Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data
Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data
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DOI：
10.1002/2013jd020224
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
M. Christensen;G. Stephens;M. Lebsock
中科院分区：
文献类型：
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作者：
M. Christensen;G. Stephens;M. Lebsock
这项研究提供了从CloudSat，MODIS（中分辨率成像光谱仪），云-气溶胶激光雷达和红外探路者卫星观测检索的低云属性的评估，目的是揭露偏见，阻碍与全球气候模型（GCM）中模拟的云属性进行有意义的比较。CloudSat与大气环流模式比较有关，是唯一能够从空间提供云水和冰含量垂直结构的卫星。CloudSat低云属性的偏差被发现与涉及云检测和与降水和严格的云筛选程序相关的算法检索失败的问题有关。我们发现，MODIS和CloudSat云液态水路径（LWP）数据同意仔细筛选缺乏降水，但显着离开降水云由于雨水污染的CloudSat检索算法中的LWP。毛毛雨和雨的存在（约20%的时间发生）与不同的平均LWP，平均粒子大小和光学厚度的所有低云，因此海洋低云的辐射特性。LWP偏差的另一个更重要的来源是明显缺乏云检测。平均而言，云廓线雷达在大约45%的暖云中错过了具有足够的液体和冰水反演的云，大部分偏差发生在所谓的“地面杂波区”中1公里以下的云中。通过结合额外的传感器，如中分辨率成像光谱仪，以下结果表明，这种LWP偏差可以大大减少。
This study provides an assessment of low cloud properties retrieved from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation with the goal of exposing biases that hinder meaningful comparisons with the simulated cloud properties in global climate models (GCMs). Being pertinent to GCM comparisons, CloudSat is the only satellite that can provide the vertical structure of cloud water and ice content from space. Biases in CloudSat low cloud properties are found to be tied to problems involving cloud detection and algorithm retrieval failures related to precipitation and strict cloud screening procedures. We show that MODIS and CloudSat cloud liquid water path (LWP) data agree when carefully screened for lack of precipitation but significantly depart in precipitating clouds due to rain water contamination of LWP in the CloudSat retrieval algorithm. The presence of drizzle and rain (occurring about 20% of the time) is associated with different mean LWP, mean particle sizes, and optical depths of all low clouds and therefore the radiative properties of the oceanic low clouds. Another more significant source of the LWP bias stems from the apparent lack of cloud detection. On average, the Cloud Profiling Radar misses clouds with adequate liquid and ice water retrievals as detected by MODIS in approximately 45% of warm clouds with the bulk of the bias occurring in clouds below 1 km in the so‐called “ground clutter zone.” By incorporating additional sensors such as MODIS, the following results suggest that this LWP bias can be greatly reduced.