Inflation method for ensemble Kalman filter in soil hydrology
Inflation method for ensemble Kalman filter in soil hydrology
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DOI：
10.5194/hess-22-4921-2018
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发表时间：
2018-09
影响因子：
6.3
通讯作者：
H. H. Bauser-H.;Daniel Berg;Ole Klein;K. Roth
中科院分区：
文献类型：
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作者：
H. H. Bauser-H.;Daniel Berg;Ole Klein;K. Roth
抽象的。集合卡尔曼滤波（EnKF）是土壤水文学中常用的数据同化方法。在这种情况下，它被用来同时估计状态和参数。由于未表示的模式误差和有限的集合规模，状态和参数的不确定性可能会变得太小同化。膨胀方法能够增加状态的不确定性，但通常与土壤水文应用斗争。我们提出了一个乘法膨胀方法，专门设计用于土壤水文学的需要。它在EnKF内采用卡尔曼滤波器，根据EnKF内的测量值和平均预测状态之间的差异来估计通货膨胀因子。我们证明了它的能力，一个小的土壤水文测试案例。该方法能够调整膨胀因子时空变化的模型误差。它成功地将膨胀转移到增广状态中的参数，从而改进了估计。
Abstract. The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology. In this context, it is used to estimate states and parameters simultaneously. Due to unrepresented model errors and a limited ensemble size, state and parameter uncertainties can become too small during assimilation. Inflation methods are capable of increasing state uncertainties, but typically struggle with soil hydrologic applications. We propose a multiplicative inflation method specifically designed for the needs in soil hydrology. It employs a Kalman filter within the EnKF to estimate inflation factors based on the difference between measurements and mean forecast state within the EnKF. We demonstrate its capabilities on a small soil hydrologic test case. The method is capable of adjusting inflation factors to spatiotemporally varying model errors. It successfully transfers the inflation to parameters in the augmented state, which leads to an improved estimation.