Multi-sensor/band Remote Sensing Approach

This figure, adapted from Saito et al. (2017) and Saito et al. (2019a), summarizes the unique features of our remote sensing techniques for liquid and ice cloud properties based on synergistic spaceborne active–passive sensor observations. A new retrieval technique based on synergistic MODIS and CloudSat observations enables robust optical and microphysical property retrievals for vertically inhomogeneous marine liquid clouds as seen in the upper panels. The corresponding paper was selected as Editor's highlight for Eos. In addition, a physics-based retrieval algorithm based on synergistic CALIOP–IIR observations can infer the fraction of horizontally oriented plates (HOP) in ice clouds on a global scale. These new retrieval methods are computationally efficient and therefore can be applicable to multi-year observations, and can depict a global picture of robust optical and microphysical properties of these clouds.

Synergistic multi-sensor-based cloud–aerosol property retrieval techniques

Clouds play a dominant role in the radiation energy budget on a global scale. They cover roughly 60% of the globe, reflecting solar radiation to space and absorbing/emitting thermal infrared radiation in the atmosphere. Therefore, their radiative impacts depend on their cloud type, geometric properties, and optical–microphysical properties. For example, typical liquid clouds cool the surface by obstructing the sun, and thereby the optical properties, coverage, and lifetime of these clouds are necessary to quantify their radiative effect. However, the latter two characteristics are complicatedly determined by the cloud formation-maintenance-dissipation processes.

On the other hand, ice clouds play a complex role in atmospheric radiation because the optical, geometrical, and microphysical properties of ice clouds could even switch their radiative effects between net cooling and warming effects on the atmosphere. To reliably constrain the net radiative effect of ice clouds, these ice cloud properties with high accuracy are essential. Spaceborne remote sensing techniques play a vital role in understanding the distributions of these cloud properties on a global scale. However, recent remote sensing methods are based on many assumptions, some of which are not always valid for a targeting cloud. For example, liquid clouds are generally vertically inhomogeneous; ice clouds consist of various ice crystal habits, and these facts could lead to systematic biases on the cloud property retrievals based on the single-homogeneous cloud layer assumption. Also, the algorithm for cloud retrievals must be computationally efficient due to the large volume of data provided by spaceborne satellites on a daily basis. We need some breakthroughs to obtain reliable cloud properties computationally efficiently. A primary goal of this research is to obtain robust, more detailed, and bias-free cloud property retrievals that are valuable for cloud microphysics investigations and to provide the global climatology of cloud properties towards reliable radiative energy budget estimations.