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首页> 外文期刊>Water resources research >Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter
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Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter

机译：用集合卡尔曼滤波器校正土壤水分同化的预测偏差

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Land surface models are usually biased in at least a subset of the simulated variables even after calibration. Bias estimation may therefore be needed for data assimilation. Here, in situ soil moisture profile observations in a small agricultural field were merged with Community Land Model (CLM2.0) simulations using different algorithms for state and forecast bias estimation with and without bias correction feedback. Simple state updating with the conventional ensemble Kalman filter (EnKF) allows for some implicit forecast bias correction. It is possible to estimate the soil moisture bias explicitly and derive superior soil moisture estimates with a generalized EnKF that uses a simple persistence model for the bias and assumes that the a priori bias error covariance is proportional to the a priori state error covariance. For the case of bi-weekly assimilation of the entire profile of soil moisture observations, bias estimation and correction typically reduces the RMSE in soil moisture (over the standard EnKF without bias correction) by around 60 percent. However, under the above assumptions, significant improvements are limited to state variables for which observations are available. Therefore, it is crucial to measure the state variables of interest. The best variant for state and bias estimation depends on the nature of the model bias and the output of interest to the user. In a model that is only biased for soil moisture, large and frequent increments for soil moisture updating may be required, which in turn may negatively impact the water balance and output fluxes. It is then better to post-process the soil moisture with the bias analysis without updating the model state.
机译：即使在校准后，通常至少在一部分模拟变量中也会对陆地表面模型产生偏差。因此，数据同化可能需要偏差估计。在这里，将在一个小农田中的原位土壤水分剖面观测值与社区土地模型（CLM2.0）模拟进行了合并，使用不同的算法对状态和预测偏差进行估算，并带有和不带有偏差校正反馈。使用常规的集成卡尔曼滤波器（EnKF）进行简单的状态更新可以进行一些隐含的预测偏差校正。可以明确地估计土壤湿度偏差，并使用广义EnKF得出较高的土壤湿度估计值，该EnKF使用简单的持久性模型作为偏差并假设先验偏差误差协方差与先验状态误差协方差成比例。对于每两周对土壤湿度观测值的整个过程进行同化的情况，偏差估计和校正通常会使土壤湿度的RMSE降低（超过未校正偏差的标准EnKF）约60％。但是，在上述假设下，显着的改进仅限于可获得观察结果的状态变量。因此，测量感兴趣的状态变量至关重要。状态和偏差估计的最佳变体取决于模型偏差的性质以及用户感兴趣的输出。在仅对土壤水分有偏见的模型中，可能需要大量频繁地增加土壤水分，这反过来可能对水平衡和输出通量产生负面影响。然后最好用偏差分析对土壤水分进行后处理，而不更新模型状态。

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