This study investigates the factors that control marine boundary layer cloud albedo measured by the Multiangle Imaging SpectroRadiometer (MISR) over domains of (200 km)². We use three key metrics to investigate domain albedo: cloud fraction, cloud heterogeneity, and cloud morphology. Cloud heterogeneity is quantified at the domain level with a unified heterogeneity index. Cloud morphology is determined from a cloud classification algorithm using an Artificial Neural Network (ANN) to classify each domain into one of four categories: (i) closed‐cell Mesoscale Cellular Convection (MCC); (ii) open‐cell MCC; (iii) disorganized MCC; and (iv) No MCC. These different types of MCC are usefully defined as low clouds of different morphologies. Classifications from the ANN are also combined with the satellite observations of MISR to develop relationships between cloud morphology, domain albedo, cloud fraction, and cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction‐albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low cloud behavior and improving their representation in General Circulation Models.

中文翻译：

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关于确定边界层反照率的因素

这项研究调查了多角度成像光谱辐射仪（MISR）在（200 km）²域上控制海洋边界层云反照率的因素。我们使用三个关键指标来研究域反照率：云分数，云异质性和云形态。云异质性在域级别使用统一的异质性指标进行量化。通过使用人工神经网络（ANN）将云分类算法从云分类算法确定云形态，将每个域分为以下四类之一：（i）闭孔中尺度细胞对流（MCC）；（ii）开孔MCC；（iii）杂乱无章的「我的客户中心」；（iv）没有「我的客户中心」。这些不同类型的MCC有用地定义为不同形态的低云。ANN的分类也与MISR的卫星观测结果相结合，以发展云形态，区域反照率，云分数和云异质性之间的关系。发现云形态在调节这些关系中起着至关重要的作用。发现云分数与反照率之间的关系直接是云形态的函数。还发现域反照率和云异质性之间的关系是MCC类型的函数。我们的结果表明，反照率强烈依赖于云的形态和云的异质性。理解MCC的物理特性和气象控制对于理解低云行为并改善其在一般环流模型中的表示具有重要意义。