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MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP

机译:MODIS Collection 6短波派生的云相位分类算法及其与CALIOP的比较

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Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
机译:云热力学阶段(冰,液,不确定)分类是从被动传感器(例如MODIS(中等分辨率成像光谱仪))进行云检索的重要的第一步。由于冰和液相云的散射和吸收特性非常不同,因此错误的云相决定可能会导致云光学和微物理性质乘积的重大错误,例如云光学厚度或有效粒子半径。此外,众所周知,冰云和液态云对地球的能源收支和水文循环有不同的影响,因此准确监测这些云的时空分布仍然具有重要意义。对于MODIS Collection 6(C6),光学和微物理属性检索所使用的短波云热力学相位算法已被完全重写,以提高各种阴天场景(例如,海洋上的薄云/厚云)的相位识别能力。 / land / desert / snow / ice surface等)。为了评估C6云阶段算法的性能,已与传统C5算法和CALIOP进行了广泛的颗粒级和全局比较。与C5相比,C6出现了全面的改善。

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