Extreme Rapid Intensification of Typhoon Vicente (2012) in the South China Sea
Posted on Jan. 3, 2014
Owen Shieh, NDPTC's Weather and Climate Program Coordinator, along with colleagues at NOAA's Earth Research Laboratory, the Joint Typhoon Warning Center, and the University of Hawaii's Department of Meteorology have recently published an article entitled "Extreme Rapid Intensification of Typhoon Vicente (2012) in the South China Sea" in the American Meteorological Society's (AMS) journal Weather and Forecasting (Volume 28, December 2013).
One of the primary challenges for both tropical cyclone (TC) research and forecasting is the problem of
intensity change. Accurately forecasting TC rapid intensification (RI) is particularly important to interests
along coastlines and shipping routes, which are vulnerable to storm surge and heavy seas induced by intense
tropical cyclones. One particular RI event in the western North Pacific Ocean with important scientific implications
is the explosive deepening of Typhoon Vicente (2012). Vicente underwent extreme RI in the
northern South China Sea just prior to landfall west of Hong Kong, China, with maximum sustained winds
increasing from 50 kt (1 kt = 0.51ms-1) at 0000 UTC 23 July to 115 kt at 1500 UTC 23 July. This increase of
65 kt in 15 h far exceeds established thresholds for TC RI. Just prior to this RI episode, Vicente exhibited
a near-908 poleward track shift. The relationship between the track and intensity change is described, and the
authors speculate that the passage of an upper-tropospheric (UT) ‘‘inverted’’ trough was a significant influence.
An analysis of real-time numerical model guidance is provided and is discussed from an operational
perspective, and high-resolution global model analyses are evaluated. Numerical model forecasts of the UT
trough interaction with the TC circulation were determined to be a shortcoming that contributed to the
intensity prediction errors for Vicente. This case study discusses the importance of considering UT features in
TC intensity forecasting and establishes current modeling capabilities for future research.
The full article can be downloaded here.