## 1 Introduction  
Mineral dust aerosols (hereafter dust for short) usually originate from the desert regions and can be transported to almost any part of the world [1, 2]. They have an important role in Earth's climate system through their influences on the radiative energy budget of Earth, the microphysics and lifetime of clouds [3], and the terrestrial and marine ecosystems. Dust aerosols can also influence the air quality, which further impact public health. Dust aerosols have been linked to respiratory illnesses, such as asthma, meningitis, and others. Fungi, bacteria, and even some viruses can travel on aerosol particles for miles, causing the spread of diseases and other ailments [4; 5]. A reliable dust detection is the first step to achieve a better understanding of dust climatic effects and to track dust event for air quality purpose, although there are many ground-based networks to monitor dust and other types of aerosols. Satellite-based remote sensing is the only means to detect dust aerosols on a regional to global scale. Satellite-based dust detection algorithms were first developed for passive observations. Most of these algorithms are so-called \"physically-based\". They rely on the physical intuitions of the developers to identify the radiative signatures (e.g., reflectance, color, and brightness temperature) of dust aerosols in a passive satellite image that are connected to the physical properties of dust (e.g., composition, size, shape, and temperature). For example, dust has significant absorption in the ultraviolet (UV). In the visible region, dust is usually brighter than the dark ocean and also has a distinct color. In the infrared, dust can reduce the brightness temperature of the scene and has a unique spectral signature [6]. These radiative signatures of dust have been used independently or in combination in the previous studies to detect dust in a passive satellite image [7; 8; 9; 10; 11; 12]. Validation of these dust detection algorithms and evaluation of their uncertainties exposed several common problems of these physical-based algorithms: first, the development of these physical-based algorithms is often based on a handful of cases due to the slow learning process of human. Second, the empirical thresholds that are commonly used in these algorithms to distinguish dust from the environment are often too rigid to fit miscellaneous situations. Third, at certain conditions, land surface, clouds, and other types of aerosols may have similar radiative signatures as dust, which could confound the detection algorithm [13; 14; 15]. As a result of these problems, physically-based dust detection from passive satellite observations often misses dust layers that are either too thick or too thin, miss-identifies clouds as dust, and misses dust over the desert regions. Finally, but not least, many physically-based algorithms only utilize a small fraction of the observations available from the passive sensors because the rest of the observations are considered to have little information content of dust. For example, [PERSON] et al. [15] developed a physically-based algorithm to detect dust from MODIS and used 14 out of 36 MODIS bands. Although these bands can provide sufficient information content for dust detection, it is hard to tell how much information can be added if including the observations from other bands [16; 17; 18; 19; 20]. The launch of the the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006 provided an unprecedented opportunity for satellite-based dust detection. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO satellite has two channels at 532 nm and 1064 nm. As an active lidar, CALIOP can resolve the vertical distribution of aerosol and cloud accurately, which is extremely difficult, if not impossible, for most passive sensors [21; 22]. CALIOP also has polarization capability in its 532 nm channel, which enables it to easily identify the shapes (spherical vs. non-spherical) of cloud and aerosol particles.