# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 1 Introduction
[10] proposed an icing detection algorithm that uses cloud effective radius (CER), cloud optical thickness (COT), and LWP products generated from GOES satellite data. The algorithm was based on a thresholding approach where the thresholds were determined through statistical analyses using a huge amount of in situ data over the contiguous United States (CONUS) region. They combined a fuzzy logic membership function and a human-derived decision tree to determine the thresholds rather than using simple physical thresholds such as those used in the T/RH scheme. The proposed icing detection algorithm was successfully validated using four hindcast icing cases. [PERSON] et al. [15] improved the icing detection model proposed by [PERSON] et al. [10], suggesting an operational NOAA FIT algorithm. However, the algorithm produced a high false alarm rate in detecting potential icing areas due to the different scales between satellite and reference data, because icing often occurs in a smaller region than the pixel size (i.e., 4 km by 4 km) of the satellite data used [15]. The thresholding approach based on the physical characteristics of icing has some limitations. Above all, the determination of thresholds for meteorological factors tends to be subjective, depending on experts [20]. Moreover, several factors in a physical icing algorithm come from model outputs (e.g., T, RH, and VV), which implies that the algorithm strongly depends on the accuracy of the corresponding physical model [10]. Although many studies have been conducted to detect and monitor satellite-based flight icing [15; 21], they have focused on the areas over North America and Europe. There has been minimal exploration of satellite-based flight icing detection in Asia, even though several meteorological satellite sensor systems are in operation, such as the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AMI). The Korea Meteorological Administration (KMA) proposed an icing detection system using COMS MI over East Asia [22]. The KMA algorithm produced a high false alarm rate when compared to PIREPs because it is a composite model of thresholding approaches based solely on the physical theory of icing [22]. This implies that the physical properties of in-flight potential icing regions might vary by location and adaptive thresholding approaches may need to be considered in order for a model to work with different areas and data. A robust icing detection model can be proposed using a number of reference data for calibration and validation. Thus, it is necessary to evaluate icing detection models using long term data covering more than a few years, unlike the existing studies which have mostly focused on the evaluation of a short period of time (e.g., a few months or specific seasons) [10; 15; 16; 17; 19; 22]. Although several satellite-derived variables such as COT and CER are related to icing, each variable has its own uncertainty and the relationship between the variables and icing might not be robustly determined through a simple thresholding approach, which implies that more advanced approaches capable of handling non-linear behaviors are necessary for icing detection. Recently, machine learning approaches have been introduced in many classification and regression tasks using satellite remote sensing [23; 24; 25; 26; 27; 28]. Unlike typical statistical approaches, machine learning is generally free from data assumptions and has been proven to be effective in modeling non-linear behaviors. For meteorological applications, machine learning has been applied to detect phenomena that pose a risk to aviation, such as overshooting tops and cloud convective initiation [29; 30; 31].