中文摘要：

同化系统中的误差问题一直被认为是制约数据同化性能的瓶颈问题。从分析陆面数据同化系统的误差问题研究现状出发,统一定义了同化系统的误差来源及误差表现,简要综述了顺序同化方法及连续同化方法中的误差定义和相关理论问题。从误差估计的角度,重点介绍了目前研究中各种误差估计的方法和面临的困难。针对误差处理方法的研究,介绍了在集合数据同化中为减小误差常用的乘数放大法、附加放大法和松弛先验法等模型误差参数化方案,并且介绍了在实际数据同化系统中为减小系统偏差常采用状态增广法。最后总结讨论了各种误差估计与处理方法的特点及其在陆面数据同化中的应用前景和发展方向。

英文摘要：

As an important methodology for optimally merging Earth observation information and geophysical model output information,data assimilation has played an important role in the area of Earth observation.At present,great progress has been made in the theoretical and methodological exploration and foundation of the operational land data assimilation system.Due to the complexity of research objectives,error problems are thought to be the bottleneck for improving the performance of data assimilation systems.Firstly,the research statuses of error problems of Land Data Assimilation Systems are reviewed.Based on the mathematical descriptions of land surface process model and measurement process,error sources and error characteristic are unifying defined.In a word,data assimilation systems include model errors,observation errors and the algorithm errors.Secondly,with respect to the sequential and variational data assimilation methods,error definitions and the related theoretical problems of those methods are briefly introduced with the emphasis on the error sources and the fundamental error parameterization methods.Moreover,from the perspective of error estimation,several novel methods for estimating model errors are reviewed from three parts： the model input error estimation,the model parameters error estimation and the model structure error estimations.As for the observation errors,the error sources can be divided with the observation algorithm errors,the representative errors and the instrument errors.Beside some exiting methods,there are no more effectively methods to deal with those kinds of error.Meanwhile,the difficulties for implementing all those methods are clarified.Thirdly,in order to reduce the errors for ensemble data assimilation systems,the common error parameterization methods,such as multiplicative inflation methods,additive inflation methods and the relax-to-prior methods,are employed.All these methods for dealing with model errors are meant to ameliorate the bias error in ensemble second moment.As far