VARIATIONAL DATA ASSIMILATION USING WAVELET BACKGROUND ERROR COVARIANCE: INITIALIZATION OF TYPHOON KAEMI (2006)   总被引：4，自引：1，他引：3  

张卫民  曹小群  肖庆农  宋君强  朱小谦  王舒畅 《热带气象学报(英文版)》2010,16(4):333-340

Background error covariance plays an important role in any variational data assimilation system, because it determines how information from observations is spread in model space and between different model variables. In this paper, the use of orthogonal wavelets in representation of background error covariance over a limited area is studied. Based on the WRF model and its 3D-VAR system, an algorithm using orthogonal wavelets to model background error covariance is developed. Because each wavelet function contains information on both position and scale, using a diagonal correlation matrix in wavelet space gives the possibility to represent some anisotropic and inhomogeneous characteristics of background error covariance. The experiments show that local correlation functions are better modeled than spectral methods. The formulation of wavelet background error covariance is tested with the typhoon Kaemi (2006). The results of experiments indicate that the subsequent forecasts of typhoon Kaemi’s track and intensity are significantly improved by the new method.  相似文献   

NUMERICAL EXPERIMENTS AND ANALYSIS OF DIGITAL FILTER INITIALIZATION FOR WRF MODEL     

王舒畅  黄思训  张卫民  朱小谦  曹小群  李毅 《热带气象学报(英文版)》2008,14(1):1-10

Initialization and initial imbalance problem were discussed in the context of a three-dimensional variational data assimilation system of the new generation “Weather Research and Forecasting Model”. Several options of digital filter initialization have been tested with a rain storm case. It is shown that digital filter initialization, especially diabatic digital filter initialization and twice digital filter initialization, have effectively removed spurious high frequency noise from initial data for numerical weather prediction and produced balanced initial conditions. For six consecutive intermittent data assimilation cycles covering a 3-day period, mean initialization increments and impact on forecast variables are studied. DFI has been demonstrated to provide better adjustment of the hydrometeors and vertical velocity, reduced spin-up time, and improved forecast variables quantity.  相似文献