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)

探索了基于WRF模式的集合卡尔曼滤波同化方法(WRF-EnKF,简称EnKF)在近海有可能达到更强台风连续循环同化中国大陆高时空分辨率多普勒天气雷达径向风观测资料的效果,同时检验台风Vicente(2012)的三维结构演变及其动力学特征。通过短期集合预报得到跟随当前流场变化着的背景误差协方差的台风涡旋和动力学结构。研究发现,EnKF同化预报系统能有效地同化高时空分辨率雷达径向速度观测资料,显著改善初始场中台风Vicente的中小尺度内核结构,同时提高对台风Vicente的路径和强度及其相伴随的短期强降水预报。在台风最强时刻同化雷达径向风观测能快速(1~2 h)得到真实的暖核台风结构,同时进一步提高台风路径和强度的预报。另外,EnKF同化雷达径向风观测资料还能有效提高短期降水预报,1 h和3 h累积降水的分布、降水中心以及降水随时间演变都能得到显著改善,这与改善台风路径、结构和强度有密切关系。因此,对中国东南沿海有可能达到较强的台风进行同化雷达径向风观测资料可改善登陆台风的预报水平,这为利用我国地基多普勒天气雷达观测资料改善模式的初始场从而提高台风预报提供一定的指示作用。

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 Vicente （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.