Abstract. We present the new Cloud Class Climatology (CCClim) dataset, quantifying the global distribution of established morphological cloud types over 35 years. CCClim combines active and passive sensor data with machine learning (ML) and provides a new opportunity for improving the understanding of clouds and their related processes. CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud_cci dataset, adding relative occurrences of eight major cloud types, designed to be similar to those defined by the World Meteorological Organization (WMO) at 1° resolution. The ML framework used to obtain the cloud types is trained on data from multiple satellites in the afternoon constellation (A-Train). Using multiple spaceborne sensors reduces the impact of single-sensor problems like the difficulty of passive sensors to detect thin cirrus or the small footprint of active sensors. We leverage this to generate sufficient labeled data to train supervised ML models. CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological timescale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models. CCClim creates multiple opportunities to study clouds, of which we sketch out a few examples. Along with the cloud-type frequencies, CCClim contains the cloud properties used as inputs to the ML framework, such that all cloud types can be associated with relevant physical quantities. CCClim can also be combined with other datasets such as reanalysis data to assess the dynamical regime favoring the occurrence of a specific cloud type in association with its properties. Additionally, we show an example of how to evaluate a global climate model by comparing CCClim with cloud types obtained by applying the same ML method used to create CCClim to output from the icosahedral nonhydrostatic atmosphere model (ICON-A). CCClim can be accessed via the following digital object identifier: https://doi.org/10.5281/zenodo.8369202 (Kaps et al., 2023b).

中文翻译：

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使用 CCClim 数据集（机器学习云类气候学）表征云


摘要。我们推出了新的云类气候学 (CCClim) 数据集，量化了 35 年来已建立的形态云类型的全球分布。 CCClim 将主动和被动传感器数据与机器学习 (ML) 相结合，为提高对云及其相关过程的理解提供了新的机会。 CCClim 基于欧洲航天局 (ESA) Cloud_cci 数据集的云属性检索，添加了八种主要云类型的相对出现次数，旨在类似于世界气象组织 (WMO) 在 1° 分辨率下定义的云属性。用于获取云类型的机器学习框架是根据下午星座 (A-Train) 中多颗卫星的数据进行训练的。使用多个星载传感器可以减少单传感器问题的影响，例如无源传感器难以检测薄卷云或有源传感器占用空间小。我们利用它来生成足够的标记数据来训练监督机器学习模型。从 1982 年到 2016 年，CCClim 的全球覆盖范围几乎是无缝的，允许在气候时间尺度上对云进行面向过程的分析。同样，适度的空间和时间分辨率使其成为一个轻量级数据集，同时可以与气候模型进行直接比较。 CCClim 创造了多种研究云的机会，我们列出了一些例子。除了云类型频率之外，CCClim 还包含用作 ML 框架输入的云属性，以便所有云类型都可以与相关物理量相关联。 CCClim 还可以与其他数据集（例如再分析数据）相结合，以评估有利于特定云类型出现及其属性的动态状态。 此外，我们还展示了一个示例，说明如何通过将 CCClim 与云类型进行比较来评估全球气候模型，而云类型是通过应用用于创建 CCClim 的相同机器学习方法来从二十面体非静水力大气模型 (ICON-A) 输出获得的。可以通过以下数字对象标识符访问 CCClim：https://doi.org/10.5281/zenodo.8369202（Kaps 等人，2023b）。