论文标题
涡流模型中随机和确定性缩减的比较：Lakshadweep Sea案例研究
A comparison of stochastic and deterministic downscaling in eddy resolving ocean modelling: the Lakshadweep Sea case study
论文作者
论文摘要
这项研究比较了两个具有相同水平分辨率的数值模型的技能，但基于代表Lakshadweep Sea（北印度洋）中海洋动力学的中尺度和子尺度特征的不同原理。第一个名为LD20-Nemo的模型基于使用Nemo（用于海洋建模的核）建模引擎求解原始方程。第二个标题为LD20-SDD，使用了较新的随机确定性降尺度方法。这两种模型均具有1/20O分辨率，并使用全球海洋物理分析的输出和以1/12O分辨率的预测模型可从哥白尼海洋服务（CMEMS）获得。 LD20-NEMO仅将来自CMEM的2D数据作为侧边界条件。 LD20-SDD消耗了来自CMEM的完整3D数据集，并利用这些数据的随机属性以比父模型更高的分辨率生成缩小的字段变量。 CMEMS，LD20-NEMO和LD20-SDD的三种技能是针对2015 - 2018年四年期的远程感知和原位观察的评估。所有型号均在复制温度和盐度方面均表现出相似的技能，但是SDD版本的性能比Nemo版本略好。分辨率的这种差异在模拟涡度和高度非线性过程所占据的海洋份额的计算方面尤其显着。尽管NEMO和SDD模型显示出相似的技能，但SDD模型比通过大的边距更有效地计算效率。
This study compares the skills of two numerical models having the same horizontal resolution but based on different principles in representing meso- and submesoscale features of ocean dynamics in the Lakshadweep Sea (North Indian Ocean). The first model, titled LD20-NEMO, is based on solving primitive equations using the NEMO (Nucleus for European Modelling of the Ocean) modelling engine. The second one, titled LD20-SDD, uses a newer Stochastic-Deterministic Downscaling method. Both models have 1/20o resolution and use the outputs from a Global Ocean Physics Analysis and Forecast model at 1/12o resolution available from Copernicus Marine Service (CMEMS). The LD20-NEMO uses only a 2D set of data from CMEMS as lateral boundary conditions. The LD20-SDD consumes the full 3D set of data from CMEMS and exploits the stochastic properties of these data to generate the downscaled field variables at higher resolution than the parent model. The skills of the three models, CMEMS, LD20-NEMO and LD20-SDD are assessed against remotely sensed and in-situ observations for the four-year period 2015-2018. All models show similar skills in reproducing temperature and salinity, however the SDD version performs slightly better than the NEMO version. This difference in resolution is particularly significant in simulation of vorticity and computation of the share of the sea occupied by highly non-linear processes. While the NEMO and SDD model show similar skill, the SDD model is more computationally efficient than the NEMO model by a large margin.
