Abstract

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure.

中文翻译：

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通过集合卡尔曼滤波评估观测资料同化在一般海洋环流模型中的效果：综合观测的数值实验

摘要

模型场的模拟质量取决于使用 PDAF 软件产品将合成数据同化到 NEMO 全球海洋模型中的各类数据的同化情况。进行了几个数值实验来模拟海洋 - 海冰系统。最初，自由模型使用不同的水平湍流粘度和扩散系数值运行，但具有相同的大气强迫。这些系数较高的模型输出用于确定后续数据同化实验中的第一猜测场，而系数较低的模型结果被假设为真实状态，并使用这些结果的一部分作为综合观察。分析结果是使用卡尔曼滤波器将各种类型的观测数据通过 PDAF 包含到具有真实底部地形的 NEMO 模型中。结果表明，数据同化过程中模型场的改善程度高度依赖于同化过程输入的数据结构。