模式误差弱约束四维变分同化研究

经典四维变分同化(four dimensional variational data assimilation,简称4dvar)中认为模式是完美的, 而实际数值模式中存在的模式误差将对变分同化效果产生直接影响, 弱约束4dvar的提出为4dvar中考虑模式误差的影响提供了一种可行途径. 研究了基于模式误差控制变量的弱约束4dvar基本理论, 并在一维浅水方程组中对其同化效果及估计模式误差的有效性进行相关数值实验研究. 结果表明,在模式误差不可忽略的情况下,模式误差弱约束4dvar在整个区间 (包括同化区间和预报区间)上预报误差均比传统4dvar小, 在模式误差相对较大时改进效果更明显,具有明显优越性; 并且,模式误差弱约束4dvar能有效估计模式误差大小及分布形式.

Study of weak constraint 4dvar with model error forcing control variable

In the traditional implementations of four-dimensional variational data assimilation (4dvar for short), it is assumed that the model used is perfect. However the model error in the model can directly affect the accuracy of data assimilation. The weak constraint 4dvar is an effective way of correcting and estimating the model error in 4dvar. In this paper, an approach to weak constraint 4dvar with model error forcing control variable is studied and implemented in the one-dimensional shallow water equations. The results show that when the model error cannot be ignored, the prediction error with the weak constraint 4dvar is smaller than with the traditional 4dvar in both the assimilation window and the prediction period, and the improvement with weak constraint 4dvar is more obvious in the condition with large model error. Also, the weak constraint 4dvar approach to estimating model error captures some basic features of model error including the magnitude and the characteristic of distribution.