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首页> 外文期刊>Advances in Water Resources >Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation
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Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

机译：评估数据同化方法的预测性能：集成卡尔曼滤波，粒子滤波和基于进化的同化

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Data assimilation (DA) has facilitated the design and application of hydrological forecasting systems. DA methods such as the ensemble Kalman filter (EnKF) and the particle filter (PF) remain popular in the hydrological literature. But a comparative evaluation of these methods to alternative techniques like the evolutionary based data assimilation (EDA) has not been thoroughly conducted. Evolutionary algorithms have been widely applied in parameter estimation and it appears natural that its application in DA be compared to standard methods, particularly, to evaluate forecasting performance of these methods. This type of evaluation is important for the design of forecasting systems and has implications for real-time forecasting operations. This study has applied the Sacramento Soil Moisture Accounting (SAC-SMA) model in the Spencer Creek catchment in southern Ontario, Canada to evaluate the performance of three DA methods. The methods assimilate streamflow into SAC-SMA, where the updated ensemble members are in turn applied to forecast streamflow for up to 30-day lead time after which they were compared to observation and open-loop estimates. The results showed that the increasing order of performance at assimilation stage and forecasting for short lead times of 10-day is the EnKF, the PF and the EDA. For longer lead times, the PF performs best and is preferable when forecasting for lead times beyond 10-day. The EnKF and the PF evolve members once between assimilation time steps whereas the EDA evolves members multiple times to improve parameter convergence. The high performance of the EDA illustrates that the dynamics of large ensemble members can be encapsulated into a small continuously evolved population and that these members have high assimilation and forecasting capability.
机译：数据同化（DA）促进了水文预报系统的设计和应用。诸如集成卡尔曼滤波器（EnKF）和粒子滤波器（PF）之类的DA方法在水文学中仍然很流行。但是，尚未对这些方法与替代技术（如基于进化的数据同化（EDA））进行比较评估。进化算法已广泛应用于参数估计，并且很自然地将其在DA中的应用与标准方法进行比较，尤其是评估这些方法的预测性能。这种类型的评估对于预测系统的设计很重要，并且对实时预测操作具有影响。这项研究已在加拿大安大略省南部的斯宾塞溪流域应用了萨克拉曼多土壤湿度会计（SAC-SMA）模型来评估三种DA方法的性能。该方法将流量吸收到SAC-SMA中，在该方法中，将更新后的集合成员反过来应用于长达30天的前置时间的预测流量，然后将它们与观测值和开环估算值进行比较。结果表明，EnKF，PF和EDA在同化阶段的性能递增顺序以及对10天的短交货期的预测。对于更长的交货期，PF表现最好，并且在预测10天以上的交货期时更可取。 EnKF和PF在同化时间步长之间演化一次成员，而EDA多次演化成员以提高参数收敛性。 EDA的高性能表明，可以将大型集合成员的动态封装为一个小的连续演化的种群，并且这些成员具有很高的同化和预测能力。

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