## 1 Introduction  
Sea surface temperature (SST) is one of the most important variables to understand oceanic and atmospheric phenomena, such as current fronts [1] and west boundary currents [2], air-sea interaction [3, 4], and climate change [5, 6]. Global SSTs derived from satellite-measured radiance have been used for weather forecasting and climate prediction applications. Infrared sensors onboard near-polar orbiting satellites and geostationary satellites can provide global SST fields at relatively high spatial (polar orbiting) and temporal (geostationary) resolution, which makes possible the frequent tracking of the ocean phenomena and their diurnal variations under clear sky conditions. Geostationary satellites, such as the Geostationary Operational Environmental Satellites (GOES) of the National Oceanic and Atmospheric Administration (NOAA), and the Meteosat Second Generation (MSG) of European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), a series of Himawari of Japan Meteorological Agency (JMA), have shown the capacity for accurate monitoring of high-temporal SST images for oceanic research and operations [7, 8, 9]. Since [PERSON] and [PERSON] [10] and [PERSON] et al. [11] proposed an algorithm in the early 1970's for SST retrieval using brightness temperatures (BTs) observed by satellite, several studies on the development of SST algorithms has emerged. Multichannel SST (MCSST) [12; 13; 14; 15] and Nonlinear SST (NLSST) [16; 17; 18; 19] algorithms, which are used for most operational SST retrieval [7; 20], are representative empirical regression algorithms. A variety of algorithms based on MCSST and NLSST were evaluated and adapted as more accurate methods for SST [21; 22]. In addition, as the available channels for SST retrieval are increased, the SST fields have been derived by applying an empirical regression algorithm optimized for each sensor [23]. The regression methods of MCSST and NLSST are still widely used for near-polar orbiting satellites as well as geostationary satellites [20; 21; 22; 23]. As the real-time simulated BTs data from radiative transfer model (RTM) [24; 25] using numerical weather prediction (NWP) model fields are available, the physical algorithms, such as the stochastic [26; 27] or deterministic method [28], have been also developed for operational SST retrieval. The first geostationary satellite of Korea, the \"Communication, Ocean and Meteorological Satellite\" (COMS) of the Korean Meteorological Administration (KMA), was launched on 27 June 2010 and is located at 128.2\({}^{\circ}\) E over the equator, covering the western Pacific Ocean and its marginal seas including the East China Sea, the Yellow Sea, the East Sea (called Sea of Japan), and the South China Sea, as well a part of the eastern Indian Ocean (Figure 1). Figure 1a shows the bathymetry obtained from ETOPO5 database from National Geophysical Data Center (NGDC) of NOAA ([[https://www.ngdc.noaa.gov](https://www.ngdc.noaa.gov)]([https://www.ngdc.noaa.gov](https://www.ngdc.noaa.gov))). The Meteorological Imager (MI) of the COMS has been operationally utilized since 2010. The area covered by COMS/MI is one of the most important regions, because it includes a warm pool of the tropical Indian Ocean and western Pacific Ocean, and the Kuroshio Current as one of the largest western boundary currents. The warm pool, which is characterized by the warmest SSTs, i.e., over the about 28 \({}^{\circ}\)C, and with the most active atmospheric convection, has a strong influence on climate change [29]. The Kuroshio Current has played an important role in global ocean circulation, transporting a large amount of heat from the tropical ocean to the mid latitudes [30]. For this reason, several countries in the Asia-Pacific region have received the COMS/MI data and used them for diverse oceanic and atmospheric purposes. Despite the importance of SST data in this region, COMS/MI-observed SSTs have been limited used so far.