Visible and shortwave-infrared spectral characteristics of mixed-phase clouds in typical satellite radiometer channels

Mixed-phase clouds (MPCs) involve complex microphysical and dynamical processes of cloud formation and dissipation, which are crucial for numerical weather prediction and cloud-climate feedback. However, satellite remote sensing of MPC properties is still challenging, and there is seldom MPC result inferred from passive spectral observations. This study examines the spectral characteristics of MPCs in the shortwave-infrared (SWIR) channels over the wavelength of 0.4–2.5 µm, and evaluates the potential of current operational satellite spectroradiometer channels for MPC retrievals. With optical properties of MPCs based on the assumption of uniform mixing of both ice and liquid water particles, the effects of MPC ice optical thickness fraction (IOTF) and effective radius on associated optical properties are analyzed. As expected, results indicate that the MPC optical properties show features for ice and liquid water clouds, and their spectral variations show noticeable differences from those for homogeneous cases. A radiative transfer method is employed to examine the sensitivity of SWIR channels to given MPC cloud water path (CWP) and IOTF. MPCs have unique signal characteristics in the SWIR spectrum. The 0.87-µm channel is most sensitive to CWP. Meanwhile, the 1.61- and 2.13-µm channels are more sensitive to water-dominated MPCs (IOTF approaching 0), and the 2.25-µm channel is sensitive to both water-dominated and ice-dominated MPCs (IOTF approaching 1). Such spectral differences are potentially possible to be used to infer MPC properties based on radiometer observations, which will be investigated in future studies. 摘要 混合相态云对数值天气预报和云-气候反馈研究都至关重要, 但是对其特性的卫星定量反演还存在巨大挑战。本论文利用辐射传输模拟, 研究混合相云在0.4–2.5 µm光谱范围内的光谱特征, 评估当前典型星载被动光谱辐射计观测反演混合相态云的潜力, 研究发现, 混合相态云的光学特性同时呈现出冰云和液态水云的特点, 而它们的光谱变化特征与冰云或液态水云存在一定差别。其中, 0.87-µm通道对CWP最敏感, 1.61- 和2.13-µm 通道对水云占主导的混合云更加敏感, 2.25-µm通道则对所有混合云都有一定的敏感性, 这些光谱差异可用于反演混合云的特性, 这也将是未来研究重点关注的内容.