## 2 Data Description
### Training and Testing Data
To utilize the potential context information provided by the adjacent pixels (i.e., yellow pixels in Figure 1), the \"2D\" input data structure includes predictor variables from five VIIRS pixels (one on-track and two adjacent pixels) to predict the corresponding dust or non-dust classification from CALIOP corresponding to the center on-track pixel. In terms of predictor variable selection, all 23 predictor variables in Table 2 are used in the baseline input data structure (referred to as \"allVar\"). As explained later in Section 3.6, we also select a subset of nine predictor variables based on the feature importance analysis of the RF method, which include five M-bands and four solar-viewing angles (marked in bold in Table 2, and referred to as \"selectVar\"). Based on the different combination of \"0-D\" vs. \"2D\" and \"allVar\" vs. \"selectVar\", four sets of input data structures are constructed to train all 5 ML methods. The results will be evaluated in Section 4. To train and test the ML based dust detection models, the collocated data are split to training and testing data with a 10-day interval. That is, data are designated to be testing data for every 10 days, and, otherwise, training data. As such, the training and testing data accounts for about 89% and 11% of the total data, respectively (i.e., all cloud-free pixels). Furthermore, because the optical characteristics are very different between land and the ocean, separate models are developed and trained for land and ocean. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Predictor Variables** \\ \hline Radiances from VIIRS M-bands (16) & \\  & M01 (0.412 μm), M02 (0.445 μm), M03 (0.488 μm), _M04_ (0.555 μm), \\  & M05 (0.672 μm), M06 (0.746 μm), M07 (0.865 μm), _M08_ (1.240 μm), \\ (band center in μm) & _M09_ (1.378 μm), _M10_ (_1.61 μm), _M11_ (2.25 μm), _M12_ (3.7 μm), \\  & M13 (4.05 μm), _M14_ (_8.55 μm), M15 (10.763 μm), _M16_ (**12.01 μm**) \\ \hline  & **Solar Azimuth Angle (SAA),** \\  & **Solar Zenith Angle (SZA),** \\  & Viewing Azimuth Angle (VAA), \\  & Viewing Zenith Angle (VZA) \\ \hline  & Day of Year (1–365), \\ Observation Information (3) & **Latitude,** \\  & **Longitude** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictor variables used in the study based on VIIRS measurement. Variables marked in bold represent the top nine features selected by RF, and variables marked in italic represent the top nine features selected by FFNN. Statistics of training and testing data are depicted in Figure 2a,b. In the training data, dust pixels account for about 57% and 15% of all cloud-free pixels over land and ocean, respectively, which is expected because dust aerosols all originate in land and are only transported over to the ocean. Evidently, the ratios of dust to cloud-free pixels in the testing data (Figure 2b) are nearly identical to those in the testing data. Figure 2c,d show the global dust frequencies derived based on the training and testing data, respectively. The dust frequency is defined as the ratio of dusty pixels to the total cloud-free pixels within a 5 \(\times\) 5 latitude-longitude grid. Over land, high dust frequencies are observed in those well-known dust laden regions, such as Sahara, central Asian, and Australia. A relatively high dust frequency is also observed in the tropical north Atlantic region in the training data (Figure 2c), which is expected because this is the outflow region of Sahara dust. On the other hand, some elevated dust frequency (>40%) is also observed over the Southern Ocean and the Antarctica continent. Although the dust from the Patagonian and Australian deserts can be transported to Southern Ocean, these transported dust events are likely to be intermittent and relatively rare in comparison with the dust events in the tropical north Atlantic. Therefore, the high dust frequency in these regions are likely to be the retrieval artifacts of CALIOP.