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The development of a 3D computational mesh to improve the representation of dynamic processes: The Black Sea test case
Ocean Modelling ( IF 3.1 ) Pub Date : 2020-02-01 , DOI: 10.1016/j.ocemod.2019.101534
Diego Bruciaferri , Georgy Shapiro , Sergey Stanichny , Andrey Zatsepin , Tal Ezer , Fred Wobus , Xavier Francis , Dan Hilton

Abstract The Black Sea is one of the largest land-locked basins in the world. Due to the vulnerability of its unique marine ecosystem, accurate long-term modelling of its hydrodynamics is needed. In this study, we first compare the skills of four NEMO based Black Sea models in a free-run which use different discretization schemes. We find that the most accurate results are obtained with the model (named CUR-ME s ) which has a 3D mesh optimized for the prevailing dynamics. This new model uses a curvilinear horizontal grid with increased resolution ( ≈ 950 m ) over the shelf-break and lower resolution ( ≈ 6 k m ) in areas where the scale of relevant processes is larger ( ≈ 20 k m ). In the vertical, CUR-MEs uses Multi-Envelope curved s-levels designed to optimize the representation of the Cold Intermediate Layer (CIL). Second, we compare CUR-MEs in free-run with the data-assimilative CMEMS reanalysis. Validation against independent observations shows that the two models have similar skills - e.g., the difference between the mean BIAS and RMSE of the two models is ≈ 0 . 15 ° C for temperature and ≈ 0 . 07 for salinity. The CUR-MEs model, even without data assimilation, is able to correctly reproduce the details of the variability of the Mean Kinetic Energy and the CIL.

中文翻译:

开发 3D 计算网格以改进动态过程的表示:黑海测试案例

摘要 黑海是世界上最大的内陆盆地之一。由于其独特的海洋生态系统的脆弱性,需要对其流体动力学进行准确的长期建模。在这项研究中,我们首先比较了四种基于 NEMO 的黑海模型在自由运行中使用不同离散化方案的技能。我们发现最准确的结果是使用模型(命名为 CUR-ME s )获得的,该模型具有针对主流动力学优化的 3D 网格。这个新模型使用曲线水平网格,其分辨率在大陆架断裂处增加(≈ 950 m),在相关过程规模较大(≈ 20 km)的区域中分辨率降低(≈ 6 km)。在垂直方向,CUR-ME 使用多包络曲线 s 级,旨在优化冷中间层 (CIL) 的表示。第二,我们将自由运行的 CUR-ME 与数据同化 CMEMS 再分析进行比较。对独立观察的验证表明,这两个模型具有相似的技能——例如,两个模型的平均 BIAS 和 RMSE 之间的差异 ≈ 0 。15 °C 的温度和 ≈ 0 . 07 盐度。即使没有数据同化,CUR-MEs 模型也能够正确再现平均动能和 CIL 变化的细节。
更新日期:2020-02-01
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