Prediction and predictability of high-impact Western Pacific landfalling Tropical Cyclone Vicente (2012) through convection-permitting ensemble assimilation of doppler radar velocity

Lei Zhu, Qilin Wan, Xinyong Shen, Zhiyong Meng, Fuqing Zhang, Yonghui Weng, Jason Sippel, Yudong Gao, Yunji Zhang, Jian Yue

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44ms-1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente's intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow.

Original languageEnglish (US)
Pages (from-to)21-43
Number of pages23
JournalMonthly Weather Review
Volume144
Issue number1
DOIs
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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