毫米波云雷达反演混合层云水含量

Cloud particles in mixed-phase clouds experience complicated three-phase transformations which led to a difficulty in quantizing cloud properties. The liquid water content (LWC) and ice water content (IWC) are important physical parameters to descript ice and liquid mixed-phase clouds state. The distributions of cloud water content (CWC, including LWC and IWC) versus mixed-phase cloud conditions such as temperature are complex and non-linear law, which still remained much uncertainty for the observational constraints. Extensive quantified investigation needs to be carried out to advance our understanding of different phases in mixed-phase clouds to constrain cloud parameterizations and thus helps to reduce uncertainties in cloud feedbacks. Based on the CloudSat W-band 94 GHz cloud profiling radar data and aircraft observation data we have, the CWC of mixed-phase clouds will be retrieved. First of all, liquid droplets and ice particles size distributions in various types of clouds will be studied based on airplane observation data. At the same time, the quantitative law of ice-liquid ratio changing with temperature will be studied based on cloud chamber and aircraft observations results collected from literatures.The research results on cloud particles size distributions will be used as priori values to retrieve CWCs. Then based on the Forward Model which CloudSat Radar-Visible Optical Depth Cloud Water Content Product(2B-CWC-RVOD) has applied, the CWCs in mixed-phase clouds will be retrieved by using cloud radar data and visible optical depth data. The retrieval algorithm here is a modification of the method described in CloudSat 2B-CWC-RVOD by considering different types of cloud droplets size distributions and the non-linear relationship between the ice-liquid ratio and cloud temperature.The research result could be used to study the CWC profiles distribution in different types of clouds. The result also can be used to retrieve other physical parameters of clouds such as relative radius to promote the cloud model parameterizations and help get more information about cloud microphysical parameters in detail. In addition, it will promote the site W-band cloud radar development and improve the usage efficiency of the data.

混合层云粒子存在多相态和相互作用，微物理过程较复杂。云水含量（包括液水含量和冰水含量）对混合层宏微观特征有较大影响，该参量是表述冰水混合状态的重要物理量，因其与混合层温度等条件存在非线性关系而更显复杂，相关研究工作还需要细化开展。本项目拟利用已有的飞机观测资料和同时段过境的CloudSat毫米波云雷达等相关资料开展研究，混合层冰水混合比与云温的非线性量化约束关系以及不同类型云滴谱和冰晶谱研究结果作为正演初值，在正演模式基础上、利用毫米波云雷达资料和光学厚度资料建立混合层云水含量反演方法，并结合收集的各种飞机观测资料，对反演效果进行评价。本项目研究结果可用来研究不同云类混合层云水含量垂直廓线分布特征，也可进一步反演混合层其他相关微物理参量，从而对混合层云微物理过程和辐射特性有更精确的把握，为各种云模式参数化提供依据，同时还可提高毫米波云雷达探测资料的使用效率。

云水含量是研究云微物理特征的重要参量，为弥补飞机入云观测的时空局限性，可利用毫米波云雷达探测资料反演云水含量。本项目在已有的飞机观测资料和配套的卫星和地基云雷达观测资料基础上，开展了混合云层云水含量反演研究。首先，对混合层云滴谱分布特征进行了研究，确定了云滴谱分布参数；其次，建立了混合层冰水混合比的量化方法，在以上工作基础上建立了混合云层云水含量反演方法。此外，开展了对流云和层状云云水含量反演方法敏感性研究，两种不同云类混合层的液水含量和有效半径反演精度受反演参数先验值和误差阈值影响较大。最后，将该反演方法应用到了我国不同区域云微物理参量研究中，反演的青藏高原夏季云水含量均值与配套的飞机观测结果较一致，云水含量反演效果较好。本项目建立的云水含量反演方法为进一步研究云微物理特征提供了很好的量化方法，同时提高了毫米波云雷达探测资料的使用效率。