首页> 外文期刊>Journal of Climate >Evaluation of a statistical model of cloud vertical structure using combined CloudSat and CALIPSO cloud layer profiles.
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Evaluation of a statistical model of cloud vertical structure using combined CloudSat and CALIPSO cloud layer profiles.

机译:结合使用 CloudSat 和CALIPSO云层配置文件评估云垂直结构的统计模型。

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摘要

A model of the three-dimensional distribution of clouds was developed from the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and the statistics of cloud vertical structure (CVS) from an analysis of radiosonde humidity profiles. The CVS model associates each cloud type, defined by cloud-top pressure of the topmost cloud layer and total column optical thickness, with a particular CVS. The advent of satellite cloud radar (CloudSat) and lidar [Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements (together C&C) of CVS allows for a quantitative evaluation of this statistical model. The zonal monthly-mean cloud layer distribution from the ISCCP CVS agrees with that from C&C to within 10% (when normalized to the same total cloud amount). The largest differences are an overestimate of middle-level cloudiness in winter polar regions, an overestimate of cloud-top pressures of the highest-level clouds, especially in the tropics, and an underestimate of low-level cloud amounts over southern midlatitude oceans. A more severe test of the hypothesized relationship is made by comparing CVS for individual satellite pixels. The agreement of CVS is good for isolated low-level clouds and reasonably good when the uppermost cloud layer is a high-level cloud; however, the agreement is not good when the uppermost cloud layer is a middle-level cloud, even when ISCCP correctly locates cloud top. An improved CVS model combining C&C and ISCCP may require classification at spatial scales larger than individual satellite pixels.
机译:根据国际卫星云气候学项目(ISCCP)的云层出现统计数据和对探空仪湿度剖面分析的云垂直结构(CVS)统计数据,开发了云的三维分布模型。 CVS模型将每种云类型(由最顶层云层的云顶压力和总色谱柱光学厚度定义)与特定CVS关联。 CVS卫星云雷达( CloudSat )和激光雷达[Cloud-Aerosol激光雷达和红外探路者卫星观测(CALIPSO)]测量技术(统称为C&C)的出现允许对该统计模型进行定量评估。 ISCCP CVS的区域平均月云层分布与C&C的分布一致,在10%以内(当归一化为相同的总云量时)。最大的差异是对冬季极地地区中层云度的高估,对最高层云(尤其是在热带地区)的云层顶压力的高估以及对中纬度南部南部低层云量的低估。通过比较单个卫星像素的CVS,对假设关系进行了更严格的检验。 CVS的协议适用于隔离的低层云,而最上层的云层是高层云则相当合适;但是,即使最上层的云层是中层云,即使ISCCP正确地位于云顶,该协议也不是很好。结合了C&C和ISCCP的改进的CVS模型可能需要在比单个卫星像素更大的空间尺度上进行分类。

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