论文标题
热海洋中的随机中尺度循环动力学
Stochastic mesoscale circulation dynamics in the thermal ocean
论文作者
论文摘要
在类似于绝热可压缩流体动力学中的影响相似,浮力梯度对不可压缩分层流的影响被称为“热热”。热旋转浅水(TRSW)模型方程包含三个小的无二维参数。这些是rossby号码,弗罗德号和浮力参数。在这三个小参数中,TRSW模型方程的渐近扩展导致鲑鱼的L1(TL1)模型的确定性热版本和热准化地(TQG)模型在热量循环中的热量平衡范围内扩展,并在流动速度和梯度上的梯度和Buoy级别和Buoy级别和Buoy的梯度中扩展。在高波数下TQG的线性不稳定性倾向于在小尺度上产生循环。在许多计算模拟中,如此高的波数不稳定性可能无法解决,但是它在小尺度上的存在可能会对可分解尺度下的流体传输产生重大贡献。有时,这种影响是通过“动能的随机反向散射”建模的。在这里,我们尝试另一种方法。也就是说,我们在trsw/tl1/tqg的模型层次结构中对“随机传输”进行建模。这些模型是通过从最近引入的Euler-Poincaré变化原理的随机版本获得的随机对流(盐)的方法得出的。我们还指出,通过使用数据驱动的随机参数参数算法,在这三个级别的描述下,将这些模型应用于不确定性定量和数据同化的不确定性定量和数据同化,以前是使用盐方法得出的。
In analogy with similar effects in adiabatic compressible fluid dynamics, the effects of buoyancy gradients on incompressible stratified flows are said to be `thermal'. The thermal rotating shallow water (TRSW) model equations contain three small nondimensional parameters. These are the Rossby number, the Froude number and the buoyancy parameter. Asymptotic expansion of the TRSW model equations in these three small parameters leads to the deterministic thermal versions of the Salmon's L1 (TL1) model and the thermal quasi-geostrophic (TQG) model, upon expanding in the neighbourhood of thermal quasi-geostrophic balance among the flow velocity and the gradients of free surface elevation and buoyancy. The linear instability of TQG at high wave number tends to create circulation at small scales. Such a high wave number instability could be unresolvable in many computational simulations, but its presence at small scales may contribute significantly to fluid transport at resolvable scales. Sometimes such effects are modelled via `stochastic backscatter of kinetic energy'. Here we try another approach. Namely, we model `stochastic transport' in the hierarchy of models TRSW/TL1/TQG. The models are derived via the approach of stochastic advection by Lie transport (SALT) as obtained from a recently introduced stochastic version of the Euler--Poincaré variational principle. We also indicate the potential next steps for applying these models in uncertainty quantification and data assimilation of the rapid, high wavenumber effects of buoyancy fronts at these three levels of description by using the data-driven stochastic parametrisation algorithms derived previously using the SALT approach.