The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)
The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)
复制标题
DOI：
10.5194/essd-11-1745-2019
复制
发表时间：
2019-11-25
影响因子：
11.4
通讯作者：
Chepfer, Helene
中科院分区：
文献类型：
--
作者：
Cesana, Gregory;Del Genio, Anthony D.;Chepfer, Helene
低云继续极大地增加了云反馈估计的不确定性。根据一个区域是由积云（Cu）还是层积云（Sc）云主导，星载和大涡模拟研究中的年际低云反馈有所不同。因此，模拟 Cu 和 Sc 云分布的正确数量和变化对于预测未来的云反馈至关重要。在这里，我们记录了来自云气溶胶激光雷达和红外探路者卫星观测 (CALIPSO) 以及 CloudSat 测量的 Sc 和 Cu 云的空间分布和剖面。为此，我们创建了一个名为积云和层积云 CloudSat-CALIPSO 数据集 (CASCCAD) 的新数据集，该数据集识别 Sc、破碎 Sc、Sc 下的 Cu、层状流出的 Cu 和 Cu。为了将 Cu 与 Sc 分离，我们设计了一种基于云高、水平范围、垂直变化性和水平连续性的原始方法，该方法分别应用于 CALIPSO 和组合的 CloudSat CALIPSO 观测。首先，在选定的 Cu、Sc 和 Sc Cu 转变案例研究中对判别算法中使用的参数选择进行了研究和验证。然后，将全球统计数据与现有无源和有源传感器卫星观测数据进行比较。我们的结果表明，被动传感器观测中使用的云光学厚度不足以区分铜云和钪云，这与之前的文献一致。使用聚类派生的数据集显示出更好的结果，尽管使用这种方法无法完全分离云类型。相反，根据铜和钪云的几何形状和空间异质性对它们进行分类以及它们之间的过渡，会导致空间分布与这些云（来自地面、船基和野外活动）的先验知识一致。此外，我们表明我们的方法通过使用云高度和垂直云分数变化的附加信息改进了现有的 Sc Cu 分类。最后，CASCCAD 数据集为气候模型中评估全球范围内的浅对流和层积云提供了基础，并有可能提高我们对低层云反馈的理解。 CASCCAD 数据集（Cesana，2019，https://doi.org/10.5281/zenodo.2667637）可在戈达德空间研究所（GIS S）网站 https://data.giss.nasa.gov/clouds/casscad/（上次访问：2019 年 11 月 5 日）和 zenodo 网站 https://zenodo.org/record/2667637 上获取（最后访问日期：2019 年 11 月 5 日）。
Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness as used in passive-sensor observations is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GIS S) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).