A single-layer, quasi-geostrophic (QG), large-scale ocean circulation model is developed in this paper to study available ocean current energy potentials harnessed by using the ocean current turbines. Power extraction is modeled by adding a parameterized Rayleigh friction term in the barotropic vorticity equation. Numerical assessments are performed by simulating a set of mid-latitude ocean basins in the beta plane, which are standard prototypes of more realistic ocean dynamics considering inter-decadal variability in turbulent equilibrium. A sensitivity analysis with respect to the turbine parameters is performed for various physical conditions. Results show that the proposed model captures the quasi-stationary ocean dynamics and provides the four-gyre circulation patterns in time mean. After an initial spin-up process, the proposed model reaches a statistically steady state at an average maximum speed between 1.5 m/s and 2.5 m/s, which is close to the observed maximum zonal velocities in the western boundary currents. The probability density function of the available power over a long time period is computed for a wide range of parameters. Numerical results shows that 10 GW mean power can be extracted from the turbines distributed over a length scale of 100 km along the western boundaries. However, it is demonstrated that bigger turbine areas would alter the flow patterns and energetics due to excessive dissipation. An increase in the turbine area results in an increase in the available power ranging from 8 to 22 GW depending on the values of turbine modeling parameters. This first step in the numerical assessment of the proposed QG model shows that the present framework could represent a viable tool for evaluating energy potentials in a highly turbulent flow regime.
Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise
本文介绍了作者在发展高维流体动力学模式基于集合的数据同化方法的项目中的最新进展。这里提出的算法是一个粒子滤波器，结合了模型简化，回火，抖动和轻推。该方法进行了测试的两层准地转模式的$β$ -平面通道流与$O(10^6)$度的自由，其中只有一分钟的一小部分噪音观察。该模型通过遵循Holm（Proc Roy Soc A，2015）中引入的地球物理流体动力学的随机变分方法来简化，作为推导未分辨尺度的随机参数化的框架。减少是实质性的：计算仅针对$O(10^4)$自由度进行。我们引入了一个随机的时间步进计划的两层模型，并证明其在时间上的一致性。然后，我们分析了不同的程序（回火与抖动和轻推相结合）的数据同化过程中使用的降阶模型的性能的影响，以及观测数据的尺寸（“气象站”的数量）和数据同化步骤如何影响结果的准确性和不确定性。
Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
Hong Kong University of Science and Technology（香港科技大学）；Institut de Physique du Globe de Paris（巴黎地球物理研究所）；Université Grenoble-Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；National Oceanography Centre, Southampton（南安普顿国家海洋中心）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
