## 1 Introduction
Spherical particles such as cloud droplets and smoke aerosols usually have near-zero lidar depolarization ratio. In contrast, non-spherical particles, such as ice particles in cirrus clouds and dust aerosols have significantly larger lidar depolarization. Combining the depolarization ratio with other measurements, such as backscattering and color ratio, CALIOP can provide reliable dust detection in situations that are highly challenging for the aforementioned passive algorithm, such as detection of a thin dust layer over a bright desert surface. CALIOP dust detection also suffers from the following important limitations: (1) CALIOP has an extremely small spatial sampling rate in comparison with passive sensors, making the detection of intermittent dust event difficult. (2) The signal-to-noise ratio (SNR) of a single lidar pulse is usually low. As a result, the CALIOP algorithm has to average multiple lidar pulses to enhance the SNR,which further reduces the sampling rate. (3) cirrus clouds and sometimes other types of aerosols (e.g., debris in the smoke) can also have non-zero lidar depolarization ratio which confounds the dust detection; this problem is common at polar regions as the low level icy clouds can be mistaken as dust. For the same reason, sometimes thick dust layers can be mis-classified cirrus clouds, although [PERSON] et al. (2009) such misclassification only account for 0.7% of the \"total [tropospheric] features\". (4) During the daytime, the CALIOP 532 nm channel suffers from solar background contamination leading to nosier retrievals. Despite these limitations, the CALIOP-based algorithm can still provide a reliable dust detection product that are widely used in the dust-related studies. In comparison with other alternative methods, e.g., ground-based AERONET and lidar, a great advantage of CALIOP is its global coverage which makes it an optimal choice for our purpose of global dust detection. In particular, it provides a valuable reference for the validation, evaluation, and improvement of dust detection based on passive satellite sensors on a global scale. Not only can developers compare the statistical climatology of dust from passive sensors with CALIOP result, but they can also collocate the passive observations with CALIOP at the pixel level to make direct comparisons and thereby adjust their algorithms. In fact, CALIPSO is part of the A-Train satellite constellation, which makes the pixel-level collocation with other A-Train sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on Aqua satellite, rather straightforward. In a number of previous studies, collocated CALIOP products have been used to evaluate the physically-based dust detection and retrieval algorithms developed for MODIS and AIRS [17; 23; 24; 25]. It should be noted that the collocated CALIOP products are mainly used as the \"ground-truth\" for validation and evaluation in these studies, but not directly used in the development of the algorithms. The recent advances of artificial intelligence have inspired several attempts to develop dust detection algorithm for passive sensors, in particular MODIS, using machine-learning (ML) or Deep-Learning (DL) methods [26; 27; 28; 29; 30]. These ML algorithms have demonstrated excellent skills. For example, [PERSON] et al. [27]. used three ML methods, Weights of Evidence (WOE), Frequency Ratio (FR), and RF, to train a model to detect dust sources from MODIS satellite image and reported over 80% accuracy rate for all three methods. Although these emerging studies are very encouraging, they also have some limitations. These studies either focused only on certain geographical regions (e.g., only Iran and Asian regions in [27]) or investigated only a few number of cases (e.g., only 31 dust events are studied in [30]). In addition, their training dataset is often based on \"physically-based\" methods from passive sensors. For example, [PERSON] et al.