中文摘要：

目前一种比较流行并且可行的同化方法一集合Kalman滤波（EnKF）能够计算依赖于流的误差统计量。理论上，EnKF能够比最优插值、三维变分等更准确地计算误差统计量，能更好地融合背景场和观测场的信息。作者利用二维平流扩散方程经过10天的同化循环，比较不同观测分布的情况下EnKF和最优插值（OI）的模拟能力。理想试验结果显示，随着观测分布密度的减小，尤其是当观测的分辨率大于OI估计的相关尺度时，集合Kalman滤波的结果比最优插值有更明显的改进。

英文摘要：

The performance of data assimilation using the ＂flow-dependent＂ statistics calculated from an ensemble of short-range forecasts （termed as Ensemble Kalman Filter, EnKF） with 100 members compared with Optimal Interpolation （OI） is examined in an idealized environment. In order to test which one is much better for dust storms or pollutants data assimilation, using a S-dimension diffusion equation and simulated observations, a series of 10-day data assimilation cycles are performed in a perfect model context with different observational networks. The results indicate that as the resolution of observations decreases, EnKF improves more than OI, especially when the resolution becomes much higher than the estimated correlation scale used in OI, which usually happens for observations of dust.