the event.

Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

“…However, if much of the rainfall in Taiwan is brought by the TC circulation itself, as in the case of TY Nepartak (2016), then small track errors tend to be a prerequisite for good QPFs. Since it is quite common for typhoons to be associated with strong southwesterly flows over the WNP in the summer, and some of the most extreme rainfall events in Taiwan are produced precisely by such a type of typhoons, e.g., [4,5,23,43,63,64], a better understanding of their behaviors, as discussed in this work, can be helpful to make better use of model products and improve QPFs.…”

“…Located along one of the main paths of tropical cyclones (TCs) or typhoons in the western North Pacific (WNP), Taiwan is frequently hit by these storms roughly 3-5 per season [1]. Due to the steep mountains of the island, with the highest peak reaching 3952 m, rainfall from TCs as well as other organized convective systems are often considerably enhanced, e.g., [2][3][4][5], and consequently cause hazards such as inundation, floods, and landslides, and damage properties and infrastructure, e.g., [6]. To cope with the heavy rainfall brought by typhoons and reduce their impacts, quantitative precipitation forecasts (QPFs) using numerical models, e.g., [7,8], are in high demand in Taiwan.…”

“…Different from these earlier attempts that applied the lagged method As computer technology continues to advance, it has also been established in Taiwan during the past decade or so that more accurate typhoon heavy-rainfall QPFs can be obtained using higher model resolutions, e.g., [13,[22][23][24], among other methods. Many studies have reported better and more realistic TC QPF results using a horizontal grid spacing (∆x) of 1-4 km and an adequate size for the fine domain, both for Taiwan [5,22,[25][26][27][28] and in other regions [29][30][31][32]. For example, long-term results through multiple seasons in 2010-2015 using the Cloud-Resolving Storm Simulator (CReSS; Refs.…”

“…For some TCs where the worse-case scenario turned out to occur, such as typhoons (TYs) Saola (2012), Matmo (2014), and Soudelor (2015), the particular scenario was successfully predicted almost one week before landfall, providing long lead times for preparation [41,42]. However, decent QPFs were only obtainable within a short range in some other cases [5,43] because the predictability varies among different TC cases.…”