DOI: 10.1002/2017MS001009
Scopus记录号: 2-s2.0-85023182489
论文题名: Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system
作者: Yang S ; -C ; , Chen S ; -H ; , Kondo K ; , Miyoshi T ; , Liou Y ; -C ; , Teng Y ; -L ; , Chang H ; -L
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 3 起始页码: 1684
结束页码: 1702
语种: 英语
英文关键词: Forecasting
; Rain
; Data assimilation
; EnKF
; Ensemble Kalman Filter
; Heavy rainfall
; Heavy rainfall forecast
; High resolution analysis
; High-resolution numerical simulation
; Model initial conditions
; Weather forecasting
; convergence
; data assimilation
; Kalman filter
; moisture flux
; precipitation assessment
; prediction
; rainfall
; severe weather
; synoptic meteorology
; temporal evolution
; weather forecasting
; Taiwan
英文摘要: 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. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75755
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Atmospheric Sciences, National Central UniversityTaoyuan, Taiwan; RIKEN Advanced Institute for Computational Science, Kobe, Japan; Department of Land, Air and Water Resources, University of California, Davis, CA, United States; Taiwan Typhoon and Flood Research Institute, Taipei, Taiwan; Research and Development Center, Central Weather Bureau, Taipei, Taiwan
Recommended Citation:
Yang S,-C,, Chen S,et al. Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(3)