Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and Atmospheric Lidar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution, and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR Target Classification (AC-TC) is the product created for this purpose by combining the ATLID Target Classification (A-TC) and CPR Target Classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat/CALIPSO synergy, while providing richer target classification using the enhanced capabilities of EarthCARE's instruments: CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds, and ATLID's lidar ratio measurements to objectively discrimination between different aerosol species and optically thin ice clouds. In this paper we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations are used to test the processors generating the target classifications, with results presented using the Halifax scene. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar-lidar synergy helps better detect ice and snow, with ATLID detecting radiatively-important optically thin cirrus and cloud-tops while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of a majority of liquid cloud not detected by the active instruments.

中文翻译：

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EarthCARE 雷达和激光雷达对大气水凝物和气溶胶的分类：A-TC、C-TC 和 AC-TC 产品

摘要。EarthCARE 任务旨在通过使用其有源仪器云剖面雷达 (CPR) 和大气激光雷达 (ATLID) 测量云和气溶胶剖面来探测地球大气层。从大气廓线中正确识别水凝物和气溶胶是反演云、气溶胶和降水特性的重要一步。使用并置雷达和激光雷达测量的协同作用可以消除大气目标性质的模糊性，这种协同作用基于雷达和激光雷达相对于剖面中存在的大气目标的互补光谱响应。这些仪器对粒度分布的不同部分很敏感，并提供独立但重叠的光学和微波波长信息。ATLID 对气溶胶和小云粒敏感，而 CPR 对大冰粒、雪花和雨滴敏感。因此，与单一仪器相比，当并置雷达和激光雷达测量存在时，可以更好地对大气目标进行分类。然后可以识别由两种仪器采样的柱内的云相、降水和气溶胶类型。ATLID-CPR 目标分类 (AC-TC) 是为此目的通过结合 ATLID 目标分类 (A-TC) 和 CPR 目标分类 (C-TC) 而创建的产品。AC-TC 对于随后的云、气溶胶和降水特性的协同反演至关重要。AC-TC 建立在以前使用 CloudSat/CALIPSO 协同作用的目标分类的基础上，同时使用 EarthCARE 仪器的增强功能提供更丰富的目标分类：CPR' s 多普勒速度测量以区分雪和边缘雪与冰云，以及 ATLID 的激光雷达比率测量以客观区分不同的气溶胶种类和光学薄冰云。在本文中，我们首先描述单仪器 A-TC 和 C-TC 产品是如何从 ATLID 和 CPR 测量中导出的。然后提出了使用协同决策矩阵将 A-TC 和 C-TC 分类结合起来的 AC-TC 产品。模拟的 EarthCARE 观测用于测试生成目标分类的处理器，结果使用哈利法克斯场景呈现。最后，通过量化大气中冰雪、液态云、雨和气溶胶的分数来评估目标分类，这些分数可以被每个仪器及其协同作用成功识别。我们表明，雷达-激光雷达协同作用有助于更好地探测冰雪，ATLID 探测辐射重要的光学薄卷云和云顶，而 CPR 穿透最深和高度集中的冰云。雨和毛毛雨的探测完全归功于C-TC，而液态云和气溶胶的探测则归功于A-TC。评估还表明，当仪器被消光 (ATLID) 或表面杂波和多次散射 (CPR) 遮挡时，可以做出简单的假设来进行补偿；这允许回收大部分未被有源仪器检测到的液体云。雨和毛毛雨的探测完全归功于C-TC，而液态云和气溶胶的探测则归功于A-TC。评估还表明，当仪器被消光 (ATLID) 或表面杂波和多次散射 (CPR) 遮挡时，可以做出简单的假设来进行补偿；这允许回收大部分未被有源仪器检测到的液体云。雨和毛毛雨的探测完全归功于C-TC，而液态云和气溶胶的探测则归功于A-TC。评估还表明，当仪器被消光 (ATLID) 或表面杂波和多次散射 (CPR) 遮挡时，可以做出简单的假设来进行补偿；这允许回收大部分未被有源仪器检测到的液体云。