中文摘要：

介绍了一种新的数据同化算法（UKF，Unscented Kalman Filter），该算法不需要计算伴随矩阵，就能够解决模式的非线性问题。以Lorenz系统为例，进行了数据同化的数值试验。结果表明：基于UKF的同化方案与背景场的初始值无关，它能有效地抑制状态变量误差的增长，同化结果精度高。

英文摘要：

Unscented Kalman Filter （UKF） is a new data assimilation algorithm, which does not need adjoint matrix and can resolve the problem of nonlinearity existing in models. In this paper, the UKF algorithm is described in detail. The Lorenz model is used in numerical experiments to examine the performance of UKF. The results show that the data assimilation scheme based on UKF is independent of the first guess of background field. This method can also restrain the increase of state error and the assimilation results are satisfying.