CASCCAD: Cumulus And Stratocumulus Cloudsat-CAlipso Dataset

In these two datasets, we document spatial distributions and profiles of stratocumulus (Sc) and cumulus (Cu) clouds on a global scale. To this end, we design an original discrimination algorithm (DA) that distinguishes Sc and Cu based on three observable cloud-properties: cloud top height (CTH), horizontal cloud fraction (HCF) and vertical cloud fraction variability (VCF). These simple criteria are sufficient to characterize the distinctive shape of Cu, which have a limited horizontal extent and highly variable CTH as opposed to Sc, which cover larger areas and have a small and stable geometrical thickness. The DA is utilized on instantaneous profiles of active-sensor Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), specifically the General Circulation Model - Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), and CloudSat-CALIPSO combined observations. By documenting the geographical distribution of Sc and Cu clouds, these datasets make it possible to evaluate the shallow convection (Cu type) and boundary layer (Sc type) clouds in state-of-the art climate models, which are typically generated by distinct parametrizations.本数据集包含两套全球尺度层积云（stratocumulus, Sc）与积云（cumulus, Cu）的空间分布及廓线特征数据。为此，我们设计了一种原创性判别算法（discrimination algorithm, DA），可基于三项可观测云属性对层积云和积云进行区分：云顶高度（cloud top height, CTH）、云水平覆盖率（horizontal cloud fraction, HCF）以及云垂直覆盖率变率（vertical cloud fraction variability, VCF）。上述简洁判据足以刻画积云的独特形态：积云水平覆盖范围有限且云顶高度变率极高；与之相对，层积云覆盖范围更广，几何厚度较小且保持稳定。该判别算法被应用于主动传感器云-气溶胶激光雷达与红外探测卫星观测计划（Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations, CALIPSO）的瞬时廓线数据，具体涵盖面向通用环流模式的CALIPSO云产品（General Circulation Model - Oriented CALIPSO Cloud Product, CALIPSO-GOCCP）以及CloudSat与CALIPSO联合观测数据集。通过记录层积云和积云的地理分布，本数据集可用于评估当前顶尖气候模式中的浅对流（积云型）与边界层（层积云型）云过程——这类云过程通常由不同的参数化方案生成。