Because the true state of complex physical systems is out of reach for real-world data assimilation problems, error covariances are uncertain and their specification remains very challenging. These error covariances are crucial ingredients for the proper use of data assimilation methods and for an effective quantification of the a posteriori errors of the state estimation. Therefore, the estimation of these covariances often involves at first a chosen specification of the matrices, followed by an adaptive tuning to correct their initial structure. In this paper, we propose a flexible combination of existing covariance tuning algorithms, including both online and offline procedures. These algorithms are applied in a specific order such that the required assumption of current tuning algorithms are fulfilled, at least partially, by the application of the ones at the previous steps. We use our procedure to tackle the problem of a multivariate and spatially-distributed hydrological model based on a precipitation-flow simulator with real industrial data. The efficiency of different algorithmic schemes is compared using real data with both quantitative and qualitative analysis. Numerical results show that these proposed algorithmic schemes improve significantly short-range flow forecast. Among the several tuning methods tested, recently developed CUTE and PUB algorithms are in the lead both in terms of history matching and forecast.

由于复杂物理系统的真实状态对于现实世界中的数据同化问题是遥不可及的，因此误差协方差是不确定的，其规格仍然非常具有挑战性。这些误差协方差是正确使用数据同化方法和有效量化后验的关键要素状态估计的误差。因此，这些协方差的估计通常首先涉及矩阵的选定规范，然后进行自适应调整以校正其初始结构。在本文中，我们提出了现有协方差调整算法的灵活组合，包括在线和离线过程。这些算法以特定的顺序应用，以使当前调整算法的要求假设至少部分地通过在先前步骤中的应用得以实现。我们使用我们的程序来解决基于具有真实工业数据的降水流模拟器的多元空间分布水文模型的问题。使用真实数据以及定量和定性分析来比较不同算法方案的效率。数值结果表明，这些提出的算法方案显着改善了短程流量预测。在测试的几种调整方法中，最近开发的CUTE和PUB算法在历史匹配和预测方面均处于领先地位。