Discovering the Importance of Mesoscale Cloud Organization Through Unsupervised Classification
Discovering the Importance of Mesoscale Cloud Organization Through Unsupervised Classification
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DOI：
10.1029/2019gl085190
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发表时间：
2020-01-16
影响因子：
5.2
通讯作者：
Denby, L.
中科院分区：
文献类型：
--
作者：
Denby, L.
浅信风对流云在气候模式中的代表性主导了气候敏感性估计的不确定性。特别是云的空间组织的辐射影响知之甚少。这项工作提出了第一个无监督的神经网络模型，自主发现云组织制度的卫星图像。训练10,000 GOES-16卫星图像（热带大西洋和北方冬季）的制度发现存在于一个层次结构的组织规模，子集群具有不同的辐射特性。该模型不需要基于预定义结构的耗时和主观的手工标记数据，从而可以对非常大的数据集进行客观研究。该模型使环境条件的研究，在不同的组织制度和制度之间的过渡和客观的比较模型的行为与观察，通过云结构出现在这两个。这些能力使人们能够发现云过程中以前未知的物理关系，从而能够在天气和气候模拟中更好地表示云。
The representation of shallow trade wind convective clouds in climate models dominates the uncertainty in climate sensitivity estimates. In particular the radiative impact of cloud spatial organization is poorly understood. This work presents the first unsupervised neural network model which autonomously discovers cloud organization regimes in satellite images. Trained on 10,000 GOES-16 satellite images (tropical Atlantic and boreal winter) the regimes found are shown to exist in a hierarchy of organizational scales, with sub-clusters having distinct radiative properties. The model requires no time-consuming and subjective hand-labeled data based on predefined structures allowing for objective study of very large data sets. The model enables the study of environmental conditions in different organizational regimes and in transitions between regimes and objective comparisons of model behavior with observations through cloud structures emerging in both. These abilities enable the discovery of previously unknown physical relationships in cloud processes, enabling better representation of clouds in weather and climate simulations.