Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF)

Chun Chieh Wu, Guo Yuan Lien, Jan Huey Chen, Fuqing Zhang

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51 Scopus citations

Abstract

A new tropical cyclone vortex initialization method based on the ensemble Kalman filter (EnKF) is proposed in this study. Three observed parameters that are related to the tropical cyclone (TC) track and structure-center position, velocity of storm motion, and surface axisymmetric wind structure-are assimilated into the high-resolution Weather Research and Forecasting (WRF) model during a 24-h initialization period to develop a dynamicallybalanced TC vortex without employing any extra bogus schemes. The first two parameters are available from the TC track data of operational centers, which are mainly based on satellite analysis. The radial wind profile is constructed by fitting the combined information from both the best-track and the dropwindsonde data available from aircraft surveillance observations, such as the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). The initialized vortex structure is consistent with the observations of a typical vertical TC structure, even though only the surface wind profile is assimilated. In addition, the subsequent numerical integration shows minor adjustments during early periods, indicating that the analysis fields obtained from this method are dynamically balanced. Such a feature is important for TC numerical integrations. The results here suggest that this new method promises an improved TC initialization and could possibly contribute to some high-resolution numerical experiments to better understand the dynamics of TC structure and to improve operational TC model forecasts. Further applications of this method with sophisticated data from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) will be shown in a follow-up paper.

Original languageEnglish (US)
Pages (from-to)3806-3822
Number of pages17
JournalJournal of the Atmospheric Sciences
Volume67
Issue number12
DOIs
StatePublished - Dec 2010

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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