A comparison between four different retrieval methods for ice cloud properties using data from the CloudSat, CALIPSO, and MODIS satellites
A comparison between four different retrieval methods for ice cloud properties using data from the CloudSat, CALIPSO, and MODIS satellites
复制标题
使用 CloudSat、CALIPSO 和 MODIS 卫星数据对冰云属性的四种不同反演方法进行比较
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
T. Stein
中科院分区：
文献类型：
--
作者：
T. Stein
A-Train 卫星星座提供了测量垂直云剖面的新功能，从而获得比迄今为止更详细的冰云微物理特性信息。变分雷达激光雷达冰云反演算法 VarCloud 利用 CloudSat 雷达和 CALIPSO 激光雷达的互补性，提供从最薄的卷云（仅由激光雷达看到）到最厚的冰云（仅由雷达穿透）的冰水含量、有效半径和消光系数的无缝反演。在本文中，VarCloud 反演的几个版本与 CloudSat 标准仅冰水含量反演、从雷达反射率和温度导出冰水含量的两个经验公式以及从 MODIS 辐射计反演垂直综合属性进行了比较。通常，检索到的变量平均一致在 2 倍以内，并且大多数差异可以通过不同的微观物理假设来解释。例如，冰水含量比较说明了检索对假定冰粒形状的敏感性。如果将冰粒子建模为扁球体而不是雷达散射的球体，则反射率因子大于 0 dBZ 的云中检索到的冰水含量平均减少 35%。 MODIS 和 VarCloud 光学深度之间平均 2 倍的差异可以通过对粒子质量和面积的不同假设来解释；如果 VarCloud 模仿 MODIS 假设，那么光学深度和有效半径都会有更好的一致性。然而，对于相同的反演，MODIS 预测平均垂直积分冰水含量比 VarCloud 低 3 倍左右，因为 MODIS 算法假设其反演的有效半径（主要代表云顶）在整个云层深度是恒定的。这些比较强调需要在所有检索算法中完善微观物理假设，并且未来的研究不仅要比较平均值，还要比较完整的概率密度函数。
The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles leading to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar-lidar ice-cloud retrieval algorithm, VarCloud, takes advantage of the complementary nature of the CloudSat radar and CALIPSO lidar to provide a seamless retrieval of ice water content, effective radius and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the MODIS radiometer. Typically the retrieved variables agree within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 35% in clouds with a reflectivity factor larger than 0 dBZ. The factor-of-2 difference between MODIS and VarCloud optical depth, on average, can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in both optical depth and effective radius. However, MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than VarCloud for the same retrievals, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms, and also for future studies to compare not only the mean values but also the full probability density function.
影响因子：
3
作者：
A. Protat;J. Delanoë;D. Bouniol;A. Heymsfield;A. Bansemer;P. Brown
通讯作者：
A. Protat;J. Delanoë;D. Bouniol;A. Heymsfield;A. Bansemer;P. Brown
影响因子：
4.4
作者：
Delanoe, Julien;Hogan, Robin J.
通讯作者：
Hogan, Robin J.
影响因子：
4.4
作者：
Delanoe, Julien;Hogan, Robin J.
通讯作者：
Hogan, Robin J.