Abstract Hybrid systems have become the state of the art among data assimilation methods. These systems combine the benefits of two other systems that are traditionally used in operational weather forecasting: an ensemble-based system and a variational system. One of the most recently proposed hybrid approaches is called hybrid gain (HG). It obtains the final analysis as a linear combination of two analyses, assuming that the innovations (i.e. the forecast and the set of observations used) between the two data assimilation methods are identical. A perfect model experiment was performed using the HG in the SPEEDY model to show a new methodology to assign different weights to the two analyses, LETKF and 3D-Var in the generation of the final analysis. Our new approach uses, in the assignment of the weights, the ensemble spread, considered to be a measure of uncertainty in the LETKF. Thus, it is possible to use the estimation of the uncertainty of the analysis that the LETKF provides, to determine where the system should give more weight to the LETKF or the 3D-Var analysis. For this purpose, we define a geographically varying weighting factor alpha, which multiplies the 3D-Var analysis, as the normalised spread for each variable at each level. Then, (1-alpha), which decreases with increasing spread, becomes the factor that multiplies the LETKF analysis. The underlying mechanism of the spread–error relationship is explained using a toy model experiment. The results are very encouraging: the original HG and the new weighted HG analyses have similar high quality and are better than both 3D-Var and LETKF. However, the dynamically weighted HG analyses are significantly more balanced than the original HG analyses are, which has probably contributed to the consistently improved performance observed in the weighted HG, which increases with time throughout the 5-day forecasts.

中文翻译：

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动态加权混合增益数据同化：完美的模型测试

摘要 混合系统已成为数据同化方法中的最新技术。这些系统结合了传统上用于业务天气预报的其他两个系统的优点：基于集合的系统和变分系统。最近提出的一种混合方法称为混合增益 (HG)。假设两种数据同化方法之间的创新（即预测和所使用的一组观测）是相同的，它作为两种分析的线性组合获得最终分析。使用 SPEEDY 模型中的 HG 进行了完美的模型实验，以展示一种新方法，可以在生成最终分析时为 LETKF 和 3D-Var 两种分析分配不同的权重。我们的新方法在权重的分配中使用了集成散布，被认为是 LETKF 中不确定性的度量。因此，可以使用 LETKF 提供的分析不确定性的估计来确定系统应该在何处给予 LETKF 或 3D-Var 分析更多的权重。为此，我们定义了一个地理上不同的加权因子 alpha，它乘以 3D-Var 分析，作为每个级别每个变量的标准化分布。然后，（1-alpha）随着传播的增加而减小，成为乘以 LETKF 分析的因子。使用玩具模型实验解释了传播误差关系的潜在机制。结果非常令人鼓舞：原始 HG 和新的加权 HG 分析具有相似的高质量，并且优于 3D-Var 和 LETKF。然而，