## 2 Data Description  
### CALIOP and VIIRS Products and Collocation  
For this study, we use one year (i.e., 2014) of global collocated daytime VIIRS and CALIOP data product developed by NASA's Science Investigator-led Processing Systems (SIPS) located at the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison. The SIPS are responsible for processing, reprocessing, production, and general assessments of joint NASA/NOAA Suomi NPP VIIRS Atmosphere Products. The collocated data contain merged aerosol/cloud layer retrievals from CALIOP version 3 (V3) operational products and the level-1b radiance observations from the 16 moderate-resolution bands of VIIRS along with the viewing/illumination geometries (see Table 1). A similar CALIOP-VIIRS collocated dataset has been successfully used in a previous study to develop a machine-learning-based cloud detection and thermodynamic-phase classification algorithm [34]. The theoretical basis of CALIOP feature detection and aerosol classification algorithms and the implementations of the operational products have been described in a series of papers [22; 35; 36; 37; 38]. Here, we only provide a very brief overview of the algorithms and products that are most relevant to this study. In the operational CALIOP retrieval workflow, the algorithm first identifies \"features\" (e.g., aerosol and cloud layers) in the lidar profile, which is also known as vertical feature masking (VFM) [22; 38]. The top and bottom altitudes of the identified feature layers are estimated and the layer-integrated properties (e.g., layer-integrated attenuated backscatter and depolarization ratio) derived. Then, each feature layer is first classified into cloud or aerosol using a cloud-aerosol discrimination (CAD) algorithm [35; 36]. An identified aerosol layer is further classified into several sub-types, e.g., dust, smoke, and marine aerosols [37], followed by quantitative aerosol optical depth retrievals. In the V3 operational algorithm, an aerosol layer is classified as \"pure dust\" when the estimated particulate depolarization ratio is larger than the predefined threshold (e.g., larger than 0.20) [37]. As its name suggests, this sub-type consists primarily mineral dust aerosols. When dust aerosols are transported from the source region, they can be mixed with local pollution, e.g., smoke and pollution. The V3 CALIOP operational product designates a special sub-types of aerosol, namely polluted dust, to distinguish dust mixtures from pure dust. In this study, we include both pure dust and dust mixtures (i.e., polluted dust) in our training dataset to train the machine-leaning based detection algorithm. Although the highest possible spatial resolution of CALIOP retrieval is 333 m, most aerosol retrievals are done at 5 km or coarser resolution based on horizontally averaged lidar signals. As aforementioned, the spatial averaging is to beat down the noises and enhance the signal-to-noise ratio. In this study, we use the standard 5 km aerosol layer products. The VIIRS on the Suomi-NPP is a whiskbroom radiometer by design. It has 22 channels ranging from 0.41 um to 12.01 um. Five of these channels are high-resolution (375 m at nadir) image bands that are primarily used for imaging. The rest sixteen channels serve as moderate-resolution bands (750 m at nadir) or M-bands, which are primarily used for quantitative operational retrievals, including aerosol, cloud, ocean color, and surface temperature, for example. It should be noted here that the spatial resolution of VIIRS M-bands is highest at the center of the scan (i.e., nadir viewing) and gradually decreases with viewing zenith angle down to roughly 1.625 km at scan edge (i.e., most oblique viewing). As a result of the average location of CALIOP tracks within VIIRS scan (see examples in Section 5), the average spatial resolution of VIIRS M-bands in our collocation data are roughly 1 km.