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首页> 外文期刊>Journal of hydrometeorology >Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation
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Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation

机译：用数据同化法估算土壤水分时模型参数收敛性的评估

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Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation(EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.
机译：数据同化（DA）方法通常用于在不完善的观测值和不确定的模型预测之间找到折衷方案。对模型状态和参数的估计已得到广泛认可，但对估计参数的收敛性还没有进行深入研究。模型状态和参数值的分布与收敛紧密相关，进而影响DA方法的最终估计精度。这项示范性研究在将土壤水分和海洋盐分（SMOS）土壤水分同化到联合英国土地环境模拟器的背景下，研究了集成卡尔曼滤波器（EnKF）和进化数据同化（EDA）的模型参数的鲁棒性和收敛性。在澳大利亚东南部的Yanco地区。结果表明，与评估和验证阶段的开环估算相比，EnKF和EDA方法的土壤湿度估算准确性较高。在EDA方法中，对每个模型参数的收敛程度进行了量化，以说明其在检索未直接观察到的变量中的潜力。当针对SMOS 2级土壤湿度评估更新后的成员时，发现EDA具有比EnKF更高的估计精度。但是，如果在同化时间段之外针对SMOS土壤水分验证了其前向土壤水分估算，则EnKF和EDA估算是可比的。这表明参数收敛不会显着影响EnKF的土壤湿度估算准确性。但是，EDA的优点是可以同时确定模型参数的收敛性，同时可以为土壤水分估算提供更高的准确性。

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