Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm
Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm
复制标题
在多层一维太阳辐射传输算法中考虑亚网格规模的云变化
DOI：
10.1002/qj.49712555316
复制
发表时间：
1999
影响因子：
8.9
通讯作者：
H. Barker
中科院分区：
文献类型：
--
作者：
L. Oreopoulos;H. Barker
提出了一种考虑次网格尺度云变化的一维多层太阳辐射传输算法。该算法具有较高的效率，适用于全球气候和天气预报等大尺度模式。虽然它建立在与标准多层一维码相同的原理上，但有两个主要的区别。首先，假设对于所有的云层，光学厚度τ的频率分布由伽马概率密度函数Pr(τ)来描述，并由平均光学厚度τ和与方差有关的参数v来表征。各层的反照率和透过率由平面平行的齐次双流近似方程的所有τ上的积分来估计，该方程按Pr(τ)加权。因此，该模型被称为伽马加权两流近似。其次，为了抵消水平均匀通量的使用，设计了一种经常降低τ层值的方法。
A multi‐layer, 1D solar radiative transfer algorithm that accounts for subgrid‐scale cloud variability is presented. This algorithm is efficient and suitable for use in large‐scale models such as global climate and weather prediction models. While it is built on the same principles as standard multi‐layer 1D codes, there are two major differences. First, it is assumed that for all cloudy layers all the time, frequency distributions of optical depth τ are described by gamma probability density functions pr(τ) and characterized by mean optical depth τ and a variance‐related parameter v. Albedos and transmittances for individual layers are estimated by integrals over all τ of the plane‐parallel, homogeneous two‐stream approximation equations weighted by pr(τ). Thus, the model is referred to as the gamma‐weighted two‐stream approximation. Second, in an attempt to counteract the use of horizontally homogeneous fluxes, a method was devised that often reduces layer values of τ.