Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system

Shu Chih Yang, Shu Hua Chen, Keichii Kondo, Takemasa Miyoshi, Yu Chieng Liou, Yung Lin Teng, Hui Ling Chang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

High-resolution numerical simulations are regularly used for severe weather forecasts. To improve model initial conditions, a single short localization is commonly applied in the ensemble Kalman filter when assimilating observations. This approach prevents large-scale corrections from appearing in a high-resolution analysis. To improve heavy rainfall forecasts associated with a multiscale weather system, analyses must be accurate across a range of spatial scales, a task that is difficult to accomplish using a single localization. This study is the first to apply a dual-localization (DL) method to improve high-resolution analyses used to forecast a real-case heavy rainfall event associated with a Meiyu front on 16 June 2008 in Taiwan. A Meiyu front is a multiscale weather system characterized by storm-scale convection, a mesoscale front, and large-scale southwesterly monsoonal flow. The use of the DL method to produce the analyses was able to correct both the synoptic-scale moisture flux transported by southwesterly monsoonal flow and the mesoscale low-level convergence offshore of southwestern Taiwan. As a result, the forecasted amount, pattern, and temporal evolution of the heavy rainfall event were improved.

Original languageEnglish
Pages (from-to)1684-1702
Number of pages19
JournalJournal of Advances in Modeling Earth Systems
Volume9
Issue number3
DOIs
StatePublished - Jul 2017

Keywords

  • EnKF
  • dual-localization data assimilation
  • heavy rainfall prediction

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