论文标题
变分随机参数及其在原始方程模型中的应用
Variational Stochastic Parameterisations and their Applications to Primitive Equation Models
论文作者
论文摘要
我们提出了对原始方程（PE）的随机参数（PE）的数值研究，该参数使用Lie Transpers（Salt）和Lie Transpers（SFLT）框架强迫的随机对流。这些框架是由于其结构的随机性引入而选择的，后者分别将传输速度和流体动量分解为它们的漂移和随机部分。在本文中，我们开发了一种新的校准方法来实施SFLT的动量分解，并与针对盐实施的拉格朗日路径方法进行了比较。然后，使用FESOM2代码的修改将所得随机的原始方程进行数值集成。对于随机参数的某些选择，我们表明盐会导致涡流动能场的增加和空间光谱的改善。 SFLT在较小程度上也显示出这些领域的改善。但是，盐确实具有过度向下的温度扩散的缺点。
We present a numerical investigation into the stochastic parameterisations of the Primitive Equations (PE) using the Stochastic Advection by Lie Transport (SALT) and Stochastic Forcing by Lie Transport (SFLT) frameworks. These frameworks were chosen due to their structure-preserving introduction of stochasticity, which decomposes the transport velocity and fluid momentum into their drift and stochastic parts, respectively. In this paper, we develop a new calibration methodology to implement the momentum decomposition of SFLT and compare with the Lagrangian path methodology implemented for SALT. The resulting stochastic Primitive Equations are then integrated numerically using a modification of the FESOM2 code. For certain choices of the stochastic parameters, we show that SALT causes an increase in the eddy kinetic energy field and an improvement in the spatial spectrum. SFLT also shows improvements in these areas, though to a lesser extent. SALT does, however, have the drawback of an excessive downwards diffusion of temperature.
