Stochastic parameterization of cloud processes
Stochastic parameterization of cloud processes
复制标题
云过程的随机参数化
DOI：
10.1016/j.atmosres.2014.01.027
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发表时间：
2014
影响因子：
5.5
通讯作者：
A. Bott
中科院分区：
文献类型：
--
作者：
A. Bott
长期以来，经典的集合预报主要集中在通过初始条件和边界条件的变化来模拟观测的不确定性。为了补充预报模型本身的固有贡献，随机物理被提出作为提高预报质量和增加欠色散系综的系综扩散的适当方法。专注于云过程及其时间发展，我们在这里将一组随机参数引入到现有的确定性物理参数化方案中，例如对流方案中的触发函数、闭合假设和湍流混合，以及网格尺度中的初始冰浓度和湍流收集内核。考虑到随机参数受到未解决的湍流波动的影响，我们基于直接数值模拟的方法从朗之万方程推导出相应的随机过程。这允许包含根据宿主模型的涡耗散率和湍流动能计算得出的湍流自相关时间。将两种气象不同实际情况的集合模拟与确定性控制模拟和 1 小时降水总和的观测进行比较。湍流自相关时间显示出与锋面动力学和对流活动相关的明显空间变化。空间降水模式图和连续排序概率得分 (CRPS) 改进显示了包括随机物理在内的集合平均值的更好的预测质量。根据气象情况，集合扩展和 CRPS 改进都表现出旋转时间并达到饱和。对于气团对流/额后阵雨，扩散和 CRPS 改善高于锋面降水。亚网格尺度云量的随机参数化被发现降低了集合的预报质量。这是因为增加的变异性的空间分布过于均匀。因此，这部分随机参数化已从集合模拟中排除，并保留在其操作确定性版本中。
For a long time, classical ensemble forecasts have concentrated on simulating the uncertainties of the observations by variations of the initial and boundary conditions. To supplement this with inherent contributions from the forecast model itself, stochastic physics has been proposed as an appropriate approach to improve the forecast quality and to increase the ensemble spread of underdispersive ensembles.Concentrating on cloud processes and their temporal development we here introduce a set of stochastic parameters into existing deterministic physical parameterization schemes as for the trigger function, closure assumption and turbulent mixing in the convection scheme, for the initial ice concentration and turbulent collection kernel in the grid scale precipitation/microphysics scheme and for the subgrid scale cloud cover in the radiation scheme.Considering the stochastic parameters to be subject to unresolved turbulent fluctuations, we derive the corresponding stochastic processes from a Langevin equation based on an approach of direct numeric simulation. This allows for the inclusion of a turbulent autocorrelation time calculated from the eddy dissipation rate and the turbulent kinetic energy of the hosting model.Ensemble simulations of two meteorologicaly different real cases are compared to deterministic control simulations and observations of 1 h precipitation sums. The turbulent autocorrelation time shows pronounced spatial variability correlated with frontal dynamics and convective activity. Maps of spatial precipitation patterns and continuous ranked probability score (CRPS) improvements show a better forecast quality of the ensemble mean including stochastic physics. Depending on the meteorological situation, the ensemble spread and the CRPS improvement both exhibit a spin up time and reach a saturation. For air-mass convection/postfrontal showers the spread and the CRPS improvement are higher than for frontal precipitation.The stochastic parameterization of subgrid scale cloud cover is found to degrade the forecast quality of the ensemble. This is explained by a too homogeneous spatial distribution of the added variability. Consequently, this part of the stochastic parameterization has been excluded from the ensemble simulations and is left in its operational deterministic version.
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DOI：
--
发表时间：
2000
期刊：
影响因子：
--
作者：
R. E. Evans;M. Harrison;R. Graham;K. Mylne
通讯作者：
K. Mylne
使用混合质量通量对流方案 HYMACS 模拟对流的非局部效应
DOI：
--
发表时间：
2011
期刊：
影响因子：
--
作者：
V. Kuell;A. Bott
通讯作者：
A. Bott
影响因子：
3.1
作者：
R. Plant;G. Craig
通讯作者：
R. Plant;G. Craig
影响因子：
3.1
作者：
M. Pinsky;A. Khain;H. Krugliak
通讯作者：
H. Krugliak
关于随机湍流模型中的柯尔莫哥洛夫常数
DOI：
--
发表时间：
2002
期刊：
影响因子：
--
作者：
S. Heinz
通讯作者：
S. Heinz