Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds
Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds
复制标题
DOI：
10.1029/2009jd012346
复制
发表时间：
2010-07-21
影响因子：
4.4
通讯作者：
Hogan, Robin J.
中科院分区：
文献类型：
--
作者：
Delanoe, Julien;Hogan, Robin J.
本文将星载雷达、激光雷达和卫星“a - train”上红外辐射计的数据结合在变分算法中检索冰云特性。该方法允许在雷达和激光雷达对探测到云的区域敏感的区域之间进行无缝检索。我们首先实现了一种云相识别方法，包括利用激光雷达信号和温度识别过冷水层，以区分冰和液体。我们还对变分方案中指定的误差进行了严格的计算。我们通过评估云属性检索中面积-直径和密度-直径关系变化的影响来估计当辐射未被同化时微物理假设对算法的影响。我们发现，这些假设的变化对雷达和激光雷达检索的影响大于雷达-激光雷达检索，尽管激光雷达的消光检索仅受到微弱影响。我们还表明，当冰云足够薄，激光雷达信号可以完全穿透时，利用云外的分子激光雷达信号作为光学深度的限制，可以改善检索到的消光。当红外辐射可用时，它们提供了一个额外的约束，并允许消光与后向散射比随高度线性变化，而不是恒定的，这改善了检索云属性的垂直分布。
In this paper, data from spaceborne radar, lidar and infrared radiometers on the "A-Train" of satellites are combined in a variational algorithm to retrieve ice cloud properties. The method allows a seamless retrieval between regions where both radar and lidar are sensitive to the regions where one detects the cloud. We first implement a cloud phase identification method, including identification of supercooled water layers using the lidar signal and temperature to discriminate ice from liquid. We also include rigorous calculation of errors assigned in the variational scheme. We estimate the impact of the microphysical assumptions on the algorithm when radiances are not assimilated by evaluating the impact of the change in the area-diameter and the density-diameter relationships in the retrieval of cloud properties. We show that changes to these assumptions affect the radar-only and lidar-only retrieval more than the radar-lidar retrieval, although the lidar-only extinction retrieval is only weakly affected. We also show that making use of the molecular lidar signal beyond the cloud as a constraint on optical depth, when ice clouds are sufficiently thin to allow the lidar signal to penetrate them entirely, improves the retrieved extinction. When infrared radiances are available, they provide an extra constraint and allow the extinction-to-backscatter ratio to vary linearly with height instead of being constant, which improves the vertical distribution of retrieved cloud properties.