# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 1 Introduction  
Aircraft icing is a dangerous threat that results in many accidents which can cause fatalities and financial losses [1; 2]. It is a phenomenon in which supercooled droplets (SCDs) collide with a hard surface forming an ice film. Clouds often contain SCDs, but icing occurs when there is a high density of SCDs [3; 4; 5]. When icing forms on aircraft bodies and wings, the aircraft's balance is disturbed, resulting in a loss of control. For this reason, detecting and avoiding potential icing areas is crucial for aviation safety. The detection of SCDs, especially in the freezing phase of rain, is usually conducted using a thresholding approach based on the subfreezing temperature range and high relative humidity [6]. Research efforts have been made to identify icing regions using ground or airborne observations and human reporting systems such as Tropospheric Airborne Meteorological Data Reporting (TAMDAR) and pilot reports (PIREPs). Such systems are designed to warn other flights by recordingthe time and location with detailed atmospheric information when airplanes are crossing dangerous areas [7]. Although observation data only provide limited spatiotemporal information on icing, they have been used as validation data in many icing-related studies [8; 9; 10]. Numerical forecasting models have also been used to estimate potential icing regions. For instance, high resolution numerical weather prediction model results have been used to calculate temperature (T), relative humidity (RH), vertical velocity (VV), and supercooled liquid water (SLW) as input parameters in potential icing calculation algorithms [11; 10]. However, numerical models often provide inaccurate results [12], which increase the uncertainty of potential icing clouds identified by the icing algorithms [13]. Geostationary satellite sensors can be an effective alternative because they collect data over wide areas with high temporal frequency (-minutes). SLW, which potentially results in icing, tends to form near cloud tops where the air temperatures typically range from freezing temperature to \(-30\)\({}^{\circ}\)C [14]. Thus, satellite observations over cloud tops can provide valuable information on icing [15]. In particular, satellite-derived data are greatly suitable for icing research due to the fact that icing intensity is closely related to several meteorological factors such as cloud temperature, thickness, phase, and distribution, which can be effectively derived from satellite images [15; 16; 17]. With an increasing interest in the synergistic use of satellite and ground observations for icing detection, many agencies and research groups have developed operational systems for icing detection, including the Flight Ling Threat (FIT) [15], and the National Aeronautics and Space Administration (NASA) Icing Remote Sensing System (NIRSS) [18]. The Alliance Icing Research Study (AIRS) investigated the climatological characteristics of icing with varying intensities over Canada, and compared meteorological data between aircraft measurements and ground-based remote sensing data, such as doppler radar, aircraft weather radar, light detection and ranging (LIDAR), infra-red (IR) and visible sensors [19]. The IR brightness temperature data derived from advanced very high resolution radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites were used for icing detection with temperature and relative humidity (T/RH) derived from numerical models, which resulted in a probability of detection (POD) of up to 70% for moderate-or-greater icing [17]. [PERSON] et al. [16] examined the feasibility of the cloud parameters produced from Geostationary Operational Environmental Satellite (GOES) satellite data as input factors in an icing detection algorithm. They suggested a probability-based algorithm with an empirical equation focusing on the liquid water path (LWP) and evaluated it with PIREPs resulting in a POD of 54.5%. [PERSON] et al.