Traditional variational data assimilation(VDA) with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of "four dimensional variational data assimilation(4D-Var) with multiple regularization parameters as a weak constraint(Tikh-4D-Var)" is proposed by imposing different regularization parameters for different observations. Meanwhile, a new multiple regularization parameters selection method, which is suitable for actual high-dimensional data assimilation system, is proposed based on the posterior information of 4D-Var system. Compared with the traditional single regularization parameter selection method, computation of the proposed multiple regularization parameters selection method is smaller. Based on WRF3.3.1 4D-Var data assimilation system, initialization and simulation of typhoon Chaba(2010) with the new Tikh-4D-Var method are compared with its counterpart 4D-Var to demonstrate the effectiveness of the new method. Results show that the new Tikh-4D-Var method can accelerate the convergence with less iterations. Moreover, compared with 4D-Var method, the typhoon track, intensity(including center surface pressure and maximum wind speed) and structure prediction are obviously improved with Tikh-4D-Var method for 72-h prediction. In addition, the accuracy of the observation error variances can be reflected by the multiple regularization parameters.
卫星微波垂直探测器的辐射观测资料在数值预报中的同化应用使得数值预报水平有了巨大的飞跃。微波资料的质量控制是保证观测资料成功同化的关键所在。文章提出一种基于AMSU-A(Advanced Microwave Sounding Unit-A)辐射亮温资料梯度信息的新质量控制方法,将亮温梯度距平值明显较大的资料作为被降水污染或因为其他原因出现的"坏"的资料。利用中尺度非静力WRF(Weather Research and Forecasting)模式和区域三维变分同化,针对"海鸥"(2008)和"圆规"(2010)2个个例,对比旧质量控制中的降水检测和阈值检测方法,评估该方法用于AMSU-A资料同化时对台风数值模拟的情况。研究表明,旧质量控制方法将会使一些"坏"的微波观测资料同化进入模式,降低模式分析场的质量,进而导致同化结果有较大误差。相对于旧方法获得的分析场,利用基于亮温梯度信息的质量控制方法可使更多"坏"的观测剔除,同化后模式初始时刻的位势高度场和风场更接近于真实情况。与传统AMSU-A辐射资料的同化相比,新质量控制方案使2个台风路径数值模拟的偏差有明显的减小:"海鸥"个例中,模拟台风路径误差的最大改善比为12,路径误差改善约540km;"圆规"个例的最大改善比为13,模拟路径误差减小118km。
