## 2 Data Description
### Training and Testing Data  
Tables 1 and 2 show the target and predictor variables used for the training of ML methods in this study. As aforementioned, our objective is to detect dust in cloud-free conditions. Thus, the first step is to separate the collocated pixels into two groups: cloudy (where CALIOP detects one more cloud layer) and cloud-free (where CALIOP detects no cloud). We exclude all cloudy pixels in our study (both training and testing). On the one hand, the exclusion of cloudy pixels makes the training process less challenging. On the other hand, however, it leads to a couple of limitations. First, because our dust detection algorithm is only applicable to cloud-free conditions, the accuracy of dust detection is partially dependent on the accuracy of cloud mask. The cloud mask errors, for example misidentification of dust as cloud (or vice versa), can in turn affect our dust detection result. Second, it is known from previous studies that dust aerosols can often be found above and/or below clouds [40]. These coexistent conditions of dust and cloud are excluded from the training for simplicity. When applied to real observations, a reliable cloud mask algorithm is expected to label most such pixels as cloudy. After the cloud screening, we then separate all the cloud-free pixels into two groups, \"dust\" or \"non-dust\", as the predictor for the training (see Table 2). A cloud-free collocated pixel is considered to be dust when one or more dust or dust mixture layers are detected by CALIOP in the whole atmospheric column. In contrast, a cloud-free collocated pixel is considered to be non-dust when no dust or dust mixture layer is detected. It should be noted that, in some cases, a dust layer may be located above or below another type of aerosol (i.e., separate and not mixed). Such pixels would be labeled as dust in our study regardless of whether the dust layer is the dominant type of aerosols in the column in terms of aerosol optical depth. In other words, our classification of dust vs. non-dust is designed to maximally preserve the dust detection by CALIOP. Finally, it should be noted that, in some relatively rare cases, CALIOP does not detect any aerosol layer in the column. Such pixels are also labeled as non-dust. Figure 1: Illustration of on-track, off-track, and adjacent VIIRS pixels as well as CALIOP track. The target variables for the training are summarized in Table 2. A total of 23 predictor variables from VIIRS will be used to perform the binary classification (i.e., dust vs. non-dust) on target variables from CALIOP. The predictor variables include the radiance observations from all the 16 VIIRS M-bands, four variables about the solar and viewing geometries, and three variables about the time (date) and location (i.e., latitude and longitude) of the pixel. As mentioned in the Introduction, many previous studies often use only a subset of available radiance observations from the passive sensors for physics-based dust detection [25]. In contrast, we use all the 16 M-bands in this study to maximally preserve and utilize all the information from VIIRS M-bands, which is an advantage of the ML methods in comparison with the physics-based methods. The geometric variables are included in the training because the reflection of sunlight by dust aerosols is dependent on solar and viewing geometries. In addition, the transmittance and emission of a dust layer in the thermal infrared region are also dependent on viewing geometry. The time and geo-location of the pixel are also included because dust events are known to be dependent on season and geo-location. For comparison purpose, four input data structures are constructed for the training based on different combinations of pixel and predictor variable selections. In terms of VIIRS pixel section, the \"0-D\" input data structure includes predictor variables from only a single on-track VIIRS pixel (i.e., green pixels in Figure 1) to predict the corresponding dust or non-dust classification from CALIOP.