变分随机参数化及其在原始方程模型中的应用
Variational Stochastic Parameterisations and their Applications to Primitive Equation Models
AI总结：
本文基于SALT和SFLT框架研究原始方程的随机参数化，提出新的校准方法，并数值验证了SALT和SFLT对涡动动能和空间谱的改进，同时指出SALT存在温度过度向下扩散的缺点。
摘要：
我们针对使用随机平流李输运（SALT）和随机强迫李输运（SFLT）框架的原始方程（PE）的随机参数化进行了数值研究。选择这些框架是因为它们以保持结构的方式引入随机性，分别将输运速度和流体动量分解为漂移部分和随机部分。在本文中，我们开发了一种新的校准方法来实现SFLT的动量分解，并与为SALT实现的拉格朗日路径方法进行了比较。随后，使用修改后的FESOM2代码对由此产生的随机原始方程进行了数值积分。对于某些随机参数的选择，我们表明SALT会导致涡动动能场增加，并改善空间谱。SFLT在这些方面也显示出改进，但程度较小。然而，SALT确实存在温度过度向下扩散的缺点。
英文摘要：
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.
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