Balanced and Unbalanced Components of Moist Atmospheric Flows with Phase Changes

Balanced and Unbalanced Components of Moist Atmospheric Flows with Phase Changes
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具有相变的潮湿大气流的平衡和不平衡分量

DOI:
10.1007/s11401-019-0170-4
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发表时间:
2019
期刊:
Series B
影响因子:
--
通讯作者:
Martin, Jonathan E.
Martin, Jonathan E.
中科院分区:
--
文献类型:
--
作者:
Wetzel, Alfredo N.;Smith, Leslie M.;Stechmann, Samuel N.;Martin, Jonathan E.

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大气变量(温度、速度等)通常被分解成分别代表低频波和高频波的平衡分量和不平衡分量。例如,可以根据线性算子的本征模式来定义这种分解。传统上,这些分解忽略了水的相变,因为相变创建了在不同阶段(多云和非多云)不同的分段线性运算符。在这里,我们研究以下问题:如何在存在相变的情况下执行平衡-非平衡分解?本文描述了一种由小的Froude数和Rossby数激励的方法,在这种情况下,渐近极限产生了具有相变的准地转方程。利用其零频本征值,可以通过位涡(PV)反演得到平衡分量,通过求解包含相变引起的Heaviside不连续的椭圆型偏微分方程(PDE)。该方法还与两种较简单的方法进行了比较:一种忽略相变,另一种简单地将原始压力数据视为流函数。对合成的理想化数据和来自天气研究和预报(WRF)模型模拟的数据进行了测试。相比之下,由于云层的存在和数据中的相变,相变法和无相变法在云区内产生了大约5K的位温差异。对于这两个流函数的差异,我们还用椭圆偏微分方程组的形式给出了理论上的证明。
Atmospheric variables (temperature, velocity, etc.) are often decomposed into balanced and unbalanced components that represent low-frequency and high-frequency waves, respectively. Such decompositions can be defined, for instance, in terms of eigen-modes of a linear operator. Traditionally these decompositions ignore phase changes of water since phase changes create a piecewise-linear operator that differs in different phases (cloudy versus non-cloudy). Here we investigate the following question: How can a balanced-unbalanced decomposition be performed in the presence of phase changes? A method is described here motivated by the case of small Froude and Rossby numbers, in which case the asymptotic limit yields precipitating quasi-geostrophic equations with phase changes. Facilitated by its zero-frequency eigenvalue, the balanced component can be found by potential vorticity (PV) inversion, by solving an elliptic partial differential equation (PDE), which includes Heaviside discontinuities due to phase changes. The method is also compared with two simpler methods: one which neglects phase changes, and one which simply treats the raw pressure data as a streamfunction. Tests are shown for both synthetic, idealized data and data from Weather Research and Forecasting (WRF) model simulations. In comparisons, the phase-change method and no-phase-change method produce substantial differences within cloudy regions, of approximately 5 K in potential temperature, due to the presence of clouds and phase changes in the data. A theoretical justification is also derived in the form of a elliptic PDE for the differences in the two streamfunctions.
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