Preliminary comparison of CloudSAT‐derived microphysical quantities with ground‐based measurements for mixed‐phase cloud research in the Arctic
Preliminary comparison of CloudSAT‐derived microphysical quantities with ground‐based measurements for mixed‐phase cloud research in the Arctic
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CloudSAT 衍生的微物理量与北极混合相云研究的地面测量值的初步比较
DOI：
10.1029/2008jd010029
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发表时间：
2008
影响因子：
--
通讯作者：
E. Eloranta
中科院分区：
文献类型：
--
作者：
G. Boer;G. Tripoli;E. Eloranta
[1] 北极纬度地区普遍存在的稀薄混合相云给 CloudSAT 带来了特殊的挑战，因为它试图绘制全球辐射相关的云量图。在这项工作中，对加拿大尤里卡通过地面云雷达和激光雷达以及 CloudSAT 云剖面雷达 (CPR) 同时观测到的北极混合相云的反演云特性进行了比较。通过这些比较，我们评估了2B-CLDCLASS产品识别降水和云类型分配的有效性，以及2B-CWC-RO产品微物理反演的准确性。这些初步比较得出了有关 CloudSAT 检索的以下结果： (1) 云检测算法运行良好，检测到从地面传感器观测到的所有云。 (2) 降水没有被很好地识别，并且经常被错误地标记为云。 (3) 与基于地面的反演和之前对这些云类型的研究相比，CloudSAT 反演的液体和冰粒子数密度被发现高出 1 到 2 个数量级。 (4) CloudSAT 颗粒有效尺寸通常太大，最大的颗粒除外，它们被错误地识别为液体。 (5) 水含量显示两种检索类型之间以及与外部研究的测量值之间的最佳一致性。所有比较均针对原始液体和冰检索以及“复合”检索完成，“复合”检索通过温度相关算法划分液体和冰对测量反射率的贡献。这些有限案例中发现的差异意味着将这些云产品应用于混合阶段云研究需要仔细分析。此外，这些差异有助于突出 CloudSAT 算法中需要改进以完成混合阶段云检索的特定假设。
[1] The omnipresent existence of thin, mixed-phase clouds in northern polar latitudes presents special challenges to CloudSAT as it attempts to map radiatively relevant cloudiness around the globe. In this work, retrieved cloud properties of Arctic mixed-phase clouds observed simultaneously in Eureka, Canada by ground-based cloud radar and lidar, and by the CloudSAT Cloud-Profiling Radar (CPR) are compared. Through these comparisons, we evaluate the efficacy of identification of precipitation and assignment of cloud type by the 2B-CLDCLASS product, as well as the accuracy of microphysical retrievals from the 2B-CWC-RO product. These preliminary comparisons result in the following findings with regard to the CloudSAT retrievals: (1) The cloud detection algorithm worked well, detecting all clouds observed from surface based sensors. (2) Precipitation was not well identified, and was often mislabeled as cloud. (3) Both liquid and ice particle number densities retrieved by CloudSAT are found to be 1 to 2 orders of magnitude too high when compared to surface-based retrievals and previous studies of these cloud types. (4) CloudSAT particle effective sizes are often too large, with the exception of the largest particles, which are misidentified as liquid. (5) Water contents show the best agreement between the two retrieval types, as well as with measured values from outside studies. All comparisons were completed for raw liquid and ice retrievals, as well as for “composite” retrievals that partition liquid and ice contributions to measured reflectivity through a temperature-dependent algorithm. Differences found for these limited cases imply that careful analysis is required for application of these cloud products to mixed-phase cloud research. Furthermore, these differences help highlight specific assumptions within the CloudSAT algorithms that are in need of improvement to complete mixed-phase cloud retrievals.