中文摘要：

本文根据最新的非同步（Asynchronous）算法设计了一个迭代EnSRF（iterative Ensemble Square Root Filter,简称iEnSRF）方案。在这个迭代方案中,同化时刻的背景场和一个较早时刻的背景场将被同时更新,得到两个时刻的分析场,然后预报模式从较早时刻的分析场再次进行集合预报到同化时刻,最后重复前面两个步骤,实现对同化时刻背景场的迭代分析。在一个理想风暴个例上,本文通过模拟雷达资料同化对这一方案进行了检验,对比了传统EnSRF方案和iEnSRF方案的同化效果。此外,本文还讨论了只在同化时刻一个时间层上进行迭代的情况。同化单部模拟雷达资料的试验表明iEnSRF方案能够在初始估计缺少风暴信息的情况下较好地还原风暴中垂直运动和潜热释放之间的正反馈关系,显著提高初始分析的质量并加快随后同化的收敛速度。而传统EnSRF在这一初始估计较差的情况下不能在初始分析中有效估计这一相关关系并导致其收敛速度较慢且收敛误差较大。当只涉及一个时间层时,迭代算法并不能取得比传统EnSRF更好的效果。这一结果表明重复使用观测的算法只有在涉及两个时间层时才能改进最终的分析结果。在同化两部模拟雷达资料的试验中,iEnSRF的初始分析仍然优于传统EnSRF的初始分析,并在对流层高层取得显著改进。单双雷达资料同化的试验结果对比表明,单纯增加观测数量并不能显著改进传统EnSRF对非观测变量（比如温度）的分析,而iEnSRF则能够更加充分地利用更多的观测进一步提升初始分析的效果。

英文摘要：

An iterative ensemble square root filter （iEnSRF） is designed on the basis of the latest asynchronous algorithm. In this iterative scheme, the forecast backgrounds at the analysis time and an earlier time are synchronously updated; then, the ensemble forecast is launched from the analysis field at the earlier time; finally, these two steps are repeated to producean iterative analysis of the background at the analysis time. The performance of this iterative scheme is examined using simulated radar data assimilation with an idealized storm case. The iEnSRF results are compared to those yielded by a traditional EnSRF. In addition, the performance of iteration involving only the background at the analysis time is discussed. The results obtained using data from a single simulated radar show that iEnSRF can effectively retrieve the positive feedback between the vertical motion and the latent heat release in the presence of a poor initial condition that provides no storm information. This improvement significantly optimizes the balance between different variables in the initial analysis and increases the convergence speed of assimilation. Conversely, the traditional EnSRF is unable to retrieve this positive feedback relationship in the initial analysis with the same poor initial condition, resulting in slower convergence and a larger analysis error. Iterative analysis cannot outperform the traditional EnSRF if the iteration considers only the background at the analysis time, indicating that considering two backgrounds at different times is necessary for the iterative algorithm to produce improvement. Results using data from two simulated radars show that the iEnSRF still outperforms the traditional EnSRF, especially in the upper troposphere. A comparison of the results using single radar data and dual radar data indicates that the traditional EnSRF cannot effectively use more data to improve the initial analysis of non-observed variables such as temperature, whereas the iEnSRF can effectively use more data to furthe