A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds
A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds
复制标题
机器学习辅助开发对流云和层状云群模型
DOI：
10.1029/2019ms001798
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发表时间：
2020
影响因子：
6.8
通讯作者：
Hagos S
中科院分区：
文献类型：
--
作者：
Hagos S
对流与环境之间相互作用的传统参数化依赖于这样一个假设：缓慢变化的大尺度环境与大量小型且短命的对流云处于统计平衡。它们未能捕获非平衡转变，例如昼夜循环和中尺度对流系统的形成以及观测到的降水统计数据和极端情况。通过对雷达观测、云允许模型模拟、理论和机器学习的分析，这项工作提出了一种新的随机云种群动态模型，用于表征对流云和层状云之间的相互作用，目的是为全球气候模型中这些相互作用的表示提供信息。澳大利亚达尔文 C 波段雷达对 15 个雨季的降水云观测数据输入机器学习算法，以获得过渡函数，该函数闭合一组与大规模强迫、质量通量、对流单元尺寸分布和层状面积相关的耦合方程。在现实的大规模强迫下，导出的转换函数表明，一方面，与层状云的相互作用会抑制对流单元大小和数量的变化，从而抑制对流质量通量的变化。另一方面，对于给定的对流面积分数，较大数量的较小单元比较较少数量的较大单元更有利于层状面积的增长。这两个因素的结合产生了在大的层状区域中嵌入一些对流单元的解决方案，让人想起中尺度对流系统。
Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly varying large‐scale environment is in statistical equilibrium with a large number of small and short‐lived convective clouds. They fail to capture nonequilibrium transitions such as the diurnal cycle and the formation of mesoscale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud‐permitting model simulation, theory, and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds, with the goal of informing the representation of these interactions in global climate models. Fifteen wet seasons of precipitating cloud observations by a C‐band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equations relating large‐scale forcing, mass flux, the convective cell size distribution, and the stratiform area. Under realistic large‐scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a few convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems.
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影响因子：
3.9
作者：
A. Wada;T. Uehara;S. Ishizaki
通讯作者：
A. Wada;T. Uehara;S. Ishizaki
使用 AMIE/DYNAMO 现场活动期间收集的数据评估与马登-朱利安振荡相关的云群的对流允许模型模拟
DOI：
10.1002/2014jd022143
发表时间：
2014
期刊：
Journal of Geophysical Research: Atmospheres
影响因子：
--
作者：
S. Hagos;Zhe Feng;C. Burleyson;K. Lim;C. Long;Di Wu;G. Thompson
通讯作者：
G. Thompson
影响因子：
4.9
作者：
R. Wood;P. Field
通讯作者：
P. Field
影响因子：
5.2
作者：
Gentine, P.;Pritchard, M.;Yacalis, G.
通讯作者：
Yacalis, G.
影响因子：
6.8
作者：
O'Gorman, Paul A.;Dwyer, John G.
通讯作者：
Dwyer, John G.