模式时间关联误差对集合平方根滤波估算土壤湿度的影响

为了定量评估模式时间关联误差对NOAH陆面模式同化表层土壤湿度观测估算土壤湿度廓线的影响,采用集合平方根滤波(En SRF)与状态增广相结合的技术,开展同时更新状态变量和订正模式偏差的观测系统模拟试验,结果表明:同化时若不对存在较大系统性偏差的模式时间关联误差进行处理,En SRF就不能有效估算土壤湿度廓线,而采用状态增广和En SRF相结合的技术,可以在更新土壤湿度时同步订正模式偏差,土壤湿度估算精度明显提高。敏感性试验进一步表明:模式偏差大小、同化时间间隔和观测误差会以不同方式对同化结果造成影响。

Impact of Time Correlated Model Errors on Soil Moisture Estimates with the Ensemble Square Root Filter

To evaluate the impacts of time correlated error models on the estimates of soil moisture profiles with the Ensemble Square Root Filter (EnSRF), an observing system simulation experiment was set up, in which the near-surface soil moisture observations were assimilated into NOAH Land Surface Model (LSM). The experiments used a combination technique of EnSRF and state augmentation, in which both the model states and model errors were updated at the same time. The results showed that EnSRF could not successfully estimate the soil moisture profiles, if a large model bias was directly ignored in the process of data assimilation. On the contrary, the soil moisture estimates had a large improvement, if the model errors were corrected. Furthermore, the sensitive tests showed that the value of model bias,the magnitudes of observational errors and the time intervals would all influence the estimates in the different way.