热带气象学报2017，Vol.33Issue(3)：345-356,12.DOI:10.16032/j.issn.1004-4965.2017.03.006
雷达径向风资料EnKF同化应用对Vicente(2012)登陆台风强度变化过程预报的影响试验研究
IMPACTS OF THE ENSEMBLE ASSIMILATION OF RADAR RADIAL VELOCITY ON THE INTENSITY EVOLUTION OF LANDFALLING TYPHOON VICENTE (2012)
摘要
Abstract
The current study explores the use of a WRF-based ensemble Kalman filter (EnKF) to continuously assimilate the high-resolution Doppler radar data near the peak stage in order to capture the detailed time evolution and the 3-D structure and dynamics of the recent Typhoon Viccntc (2012) that made landfall during 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province of China.With vortex and dynamics dependent background error covariance estimated by the short-term ensemble forecasts,it is found that the WRF-EnKF can efficiently assimilate the high resolution radar radial velocity to improve the depiction of the typhoon inner-core structure of Vicente which further improves the forecasts of the track,intensity and precipitation associated with this landfalling typhoon.We further use the WRF-EnKF analysis and forecasts along with the ensembles initialized from the EnKF perturbations at different time to examine the dynamics of Vicente with respect to the number of volumes of radar observations being assimilated,different lead time before and during the landfall.We are particularly interested in the heavy rainfall associated with the landfalling Vicente which affects a large area of South China.The results show that assimilating the Doppler Radar data is a very promising way to improve the TC forecasts,which also demonstrate that doing data assimilation near the peak stage of TC also has the ability to improve the forecast.关键词
热带气旋/雷达径向风/集合卡尔曼滤波Key words
TC/radar radial velocily/EnKF分类
天文与地球科学引用本文复制引用
朱磊,万齐林,刘靓珂,沈新勇,高郁东..雷达径向风资料EnKF同化应用对Vicente(2012)登陆台风强度变化过程预报的影响试验研究[J].热带气象学报,2017,33(3):345-356,12.基金项目
科技部国家大气污染专项项目(2016YFC0203301) （2016YFC0203301）
国家重点基础研究发展计划973项目(2015CB453201) （2015CB453201）
国家自然科学基金项目(41375058、41475102、41530427) （41375058、41475102、41530427）
江苏省自然科学基金重点项目(BK20150062)共同资助 （BK20150062）
