中文摘要：

集合卡曼滤波由于易于使用而被广泛地应用到陆面数据同化研究中,它是建立在模型为线性、误差为正态分布的假设上,而实际土壤湿度方程是高度非线性的,并且当土壤过干或过湿时会发生样本偏斜。为了全面评估它在同化表层土壤湿度观测来反演土壤湿度廓线的性能,特引入不需要上述假设的采样重要性重采样粒子滤波,比较非线性和偏斜性对同化算法的影响。结果显示：不管是小样本还是大样本,集合卡曼滤波都能快速、准确地逼近样本均值,而粒子滤波只有在大样本时才能缓慢地趋近;此外,集合卡曼滤波的粒子边缘概率密度及其偏度和峰度与粒子滤波完全不同,前者粒子虽不完全满足正态分布,但始终为单峰状态,而后者粒子随同化推进经历了单峰到双峰再到单峰的变化。

英文摘要：

The ensemble Kalman filter is an easy to use,flexible,and efficient data assimilation algorithm widely used in Land Surface Data Assimilation System.It bases on the normality approximation of model error and observational error as well as the linearity assumption of the model.However,the soil moisture equation is highly nonlinear and also soil moisture can be highly skewed toward the wet or dry ends.To evaluate the effects of these approximations and the performance of the ensemble Kalman filter （EnKF） in estimating soil moisture profile based on the near-surface soil moisture measurements,the results from the EnKF are compared with those obtained from a Sequential Importance Resampling （SIR） particle filter that is one of nonlinear filters.The comparative results show：The EnKF can quickly and accurately obtain the exact soil moisture profile regardless of a small ensemble size or a large ensemble size;however,the SIR needs very large ensemble members.The near-surface soil moisture marginal forecast probability densities,the skewness and kurtosis obtained from the EnKF are completely different from those from the SIR filter;the densities from the EnKF is only one peak mode during the total assimilation time window while those from the SIR experience processes from one peak mode to two peak modes and again to one peak mode.