Ocean Sciences Department, UC Santa Cruz, Santa Cruz, CA, United States of America.
PLoS One. 2023 Sep 5;18(9):e0291039. doi: 10.1371/journal.pone.0291039. eCollection 2023.
Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.
高级海洋生态系统模型可以包含 100 多个生物地球化学变量，这使得对这些模型的数据同化成为一个具有挑战性的前景。传统的变分数据同化技术，如 4dVar，依赖于切线线性和伴随代码，对于具有几十个以上变量的复杂生态系统模型来说，这可能很难创建。最近的混合集合变分数据同化技术使用模型预测的集合来生成模型统计数据，因此可以避免切线线性或伴随代码的需求。我们提出了一种新的用于耦合物理生态系统模型的四维集合最优插值（4dEnOI）技术的实现方法。我们的 4dEnOI 实现使用小集合、空间和变量协方差本地化来创建可靠的依赖于流的统计数据。该技术易于实现，不需要切线线性或伴随代码，并且在计算上适合于先进的生态系统模型。我们将 4dEnOI 实现与简单海洋生态系统模型的 4dVar 技术进行了比较，该模型有 4 个生物地球化学变量，与加利福尼亚洋流系统的物理循环模型耦合。在这些测试中，我们的 4dEnOI 参考实现与 4dVar 基准在降低模型观测误差方面表现相似。我们表明，4dEnOI 结果通常严重依赖于协方差本地化，特别是在应用于降低物理和生物地球化学模型以及生物地球化学变量之间的耦合强度时，从变量本地化中受益。通过对算法进行小的修改，如多次 4dEnOI 迭代，可以进一步改进 4dEnOI 结果，但需要额外的计算成本。
