A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS
A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS
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DOI：
10.1175/2011jamc2646.1
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发表时间：
2011-09-01
影响因子：
3
通讯作者：
Hogan, Robin J.
中科院分区：
文献类型：
--
作者：
Stein, Thorwald H. M.;Delanoe, Julien;Hogan, Robin J.
A-Train卫星星座提供了一种新的能力来测量垂直云层，这导致了比目前可能的关于冰云微物理特性的更详细的信息。一种变分雷达-激光雷达冰云反演算法(VarCloud)利用云卫星雷达与云气溶胶激光雷达和红外线探路者卫星观测(CALIPSO)激光雷达的互补性质，提供从最薄的卷云(只能被激光雷达看到)到最厚的冰云(只能被雷达穿透)的冰水含量、有效半径和消光系数的无缝反演。本文将VarCloud的几个版本与CloudSat标准的纯冰的冰水含量的反演、由雷达反射率和温度推导出的冰水含量的两个经验公式以及从中分辨率成像光谱仪(MODIS)辐射计的垂直综合特性进行了比较。平均而言，检索到的变量通常在系数2的范围内一致，大多数差异可以用不同的微物理假设来解释。例如，冰水含量的比较说明了反演对假定的冰粒形状的敏感性。如果将冰粒模拟为扁球体而不是球体用于雷达散射，则在反射率因子大于0dBz的云中，反演的冰水含量平均减少50%。VarCloud反演的光学厚度平均比MODIS低2倍，这可以用不同的粒子质量和面积假设来解释；如果VarCloud模拟了MODIS的假设，那么有效半径和光学厚度被高估了。然而，MODIS预测，对于相同的反演，平均垂直积分冰水量将比VarCloud的平均冰水量低约3倍，因为MODIS算法假设其反演的有效半径(主要代表云顶)在整个云深度都是恒定的。这些比较突出了在所有检索算法中完善微物理假设的必要性，以及今后不仅比较平均值而且比较全概率密度函数的研究的需要。
The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads 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 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (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 Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to 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 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, 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.