# Validation of Sea Surface Temperature From GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle

Kyung-Ae Park, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eunha Sohn, Huitae Joo, Joon-Soo Lee, Joon-Yong Yang

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Kyung-Ae Park, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eunha Sohn, et al.. Validation of Sea Surface Temperature From GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, pp.12024 - 12041. 10.1109/jstars.2025.3557003. hal-05443185

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# Validation of Sea Surface Temperature from GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle

Kyung-Ae Park, Member, IEEE, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eun-Ha Sohn, Hui-Tae Joo, Joon-Soo Lee, Jun-Yong Yang

**Abstract**— This study evaluates the accuracy of sea surface temperature (SST) data produced by Korea's second geostationary satellite, GK-2A, over its first four years of operation (2019-2023). The root mean square difference (RMSD) and bias between satellite-derived SSTs and in situ measurements were approximately 0.5K and -0.04K, respectively, satisfying the target accuracy. Errors exhibited latitude-dependent variations and differed between day and night observations. SST was found to be overestimated in conditions of weak wind (<3 m/s) and underestimated during nighttime. Additionally, SST estimates were higher in conditions of high atmospheric humidity and lower during dry conditions. In regions with developed oceanic fronts, the intensity of the fronts linearly increased the RMSD of the satellite SST. A notable increase in SST uncertainty was observed when the satellite zenith angle (SZA) exceeded 50°. To address this, eight additional SST estimation formulas considering both linear and nonlinear relationships related to the SZA were developed and validated. The newly derived formula reduced the influence of the SZA, especially at high SZAs. The study emphasizes the critical role of the SZA in contributing to SST errors and highlights the need for continuous verification and improvement of SST accuracy before its utilization. By proposing optimal algorithms for SST retrievals from geostationary satellites, this study is anticipated to improve the monitoring of SSTs in regions covered by GK-2A. Given the high frequency of observations from geostationary satellites, findings highlight the necessity of optimizing zenith angle corrections in geostationary satellite SST operations for reliable global SST applications.

**Index Terms**— GK-2A, Satellite zenith angle, Sea surface temperature, Uncertainty, Validation

## I. INTRODUCTION

SEA surface temperature (SST) is one of the most crucial marine environmental variables used in analyzing the spatiotemporal variability of oceanic and atmospheric

This study was supported by the National Research Foundation of Korea with funding from the government (Ministry of Science and ICT) (No. RS-2023-00208935). This research was partly conducted with support by the Korea Coastal Ocean Change Monitoring and Ecosystem Characteristics Research of National Institute of Fisheries Science (R2025014). H.J. Woo was partly supported the "Development of technology using analysis of ocean satellite images (20210046)" funded by the Korea Institute of Marine Science & Technology Promotion (KIMST). (Corresponding author: Hye-Jin Woo).

Kyung-Ae Park is with the Department of Earth Science Education/Center for Education Research, Seoul National University, Seoul, 08826, Korea (e-mail: kapark@snu.ac.kr).

Hye-Jin Woo is with the Department of Earth Science Education, Seoul National University, Seoul, 08826, Korea (e-mail: hyejinwoo@snu.ac.kr).

phenomena [1]. Satellite-based SST retrievals have significantly advanced our capability to monitor the ocean surface and understand physical process as a key oceanic variable [2]. In addition, SST has been extensively used in real-time applications for assimilation into ocean circulation models and atmospheric numerical prediction models (e.g., [3]-[8]). Satellite-based SST data obtained through synoptic observations have been actively utilized in many valuable studies to analyze ocean fronts, eddies, atmosphere-ocean interactions, long-term variability trends, marine heat wave, climate changes, and to understand the dynamic processes of the ocean surface over several decades (e.g., [9]-[15]).

Geostationary satellites, despite their lower spatial resolution compared to their near-polar orbit satellites, have seen significant improvements offering 2 km spatial resolution and high-frequency observations every 10 minutes, allowing the detection of short-term oceanic variations. Geostationary satellites like NOAA's GOES-R, EUMETSAT's Meteosat Second Generation (MSG), and JMA's Himawari-8 have become integral to SST observation, widely used in various marine research and real-time operational forecasting. By using real-time polar-orbit and geostationary satellite data, daily SST composites have been produced on a global scale and distributed through several analyses, such as Operational SST and Sea Ice Analysis (OSTIA) [5], [16], Optimum Interpolation SST (OISST) analysis [17], Multi-Scale Ultra-high Resolution SST (MURSST) [18], ESA Climate Change Initiative (CCI) SST [5], [15], [19], Canadian Meteorological Centre (CMC) analysis, Remote Sensing System (REMSS) analysis, Merged satellite and in situ data Global Daily SST (MGDSST) analysis, and Geo-Polar Blended SST (Blended SST) product [20], underpinning their significance in the field of oceanographic and meteorological studies. These SST products reflect the

Stephane Saux Picart is with the Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Lannion, France (e-mail: stephane.sauxpicart@meteo.fr).

Anne O'Carroll is with the EUMETSAT, Darmstadt, 64295, Germany (e-mail: Anne.Ocarroll@eumetsat.int).

Eun-Ha Sohn is with the National Meteorological Satellite Center, Korea Meteorological Administration, Chungcheongbuk-do 27803, Korea (e-mail: soneh0431@kma.go.kr).

Hui-Tae Joo, Joon-Soo Lee, and Jun-Yong Yang are with the National Institute of Fisheries Science, Busan 46083, Korea (e-mail: huitae@korea.kr; leejoonsoo@korea.kr; yangjy1020@korea.kr).
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comprehensive efforts and continuous innovation to accurately
monitor and understand spatial and temporal variability of
oceanic phenomena.

Since the 1970s, the advent of infrared sensors has allowed for the observation of brightness temperature (BT), which has enabled the calculation of SST from satellites using multi-channel algorithms. Initial efforts by Anding and Kauth [21], followed by Prabhakara et al. [22], laid the groundwork for these techniques. The National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High-Resolution Radiometer (AVHRR) became a pivotal sensor for deriving SST, utilizing methods like Multi-Channel SST (MCSST) [23], [24] and Non-Linear SST (NLSST) [25], producing reliable temperatures over the decades, as validated by various studies citing an RMSD of about 0.5K, proving its utility for diverse applications. With the onset of the 2000s, a new generation of near-polar orbiting satellites, including the Terra/Aqua satellites' Moderate Resolution Imaging Spectroradiometer (MODIS) and the Suomi National Polar-orbiting Partnership (NPP), has contributed to SST retrievals.

The first Korean geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), was launched on June 27, 2010. The Korea Meteorological Administration (KMA) utilized it for meteorological operations and related services until March 31, 2020. COMS was equipped with a Meteorological Imager (MI), utilizing one visible and four infrared channels to observe the Earth. The COMS/MI provided users with global SST data every three hours at a spatial resolution of 4 km. As successor to COMS and the 2nd Korean geostationary satellite, the Geostationary Korea Multi-Purpose Satellite-2A (GK-2A) was launched on December 4, 2018, and positioned over the equator at 128.2°E. It began offering its SST data as a standard product on July 25, 2019, and continues to distribute data in real-time through the National Meteorological Satellite Center of the KMA. The Advanced Meteorological Imager (AMI) on GK-2A is comprised of 16 bands, including three visible channels, three near-infrared, four mid-wave infrared, and six long-wave infrared channels. GK-2A/AMI has provided SST for the full-disk area every 10 minutes at a 2 km spatial resolution. Prior to the launch of GK-2A, the SST algorithm for GK-2A was developed using Himawari-8 data as proxy data and validated for accuracy [26]. Subsequently, GK-2A/AMI SST has been utilized operationally by the KMA and extensively in oceanographic research. However, studies validating the accuracy of GK-2A SST products have not yet been reported.

Therefore, in this study, we introduce the algorithm for GK-2A/AMI SST, validate the accuracy of GK-2A SST in the Asia-Pacific region over the past four years, and present the characteristics of SST differences in comparison to in-situ measurements. To achieve this, we collect observed SST data in the study area, generate collocated datasets between GK-2A/AMI SST and in-situ SST data, and evaluate SST accuracy. Furthermore, we intend to explore and evaluate the influence of

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** (a) Spatial distribution of the number of matchup points between GK-2A/AMI and in situ measurements in bins of 1° × 1° and the histograms of the matchup database with respect to (b) month, (c) local time (hour), (d) latitude (°N), and (e) in situ temperature (°C) for the period from July 2019 to August 2023.</figcaption></figure>

various environmental factors that may contribute to errors in
GK-2A SST accuracy, particularly the effects of satellite zenith
angle (SZA).

## II. DATA AND METHODS

### A. Satellite Data

The AMI sensor on the GK-2A satellite has 16 bands ranging from 0.43 µm to 13.39 µm and produces SST data for the full disk every 10 minutes at a 2 km resolution at nadir (Fig. 1(a), Table I). The GK-2A SST algorithm, similar to that of GOES-R, employs a Hybrid SST method using satellite BT and simulated clear-sky BT from radiative transfer models for each channel [27], [28], as well as a 4-band empirical SST method (MSST hereafter). The default algorithm for GK-2A/AMI SST, operated by the KMA, is analogous to the U.S. GOES-R/ABI (Advanced Baseline Imager) SST and utilizes the MSST method based on BTs from four bands (8.7 µm, 10.5 µm, 11.2 µm, and 12.3 µm) [26], [29], [30]. The present research utilized SST data derived from the GK-2A/AMI bands collected from July 1, 2019, to August 31, 2023, a period spanning four years and two months. SST calculations were particularly applied using the 10-minute interval data from the full disk area. Level-2 SST data with the full resolution of the GK-2A satellite observation grid were used to produce the matchup database.

### B. In-Situ Temperature Measurements

For the validation of the SST products derived from the GK-2A satellite, in situ oceanic temperature data were collected during the satellite data period. Data from drifting buoys and moored buoys, continuously collected in real-time via the World Meteorological Organization's (WMO) Global Telecommunication System (GTS), can be utilized. The GK-2A SST processing chain employs these real-time GTS data after quality control (QC) for SST calculations [26], [30]. However, this study did not use the GTS network; instead, it utilized high-quality data rated as Quality Level 5 from the U.S.
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<table><caption>TABLE I
SPECIFICATIONS OF THE GEO-KOMPSAT 2A ADVANCED METEOROLOGICAL IMAGER (GK-2A/AMI) SPECTRAL CHANNELS</caption><thead><tr><th>Category</th><th>Channel No.</th><th>Channel Name</th><th>Center Wavelength (μm)</th><th>Spatial Resolution (km)</th></tr></thead><tbody><tr><td rowspan="3">Visible</td><td>1</td><td>VIS (VIS0.4)</td><td>0.47</td><td>1.0</td></tr><tr><td>2</td><td>VIS (VIS0.5)</td><td>0.51</td><td>1.0</td></tr><tr><td>3</td><td>VIS (VIS0.6)</td><td>0.64</td><td>0.5</td></tr><tr><td rowspan="3">Near Infrared</td><td>4</td><td>VNIR (VIS0.8)</td><td>0.86</td><td>1.0</td></tr><tr><td>5</td><td>SWIR (NR1.3)</td><td>1.37</td><td>2.0</td></tr><tr><td>6</td><td>SWIR (NR1.6)</td><td>1.61</td><td>2.0</td></tr><tr><td rowspan="4">Mid-Wave Infrared</td><td>7</td><td>MWIR (IR3.8)</td><td>3.83</td><td>2.0</td></tr><tr><td>8</td><td>MWIR (IR6.3)</td><td>6.21</td><td>2.0</td></tr><tr><td>9</td><td>MWIR (IR6.9)</td><td>6.94</td><td>2.0</td></tr><tr><td>10</td><td>MWIR (IR7.3)</td><td>7.33</td><td>2.0</td></tr><tr><td rowspan="6">Long-Wave Infrared</td><td>11</td><td>TIR (IR8.7)</td><td>8.59</td><td>2.0</td></tr><tr><td>12</td><td>TIR (IR9.6)</td><td>9.62</td><td>2.0</td></tr><tr><td>13</td><td>TIR (IR10.5)</td><td>10.35</td><td>2.0</td></tr><tr><td>14</td><td>TIR (IR11.2)</td><td>11.23</td><td>2.0</td></tr><tr><td>15</td><td>TIR (IR12.3)</td><td>12.37</td><td>2.0</td></tr><tr><td>16</td><td>TIR (IR13.3)</td><td>13.29</td><td>2.0</td></tr></tbody></table>

NOAA/NESDIS's STAR in situ Quality Control Monitor (iQUAM) dataset, which has already undergone a rigorous and successful QC process [31]. To understand the error characteristics associated with marine-atmospheric environmental changes, wind speed data from iQUAM and KMA moored buoys were also collected and utilized.

### C. Sea Surface Temperature Regression Algorithm

With advances in sensor technology, the number of satellite channels available for deriving SST has increased compared to the past. Himawari-8/AHI, GOES-16/ABI, and GK-2A/AMI all have channels at 8.7 µm, 10.5 µm, 11.2 µm, and 12.3 µm. Consequently, the MSST retrieval algorithm has been developed, utilizing BTs from these four bands. The GK-2A SST algorithm suite includes MCSST, NLSST, Hybrid SST (HSST), and MSST. These algorithms are designed to be interchangeable so that if any one of the GK-2A channels fails, an alternative algorithm can be selected for use. The HSST is developed to derive SST by using both observed satellite BTs and clear-sky BTs simulated through the Radiative Transfer for TOVS (RTTOV) model version 11.2 [26], [32]. Unlike MCSST and NLSST, HSST calculates coefficients based on the regression analysis of the differences between the observed BTs and those simulated by the radiative transfer model [27], [28].

The GK-2A SST algorithms, including split-window MCSST (1) and NLSST (2), and HSST (3), utilize the following formulas [25], [26]

$$ MCSST = a_1 T_{10.5} + a_2 (T_{10.5} - T_{12.3}) + a_3 (T_{10.5} - T_{12.3}) (\sec\theta - 1) + a_4 \quad (1) $$

$$ NLSST = a_1 T_{10.5} + a_2 T_{FG} (T_{10.5} - T_{12.3}) + a_3 (T_{10.5} - T_{12.3}) (\sec\theta - 1) + a_4 \quad (2) $$

$$ HSST = T_{FG} + a_1 (T_{10.5} - T_{CS10.5}) + a_2 T_{FG} \{(T_{10.5} - T_{CS10.5}) - (T_{12.3} - T_{CS12.3})\} + a_3 \{(T_{10.5} - T_{CS10.5}) - (T_{12.3} - T_{CS12.3})\} (\sec\theta - 1) + a_4 \quad (3) $$

where $\theta$ represents the SZA, $T_{CS10.5}$ refers to the BT at 10.5 µm from the clear-sky radiance of the RTTOV model (version 11.2) [32], and $T_{FG}$ is a first-guess or reference SST (OSTIA herein) as a priori estimate of SST. The RTTOV is known to be a very fast radiative transfer model for passive downward-viewing satellite radiometers, spectrometers, and interferometers [33]. The coefficients from $a_1$ to $a_4$ are empirically derived from regressions between satellite BTs and in-situ measurements.

The default algorithm for GK-2A SST as adopted by the KMA is the MSST method, which has been in operational use since the satellite's launch in 2018. The composition and background of each algorithm are detailed in [26]. The MSST method, in particular, is an extension of the empirical regression analysis traditionally based on 2-3 bands, by incorporating additional bands to derive SSTs. This multi-band SST algorithm was first utilized in real-time on the United States' GOES-R satellite [29]. Similarly, GK-2A/AMI adopts all channels at 8.7 µm, 10.5 µm, 11.2 µm, and 12.3 µm. The GK-2A MSST algorithm, like that used in GOES-16/ABI, centers around the 10.5 µm band and adds three more bands, thus employing a total of four bands based on the following formulation as follows

$$ MSST = a_1 T_{10.5} + a_2 (T_{10.5} - T_{12.3}) + \{a_3 (T_{10.5} - T_{8.7}) + a_4 (T_{10.5} - T_{11.2})\} (\sec\theta - 1) + \{a_5 (T_{10.5} - T_{8.7}) + a_6 (T_{10.5} - T_{11.2}) + a_7 (T_{10.5} - T_{12.3})\} T_{FG} + a_8 \quad (4) $$
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<caption>TABLE II</caption>

ERROR STATISTICS FOR GK-2A SST, INCLUDING RMSD, BIAS, STANDARD DEVIATION, ANOMALY CORRELATION COEFFICIENT, AND SCATTER INDEX, COMPARING DIFFERENT FIRST-GUESS SST USAGES (OSTIA, GK-2A MCSST, GK-2A NLSST) FROM 2019 TO 2023.

<table><thead><tr><th rowspan="2"></th><th colspan="2">RMSD (K)</th><th colspan="2">Bias (K)</th><th colspan="2">STD (K)</th><th colspan="2">Anomaly Corr. Coef.</th><th colspan="2">Scatter Index</th></tr><tr><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th></tr></thead><tbody><tr><td>OSTIA</td><td>0.4562</td><td>0.4756</td><td>0.0479</td><td>-0.0342</td><td>0.4537</td><td>0.4744</td><td>0.8222</td><td>0.7952</td><td>0.0191</td><td>0.0199</td></tr><tr><td>MCSST</td><td>0.4717</td><td>0.4998</td><td>0.0617</td><td>-0.0449</td><td>0.4676</td><td>0.4978</td><td>0.8121</td><td>0.7768</td><td>0.0198</td><td>0.0210</td></tr><tr><td>NLSST</td><td>0.4729</td><td>0.5011</td><td>0.0618</td><td>-0.0449</td><td>0.4689</td><td>0.4991</td><td>0.8115</td><td>0.7761</td><td>0.0198</td><td>0.0210</td></tr></tbody></table>

The MSST algorithm uses a single set of coefficients formulation regardless of whether it is day or night. Employing separate algorithms or regression coefficients for day and night can lead to discrepancies in SST at transitional times such as twilight. In contrast, by using the same SST coefficients regardless of the time of day, MSST offers the advantage of resolving issues that arise at these boundaries between day and night. The MSST method calculates satellite SSTs by generating coincident points with in situ ocean temperatures. Using matchups from the past month, daily coefficients are determined and applied to the formulation to derive SSTs of GK-2A.

For SST first guess, EUMETSAT OSI SAF uses climatology SST [34], while NOAA's GOES-16/ABI employs foundation SST represented by CMC SST [35]. In this study, GK-2A MCSST, GK-2A NLSST, and OSTIA were each utilized as first-guess SST inputs. We calculated SST and compared these against in-situ observations to determine which had the lowest error statistics. Among the three first-guess SST options, OSTIA showed the lowest differences both during daytime (RMSD: 0.4562K, Bias: 0.049K, STD: 0.47K) and nighttime (RMSD: 0.4756K, Bias: -0.0342K, STD: 0.47K). This suggests that achieving the ultimate goal of calculating SST$_{20}$ does not necessarily mandate the inclusion of 20 cm SST data. Employing a stable SST foundation, devoid of diurnal variations, may yield a more consistent and reliable SST calculation. Based on these findings, OSTIA SST was ultimately selected and utilized as the first-guess SST for MSST (Table II).

### D. Removal of Cloud Pixel

Subsequent steps were taken to eliminate cloudy or cloud-contaminated pixels by applying thresholds similar to those used in cloud detection methods for GOES-R [26], [27], [28]. First, pixels with infrared channel BTs ($T_{10.5}$ and $T_{12.3}$) below – 3.5K were removed for both day and night in the gross cloud test. Extremely high temperatures exceeding 37°C were also discarded. In the cloud removal algorithm, the RTTOV model was used to simulate clear-sky radiance at four infrared bands ranging from 8 µm to 13 µm and to obtain the clear-sky BTs at each band [33]. To run the RTTOV model, atmospheric data

such as surface pressure, skin temperature, 2-m temperature, 2-m mixing ratio, 10-m wind speed, and profiles of pressure and temperature were obtained from the KMA Unified Model (UM) weather prediction results, which have a spatial grid of 25 km × 25 km. Using these model results, we applied an RTTOV test step to verify the accuracy of fitting the observed BT vector with the clear-sky simulated BT vector [28], [36].

We also applied a static SST test to detect obviously unrealistic SST values and an adaptive SST test to identify ambient cloudiness at cloud boundaries. In a series of uniformity tests, the standard deviations of BTs ($T_{8.7}$, $T_{10.5}$, $T_{11.2}$, and $T_{12.3}$) within a 3 × 3 pixel window were calculated, and pixels with values greater than 0.3K were removed. Another uniformity test discarded pixels with differences between maximum and minimum BTs ($Max_{T_{10.5}} - Min_{T_{10.5}}$, $Max_{T_{12.3}} - Min_{T_{12.3}}$) greater than 0.5 K from further processing. Additionally, further tests were performed to detect thin cirrus clouds by applying dynamic thresholds to the $T_{10.5} - T_{12.3}$ difference as a function of $T_{10.5}$ [37]. Finally, pixels with poor satellite-sun-target geometry were eliminated using sun glint angle thresholds (15°) and SZAs exceeding 65° [38], [39].

### E. Matchup Procedure and Validation

To validate the SSTs derived from the GK-2A satellite, actual ocean water temperature data, including surface drifter data and coastal buoy observations, were compared with satellite data. Matching points were generated by considering a time interval within 10 minutes and a distance within 2 km. The entire dataset utilized GK-2A data collected from July 2019 to August 2023 (spanning four years and 2 months), excluding the post-satellite in-orbit test (IOT) period, to ensure that the validation results were not biased towards specific seasons. The data used in the SST validation were those that passed all the cloud removal steps mentioned in the previous section, ensuring the best clear-sky conditions.

Satellite-observed SSTs can be classified to SST$_{depth}$ measured at a few meter depth, sub-skin SST$_{sub-skin}$, foundation temperature SST$_{fnd}$, and SST$_{20}$ measured by surface drifters at a depth of 20 cm [2], [40]. In this study, all in-situ observation data obtained from ships, CTD, and KMA coastal buoys were
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excluded, and only data from surface drifters measured at about
20 cm were used. Therefore, the series of SSTs presented below
represent the 20 cm water temperature and are conveniently
denoted as SST instead of SST20.

For a quantitative statistical evaluation, this study utilized the RMSD, bias, standard deviation (STD), anomaly correlation coefficient (ACC), and scatter index (SI). The ACC was calculated as the correlation coefficient between two SST datasets (e.g., [41]), obtained by subtracting the climatological values from the OSTIA SSTs for each matchup in the database. The SI is a statistical measure used to quantify the relative dispersion or spread of data points around a reference line, particularly in comparing observed and retrieved SST data. It is calculated by RMSD normalized by mean of the observations at each grid point. A lower SI indicates that the retrieved SST closely match the observed SST, while a higher SI suggests greater discrepancies between the retrieved SST and in-situ measurement [42].

In this study, Kelvin (K) was used to measure statistical errors in SST and BT, adhering to scientific convention. Celsius (°C), on the other hand, was employed for discussions of SST and SST gradient magnitude to facilitate intuitive understanding.

III. RESULTS

A. *Distribution of Matchup Database*

From July 2019 to August 2023, the matchup process between GK-2A satellite SST and in situ SST measurements resulted in a total of 1,727,054 matchup points (Fig. 1). Note a few matchup points were obtained along the coast of Korean Peninsula, likely due to the marine meteorological buoys deployed by the KMA. To assess the quality of SST₂₀ retrievals, the number density of the matchup database was reduced to 1,445,866 by eliminating KMA buoys and other moored buoys in the Indian Ocean. Fig. 1(a) depicts the spatial distribution of these matchup points within a given grid (1° × 1°), showing that the number of satellite-in situ matchup points was not uniformly distributed and was predominantly concentrated in the mid-latitudes of the Northern Hemisphere. In contrast, fewer than 300 matchup points were generated in open ocean areas, while the latitude range of 20–40°N produced between 500 and 1,000 matchup points, widely distributed spatially. Areas like the northern coast of the East Sea (Sea of Japan), the northern Yellow Sea where surface drifters and moored buoys are absent, did not generate any matchup points. Similarly, near Indonesia and the Philippines, regions frequently covered by clouds such as the Intertropical Convergence Zone (ITCZ), yielded minimal matching points.

Seasonally, the distribution of matchup points was checked for
bias and was found to be consistent, with monthly counts ranging
between 100,000 to 130,000, showing little variation across
seasons (Fig. 1(b)). Fig. 1(c) shows the distribution of matchup
points across local times, indicating an even distribution across day
and night, not skewed to any particular time. However, significant
imbalances were observed latitudinally; fewer matchup points
were found near the equator and in high-latitude regions, with

<figure><img src="image_3.png" /></figure>

**Fig. 2.** Comparison between GK-2A/AMI SSTs and in-situ SSTs during the (a) daytime and (b) nighttime, where the color represents the percentage of the data to the total number of matchup points in a bin of 0.5°C × 0.5°C from July 2019 to August 2023. SST differences (GK-2A SST – in-situ SST) in (c) daytime and (d) nighttime against in-situ SST, where the bars represent standard deviation of the SST differences between in-situ SSTs and satellite SST retrievals of each interval of 1°C, respectively. (e) and (f) represent the number density of each interval depicted in (c) and (d), respectively.

higher frequencies concentrated between 20–40°N(°S) in both the
Northern and Southern Hemispheres, with the Northern
Hemisphere showing more points (Fig. 1(d)). Additionally, the
frequency of matchup points relative to surface water temperatures
showed an increasing trend from 5°C to 29°C (Fig. 1(e)), with no
matchup points existing in extreme cold temperatures below 5°C
and extreme high temperatures above 32°C. This spatial and
temporal distribution of matchup points was used to validate the
quality of the GK-2A SST.

B. *Overall Quality of GK-2A SST*

SST data derived from GK-2A were validated against
oceanic observational data. Initially, the matchup database was
categorized into daytime and nighttime periods based on the
solar zenith angle at each matchup point, with angles less than
80° classified as daytime and those greater than 80° as
nighttime [26], [30]. Basic statistical metrics such as bias,
RMSD, STD, ACC and SI were calculated to assess the
classified daytime and nighttime SST uncertainties. The
statistics will be a combination of retrieval uncertainties
(systematic error, bias and random uncertainties), in situ
uncertainties and uncertainties from the validation procedure
and a series of processes. Fig. 2(a) and 2(b) display the
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<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Comparison of GK-2A SSTs and in-situ temperatures for (a) total, (b) daytime, and (c) nighttime periods, with colors indicating the percentage of data relative to total matchup points within each 0.5°C × 0.5°C bin from July 2019 to August 2023. Panels (d)-(f) show comparisons of Himawari-8 SSTs against in-situ SSTs during the same period, and (g)-(i) detail the inter-comparison between GK-2A SSTs and Himawari-8 SSTs.</figcaption></figure>

comparison of in situ ocean temperatures with GK-2A/AMI
SSTs for daytime and nighttime, respectively, with number
density in each grid.

Results of the daytime SST validation for GK-2A showed
almost no bias at –0.0020 K, with an RMSD and STD of 0.4785
K, an ACC of 0.8048, and a SI of 0.0200 (Fig. 2(a)). The
nighttime SST validation showed a slight negative bias of –
0.0714 K, an RMSD of 0.5009 K, a STD of 0.4958 K, an ACC
of 0.7794, and an SI of 0.0210 (Fig. 2(b)). Although GK-2A
SSTs displayed slightly lower bias and RMSD during the
daytime compared to nighttime, as seen in Fig. 2(c) and 2(d),
the RMSD (satellite – buoy) did not exhibit any significant
differences between day and night, showing nearly identical
distribution. This underscores the robust application of pre-
launch algorithms without compromising performance.

Regardless of daytime or nighttime, GK-2A consistently
demonstrated an RMSD of about 0.5 K or lower, with slightly
lower uncertainty during daytime compared to nighttime. Fig.
2(c) and 2(d) further illustrate the SST matchup differences
plotted against sea temperatures. As shown in Fig. 2(e) and 2(f),
the number of matchups is significantly lower for conditions
where the in-situ water temperature is below 10°C, but a clear
positive bias becomes evident as the temperature decreases.
Specifically, at temperatures below 5°C, the bias reached as
high as 0.8 K, and this pattern appeared similarly regardless of
day or night. This positive bias at lower temperatures can be
attributed to the large positive bias observed at high latitudes
greater than 40 degrees, which accounts for 97% of the total
observations in the temperatures below 5°C.
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<caption>TABLE III</caption>

ERROR STATISTICS FOR GK-2A SST DURING DAY AND NIGHT FROM 2019 TO 2023, INCLUDING RMSD(K), BIAS(K), STANDARD DEVIATION(K), ANOMALY CORRELATION COEFFICIENT, AND SCATTER INDEX). ADDITIONALLY, IT COMPARES SST ERRORS BETWEEN GK-2A SST AND HIMAWARI-8 SST, ALONG WITH INTER-COMPARISON ERRORS BETWEEN THESE TWO SST DATASETS

<table><thead><tr><th>Satellite</th><th>Period</th><th>Error Statistics</th><th>Total</th><th>Daytime</th><th>Nighttime</th></tr></thead><tbody><tr><td rowspan="5">GK-2A</td><td rowspan="5">Jul 2019 - Aug 2023</td><td>RMSD</td><td>0.4913</td><td>0.4785</td><td>0.5009</td></tr><tr><td>Bias</td><td>-0.0411</td><td>-0.0020</td><td>-0.0714</td></tr><tr><td>STD</td><td>0.4896</td><td>0.4785</td><td>0.4958</td></tr><tr><td>Anomaly Corr. Coeff.</td><td>0.7912</td><td>0.8048</td><td>0.7794</td></tr><tr><td>Scatter Index</td><td>0.0206</td><td>0.0200</td><td>0.0210</td></tr><tr><td rowspan="5">GK-2A</td><td rowspan="5">Jan 2022 - Dec 2022</td><td>RMSD</td><td>0.4503</td><td>0.4466</td><td>0.4534</td></tr><tr><td>Bias</td><td>-0.0171</td><td>0.0053</td><td>-0.0369</td></tr><tr><td>STD</td><td>0.4499</td><td>0.4466</td><td>0.4519</td></tr><tr><td>Anomaly Corr. Coeff.</td><td>0.8019</td><td>0.8082</td><td>0.7939</td></tr><tr><td>Scatter Index</td><td>0.0177</td><td>0.0176</td><td>0.0178</td></tr><tr><td rowspan="5">Himawari-8</td><td rowspan="5">Jan 2022 - Dec 2022</td><td>RMSD</td><td>0.6402</td><td>0.5964</td><td>0.6765</td></tr><tr><td>Bias</td><td>-0.3901</td><td>-0.3299</td><td>-0.4434</td></tr><tr><td>STD</td><td>0.5075</td><td>0.4969</td><td>0.5108</td></tr><tr><td>Anomaly Corr. Coeff.</td><td>0.7494</td><td>0.7615</td><td>0.7364</td></tr><tr><td>Scatter Index</td><td>0.0252</td><td>0.0235</td><td>0.0266</td></tr><tr><td rowspan="5">GK-2A vs. Himawari-8</td><td rowspan="5">Jan 2022 - Dec 2022</td><td>RMSD</td><td>0.5881</td><td>0.5531</td><td>0.6174</td></tr><tr><td>Bias</td><td>0.3730</td><td>0.3351</td><td>0.4065</td></tr><tr><td>STD</td><td>0.4547</td><td>0.4401</td><td>0.4646</td></tr><tr><td>Anomaly Corr. Coeff.</td><td>0.8182</td><td>0.8305</td><td>0.8043</td></tr><tr><td>Scatter Index</td><td>0.0235</td><td>0.0178</td><td>0.0182</td></tr></tbody></table>

GK-2A and Himawari-8 satellites, positioned at longitudes 128.2°E and 140.7°E respectively, monitor overlapping regions, enabling the comparative analysis of SSTs within their common observation areas. We matched GK-2A/AMI SST, Himawari-8/Advanced Himawari Imager (AHI) SST, and surface drifter data, producing 272,308 matchup points for areas with a SZA of 65° using data from 2022. The comparative results between the two satellite SSTs and in-situ observations, as well as the inter-comparison between the two satellite SSTs, are presented in Fig. 3 and Table III. GK-2A/AMI SST showed a minimal bias and an RMSE and STD of 0.45 K, while Himawari-8/AHI displayed a negative bias of -0.69 K, with higher RMSE and STD values of 0.64 K and 0.51 K, respectively (Fig. 3). In comparison between GK-2A/AMI and Himawari-8/AHI, there was a positive bias of 0.37 K, with RMSE and STD of 0.59 K and 0.45 K, respectively (Table III). These differences likely stem from GK-2A representing sub-surface temperatures at 20 cm depth, whereas Himawari-8 SST, due to its algorithm's characteristics, represents skin temperature [43].

### C. Spatial Distribution of GK-2A SST Matchup Differences

The overall RMSD of GK-2A SST is within 0.5K, which aligns with the target accuracy roughly established by KMA before the satellite launch based on previous literature. However, the spatial distribution of SST matchup RMSD statistics is expected to vary across the full disk area. Therefore, only grid cells containing at least 50 matchup points in a 2°×2° grid were selected to calculate the RMS and bias for day and night, as depicted in Fig. 4. Fig. 4(a) and 4(b) show the spatial distribution of daytime and nighttime RMSD, limited to SZAs below 65°. Although most cases show an RMS less than 0.6 K, there is a tendency for SST matchup differences to increase towards the edges of the full disk. Particularly, discrepancies around 1.0–1.5 K were relatively higher near Antarctica in the Southern Hemisphere and the northwestern Pacific in the Northern Hemisphere, suggesting the possibility of SZA influencing SST accuracy.
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<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Comparison between GK-2A/AMI-derived SSTs and in-situ SSTs during the (a) daytime RMSD, (b) nighttime RMSD, (c) daytime bias, and (d) nighttime bias bin of 2°×2° from July 2019 to August 2023.</figcaption></figure>

The spatial distribution of SST bias, similar to RMS, showed a pronounced positive bias towards the edges of the full disk where the SZAs are greater (Fig. 4(c) and 4(d)). Positive biases were also broadly distributed near Antarctica, along the southern and western coasts of Australia, in the low-latitude eastern Pacific near the equator, and in the Indian Ocean. In contrast, negative biases were predominantly found in the Southern Hemisphere, including the eastern coast of Australia, and occurred around the Korean Peninsula as well. These biases displayed similar characteristics between day and night.

### D. Hourly-Latitudinal Variations of SST Quality

To investigate if there were diurnal variations in GK-2A SST quality, the matchup statistics were sampled according to local time and latitude. Fig. 5(a) and 5(b) illustrate the distribution of RMS and bias derived by comparing GK-2A SSTs with in situ water temperatures, categorized by latitude and time. In the Southern Hemisphere at latitudes above 40°S, RMS statistics were observed to be between 0.5–0.6 K; however, no significant time-dependent variations in errors were evident across the board. Between 10 degrees north and south of the equator, RMS around 0.6 K were observed between 8 and 14 hours, slightly higher than at mid-latitudes. The smallest RMSD, consistently under 0.4 K, was observed in the 10–30° range across both hemispheres, exhibiting no distinct diurnal variations. This may be attributed to the increased number of matchups, as indicated by previous histograms depicting data number density in Fig. 1(d).

In the latitude band of 30–40°N, relatively higher RMSD values of 0.6–0.7 K were recorded, and at latitudes above 40°, notable RMS of 0.8–1.0 K emerged (Fig. 5(a)). Bias showed a

pronounced positive deviation increasing towards higher latitudes in both hemispheres, with daytime (8–16 local time) showing slightly higher biases of about 0.2–0.3 K compared to nighttime, by approximately 0.1 K (Fig. 5(b)). This pattern was similar at high latitudes above 40 degrees in the Southern Hemisphere. In mid-latitude regions between 10–30°S, a very weak positive bias was present between 8–10 local time, with negative biases predominating at other times similar to nighttime data. Near the equator, a weak positive bias of about 0.1–0.2 K occurred around 8 local time.

Overall, significant discrepancies were evident in latitudes above 40 degrees in both hemispheres (Fig. 5(a)). While the Southern Hemisphere generally showed smaller RMSD values, a pronounced positive bias was notable, and in the higher latitudes of the Northern Hemisphere, both RMSD and bias showed significant variations. The distribution of errors relative to local time did not display distinct characteristics. These results suggest that the *MSST* methodology of not distinguishing between day and night but using a single formulation derived from the previous month's matchup data for SST coefficients is reasonably valid.

<caption>**Fig. 5.** Hourly-latitudinal variations of (a) RMSD (K) and (b) bias error (K) between GK-2A SSTs and in-situ SSTs (GK-2A – in-situ) from July 2019 to August 2023. (c) and (d) as same (a) and (b) but for monthly-latitudinal variations.</caption>

### E. Monthly-Latitudinal Variations of SST Errors

Fig. 5(c) illustrates the monthly distribution of RMS statistics for GK-2A SST by latitude. The mid-latitude region between 10–30 degrees exhibited the smallest errors, ranging from 0.3 K to 0.5 K, with no distinct seasonal characteristics. In contrast, latitudes between 40–50°N displayed relatively larger RMS statistics, reaching 0.9 K to 1.0 K, with the highest values characteristically occurring during the summer months from June to September. The causes of these errors may vary, but as showed in Fig. 4, they could be associated with issues at the edges of the full disk, low sea surface temperatures, ocean currents, cloud contaminated pixels, or the size of frontal systems. Like RMSD, the bias did not show distinct seasonal patterns either. Positive biases occurred at higher latitudes in
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<figure><img src="image_6.png" /><figcaption>**Fig. 6.** (a) GK-2A/AMI SST differences (K) as a function of wind speed (m/s) at day (red) and night (blue). The errorbars represent the mean differences ($\pm \sigma/\sqrt{N-1}$) of each interval, standard deviation $\sigma$ and $N$ the data number of each bin and (b) histogram of data number density ($N$) at each bin.</figcaption></figure>

both the Southern and Northern Hemispheres (Fig. 5(d)). In this region, the SZA is also high, and the relatively small number of matchup data could also contribute to the large errors.

Among the various factors affecting SST errors, these physical environments play an important role, but the uneven distribution of matchups has often been overlooked. Although historically less emphasized than other issues, the monthly and latitudinal distribution of SST errors can highlight the importance of the uneven distribution of matchups. This means that areas with a greater number of matchups in mid-latitudes may exhibit higher accuracy.

## F. Effect of Wind Speed on SST Differences

Since satellite-derived SST can be affected by atmospheric conditions such as wind, we investigated the relationship between GK-2A SST errors and wind speed. Although some surface drifters are equipped with wind sensors, matchup data including wind information were not generated in the final database. To examine the relationship with wind, additional matchups were conducted using moored buoys. As a result, 281,188 matchups were obtained from the KMA buoys along the Korean coast and buoys in the Indian Ocean. The temperature sensors on these buoys are positioned at depths of approximately 4–5 meters. Typically, wind data should be converted to a 10-meter height, considering atmospheric stratification using the LKB (Liu-Katsaros-Businger) model [44]. However, since not all buoys report atmospheric observations, the wind data were converted to 10-meter winds under the assumption of neutral conditions. Therefore, the wind speed data used in the subsequent analysis may have some uncertainty as atmospheric stratification between the buoy height and the 10-meter altitude was not considered [11].

Fig. 6(a) depicts SST differences as a function of wind speed,

noting that data for wind speeds above 6 m/s are relatively scarce, thereby reducing the reliability of the results. To assess the impact of wind speed, the matchup data were categorized into day and night based on the solar zenith angle of 80°, allowing for the analysis of SST differences with in-situ SST. Overall, SST discrepancies exhibited a slight negative bias within –0.2 K. However, at wind speeds below 4 m/s, characteristic biases were observed, consistent with previous studies. Satellite-derived SSTs were up to 0.23 K higher than in-situ measurements during the day, while at night, the bias decreased to around –0.23 K as wind speeds dropped below 1 m/s. The overall bias was distributed within a range of ±0.2 K. At wind speeds above 6 m/s, the number of observations tended to gradually decrease (Fig. 6(b)), resulting in very little variability in bias with respect to wind speeds, ranging from –0.02 to –0.06 K during the day and –0.11 to –0.14 K at night. This observation can be attributed to the fact that the majority of wind matchup data are sourced from KMA buoys located around the Korean Peninsula. These buoys have water temperature sensors installed at depths of 3-4 m, hence the SST differences depicted in Fig. 6(a) reflect the discrepancy between the SST$_{20}$ and the water temperature measured at several meters' depth. The negative deviation is likely due to this variation in the vertical temperature profile of the water. Furthermore, an analysis of the monthly wind speed distribution in the matchup data (>6 m/s) revealed a higher occurrence in winter (25%) compared to summer (17%). This seasonal variation in the frequency of matchups is also believed to contribute to the observed negative bias during the winter months. GK-2A SST discrepancies also showed a slight negative bias at moderate wind speeds, but this had less impact compared to the low wind speed range. Distinct positive and negative biases were observed for day and night at wind speeds below 4 m/s. These trends are consistent with previous studies on the impact of wind speed on skin-bulk satellite SST differences [3], [45]–[50].

One key aspect of wind speed's impact on SST retrieval accuracy is the variability of sea surface emissivity [51]. This emissivity changes with surface roughness, emission angle, and refractive index, which in turn depends on salinity, chlorinity, and temperature (e.g., [52]). Emissivity gradually decreases as the zenith angle of the observed radiation increases. Radiation zenith angles below 30° have minimal influence, but angles above 70° exhibit significant effects [53]. This variation in sea surface emissivity may also contribute to the observed negative bias. The influence of the sea surface emissivity should be further explored in future studies.

## G. Effect of Water Vapor on SST Differences

Atmospheric water vapor content significantly influences the accuracy of satellite-derived SST. The BT difference (BTD) typically increases with water vapor concentration, and in very humid conditions, the BTD can exceed 2 K. Upward radiation from the sea surface is absorbed by atmospheric water vapor before reaching the satellite sensor. Although the radiative
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<figure><img src="image_7.png" /><figcaption>**Fig. 7.** GK-2A/AMI SST errors as a function of brightness temperature differences between 10.5 µm and 11.2 µm at (a) day and (b) night, where the background colors represent the density of matchup data and in each bin for the period of 2019–2023. The errorbars represent the standard deviation of the SST difference of each interval. (c) and (d) the histograms of BT differences for each interval of (a) and (b), respectively.</figcaption></figure>

emission from water vapor is generally weaker than that from the sea surface, under high water vapor conditions, it can distort sea surface radiation and result in larger-than-expected BTD values. This study assessed the GK-2A SST algorithm's effectiveness in mitigating these effects by analyzing SST error statistics related to the 10.5 µm and 11.2 µm channel differences. Lower BTD values correspond to relatively dry atmospheric conditions, while values exceeding 3 K reflect high humidity levels. When the BTD is small, SST errors exhibit a slight negative bias. However, as the BTD exceeds 2 K, the bias shifts sharply toward overestimation (Fig. 7). Fig. 7 illustrates that while matchup data density decreases significantly for BTD values over 4 K, it increases nonlinearly within the 2 K to 4 K range. Under high water vapor conditions, lower satellite-observed BT12 values contribute to an increase in BTD, leading empirical SST algorithms to overestimate actual SSTs (e.g., [54]). This tendency is similar to those observed in NOAA/AVHRR solit window SST errors (e.g., [39]). Although incorporating BTDs into SST retrieval algorithms helps mitigate water vapor effects, residual biases may still persist. Further detailed research is required to comprehend the physical mechanisms behind this phenomenon.

Although high water vapor conditions can lead to significant overestimation errors of up to 2 K, in the 0–2 K range—representing moderately humid atmospheric conditions—GK-2A SST closely aligns with in situ measurements. This indicates that the GK-2A SST retrieval algorithm, based on a 4-band approach, produces relatively stable SST outputs. However, caution is required in its application under varying atmospheric conditions. This pattern is consistently observed both during the day and at night, with similar error magnitudes (Fig. 7(b)).

<figure><img src="image_8.png" /><figcaption>**Fig. 8.** RMSD as a function of SST gradient magnitude (°C km⁻¹) calculated using (a) GK-2A/AMI SST data and (b) OSTIA SST where the errorbars represent the mean differences (±σ/√N - 1) for each interval, with data number N and standard deviation σ. The dotted line indicates a least-squared fit line.</figcaption></figure>

When the BTD is negative, GK-2A SST tends to be underestimated compared to in-situ temperatures. Generally, the presence of atmospheric aerosols along the satellite's observational path results in negative BTDs, subsequently leading to an underestimation of sea surface temperature [55], [56]. The negative SST errors corresponding to negative BTDs in Fig. 7 is attributed to the influence of aerosol optical depth in the atmosphere.

## H. Effect of Surface Thermal Front on SST Errors

GK-2A observes a wide area of the ocean, encompassing regions with diverse oceanic phenomena and large current systems. Where warm and cold currents meet, such as at the boundaries between the Kuroshio and Oyashio currents, the North Equatorial Current, and the South Equatorial Current, well-developed oceanic thermal fronts are present [57], [58]. When creating the matchup database between satellite and in-situ temperature observations, the spatial gradient of SST (∇T) was also calculated to assess the impact of fronts using the following formula (6), which incorporates the magnitude of the zonal (x) and meridional (y) gradients of GK-2A SST (e.g., [59]). Given the low observation frequency of polar-orbiting satellites and the temporal variations in daily composite fields derived from multi-satellite data, we utilized the GK-2A SST image, which exhibits no temporal or spatial differences compared to the matchup data. To facilitate comparison with the GK-2A SST front, the frontal magnitude derived from OSTIA SST is also presented.

$$|\nabla T| = \sqrt{(\partial T / \partial x)^2 + (\partial T / \partial y)^2} \quad (6)$$

Considering the importance of the magnitude of the fronts, the SST gradient method was used instead of the edge detection method by Cayula and Cornillon [60]. This gradient magnitude is considered as an indicator of the strength of oceanic fronts, and the relationship between this SST front and the RMSD of the SST difference between satellite and in-situ measurements was investigated (Fig. 8).
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<figure><img src="image_9.png" /><figcaption>**Fig. 9.** Scatter plot of RMSD (K) and bias (K) of GK-2A SST as compared with in-situ SST for the period from July 2019 to August 2023, where the radius of dotted circles represents the mean SST differences ($radius = \sqrt{(bias)^2 + (RMSD)^2}$) and the colors represent satellite zenith angle (°).</figcaption></figure>

Fig. 8 clearly shows that as the magnitude of the SST gradient increases, the SST RMSD also increases linearly. Lower RMSD values were observed where thermal fronts were weak, and as the temperature gradient increased, RMSD progressively increased, and the amplitude of discrepancy variation was also relatively larger. A regression analysis conducted to explore the linear relationship between GK-2A SST front magnitude and SST differences demonstrated a statistically significant relationship within a 95% confidence interval (RMSD=1.4603*|SST|+0.3899, *p*-value=3.4667e-19, *R*² = 0.9918) (Fig. 8(a)). The frontal magnitude calculated using daily composite OSTIA data from multiple satellites showed a slightly lower trend but overall exhibited results similar to those of GK-2A (RMSD=2.4650*|SST|+0.4941, *p*-value=3.5961e-19, *R*² = 0.8721) (Fig. 8(b)). This indicates that as the front becomes more pronounced and spatial inhomogeneity increases, the error in satellite-derived SST increases. Similar findings have been reported in the validation processes for microwave AMSR-2 SSTs [61], [62].

# I. Dependence of SST Discrepancies on Satellite Zenith Angle

As the SZA increases, the optical depth of the atmosphere through which the radiated energy from the ocean surface passes also increases, enhancing the atmospheric impact on the radiation reaching the satellite. Due to the proportional increase in atmospheric absorption with SZA, correcting for SZA is critical to accurately determining SST (e.g., [63]). When the SZA exceeds approximately 50 degrees, sea surface emissivity undergoes significant changes due to the effects of wind-

induced surface roughness, which can severely degrade the accuracy of SST determination [43], [53]. Sensors on geostationary satellites like COMS/MI, Himawari-8/AHI, and GOES-16/ABI, as well as most polar-orbiting satellites, have long considered the impact of SZA on SST retrievals due to their extensive observational coverage [30], [39], [43]. Despite corrections for SZA in the GK-2A SST algorithm, a tendency for increased SST errors toward the edges of the full disk was observed in Fig. 4. Therefore, the variations in SST errors due to SZA were examined.

GK-2A observations occur at SZAs exceeding 65°, but the accuracy of SST decreases under such conditions; thus, SSTs from regions where the zenith angle is greater than 65° are officially excluded from GK-2A SST calculations. Fig. 9 presents a scatter plot combining the RMSD and Bias errors of GK-2A SST, where the matchup database is divided into bins by SZAs at 1-degree intervals, and the RMS and bias of SST differences are calculated for each bin. The dotted circles indicate the SST error radius based on bias and RMSD ($radius = \sqrt{(bias)^2 + (RMSD)^2}$), with proximity to the center of the circle indicating relatively smaller SST errors. The colors represent the SZA from 0° to 65°. Points in a shade of blue, slightly outside the first circle, showed the lowest uncertainties (0.3 K RMSD, 0.08 K bias) and corresponded to the SZA of approximately 10–20°. Generally, matchups with SZAs less than 40° were distributed within an RMSD of 0.4 K. In contrast, as the SZA exceeded 50° and increased further, the impacts of the matchup points moved outward toward the periphery of the concentric circles. Notably, matchups corresponding to around 60°, represented by red points, clearly showed a linear increase in bias as RMSD increased. This distribution of errors suggests that, despite accounting for the influence of SZA in SST calculations, large angles continue to exert a significant impact.

This amplification of SST bias and RMSD with increasing SZAs raises the question of whether other ocean-atmosphere state changes are strongly correlated with increasing zenith angles. Fig. 10 illustrates the distribution of GK-2A SST, frontal magnitude, and BTD with respect to latitude and SZA. The SST generally exhibits a weak negative correlation with increasing SZA at a given latitude (Fig. 10(a)). However, this appears to be attributed to the presence of a warm pool region near the center of the study area, where high SSTs are concentrated, with temperatures decreasing toward the edges of the disk at a given latitude (Fig. 10(d)). The magnitude of oceanic fronts is pronounced in the higher latitudes of the Northern Hemisphere but shows minimal variation with zenith angle elsewhere (Figs. 10(b) and 10(d)). The water vapor content, indirectly inferred from the BTD, is lower near the edges of the observed region, suggesting a weak correlation between moist conditions and SST overestimation due to high water vapor (Figs. 10(c) and 10(f)). This distribution appears to reflect the unique characteristics of the study area, particularly the presence of the central warm pool. Therefore, it is unlikely
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<figure><img src="image_10.png" /><figcaption>**Fig. 10.** (a) In-situ SST (°C), (b) OSTIA SST fronts (°C km⁻¹), and (c) BT differences (BT10.5–BT11.2) (K), plotted against satellite zenith angle (°). Panels (d)-(f) illustrate latitudinal variations in the rate of change for in-situ SSTs from (a), SST frontal magnitude from (b), and BT differences from (c) with respect to satellite zenith angle (°), indicated by positive (red) and negative (blue) rates. The black circles in (d) to (f) represent change rates that are statistically significant within a 95% confidence level.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 11.** GK-2A SST differences (satellite SST—in situ SST) as a function of satellite zenith angle (°); (a) daytime RMSD, (b) nighttime RMSD, (c) daytime bias, and (d) nighttime bias for the period from July 2019 to August 2023, where the bars indicate the mean difference of each interval. The errorbars in (c) and (d) represent the mean differences (± σ/√(N − 1)) of each interval, standard deviation σ and N the data number of each bin.</figcaption></figure>

that the underlying ocean-atmosphere state at the matchup point
locations directly causes the significant positive bias and
RMSD amplification observed at large SZA in Fig. 9.

If SZA influences SST errors, similar results can be expected regardless of whether it is day or night. To verify this, the matchup database was categorized into day and night based on solar zenith angle, and the impact of SZA on GK-2A SST errors was analyzed. Fig. 11 displays the RMSD and bias for SZAs exceeding 30°, differentiated between day and night. During the day, RMSD varied from 0.4 to 0.7 K at zenith angles above 30° and increased sharply above 50° (Fig. 11(a)). This pattern was almost identical at night (Fig. 11(b)). During the day, bias showed a negative deviation averaging –0.03 K at zenith angles from 30 to 55 degrees. The bias changed rapidly from –0.05 K to 0.15 K as the angle increased from 50° to 65° (Fig. 11(c)). At night, a negative bias of around –0.13 K appeared at angles up to 55°, then increased sharply to 0.1 K at 65 degrees (Fig. 11(d)). Both day and night errors showed a strong dependence on SZAs above 55°.

RMSD values exceeding 0.5 K and positive biases were
dominant when the SZA was above 60°, regardless of sea
temperature, latitude, or time of day. This suggests that the SZA
can be a major contributor to SST differences irrespective of
other factors. Despite the inclusion of terms accounting for the
SZA in the GK-2A SST algorithm, there was the sharp increase
in SST differences associated with the zenith angle during both
day and night. SST uncertainty may also be related to residual
cloud contamination. Even with the use of a strong cloud mask
through a series of cloud removal processes, there is still room
for improvement in the algorithm by considering the influence
of SZA on GK-2A SST uncertainty.
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<caption>TABLE IV</caption>

FORMULATION OF MSST AND EIGHT ADDITIONAL FORMULATIONS TO TEST SST QUALITY STATISTICS, WHERE $S_\theta$ IS $\sec\theta - 1$ WITH $\theta$ REPRESENTING THE SATELLITE ZENITH ANGLE AND BOLD FONT INDICATES THE NEWLY ADDED TERMS IN MSST.

<table><thead><tr><th></th><th>Formulation</th></tr></thead><tbody><tr><td>MSST</td><td>$MSST = a_1 T_{10.5} + a_2(T_{10.5} - T_{12.3}) + \{a_3(T_{10.5} - T_{8.7}) + a_4(T_{10.5} - T_{11.2})\} S_\theta + \{a_5(T_{10.5} - T_{8.7}) + a_6(T_{10.5} - T_{11.2}) + a_7(T_{10.5} - T_{12.3})\} T_{FG} + a_8$</td></tr><tr><td>C1</td><td>$SST = a_1 T_{10.5} + a_2 T_{10.5} S_\theta + a_3(T_{10.5} - T_{12.3}) + \{a_4(T_{10.5} - T_{8.6}) + a_5(T_{10.5} - T_{11.2})\} S_\theta + \{a_6(T_{10.5} - T_{8.6}) + a_7(T_{10.5} - T_{11.2}) + a_8(T_{10.5} - T_{12.3})\} T_{FG} + a_9$</td></tr><tr><td>C2</td><td>$SST = a_1 T_{10.5} + a_2(T_{10.5} - T_{12.3}) + \{a_3(T_{10.5} - T_{8.6}) + a_4(T_{10.5} - T_{11.2})\} S_\theta + \{a_5(T_{10.5} - T_{8.6}) + a_6(T_{10.5} - T_{11.2})\} S_\theta^2 + \{a_7(T_{10.5} - T_{8.6}) + a_8(T_{10.5} - T_{11.2}) + a_9(T_{10.5} - T_{12.3})\} T_{FG} + a_{10}$</td></tr><tr><td>C3</td><td>$SST = a_1 T_{10.5} + a_2(T_{10.5} - T_{12.3}) + \{a_3(T_{10.5} - T_{8.6}) + a_4(T_{10.5} - T_{11.2})\} S_\theta + a_5 S_\theta + \{a_6(T_{10.5} - T_{8.6}) + a_7(T_{10.5} - T_{11.2}) + a_8(T_{10.5} - T_{12.3})\} T_{FG} + a_9$</td></tr><tr><td>C4</td><td>$SST = a_1 T_{10.5} + a_2 T_{10.5} S_\theta + a_3(T_{10.5} - T_{12.3}) + \{a_4(T_{10.5} - T_{8.6}) + a_5(T_{10.5} - T_{11.2})\} S_\theta + \{a_6(T_{10.5} - T_{8.6}) + a_7(T_{10.5} - T_{11.2})\} S_\theta^2 + \{a_8(T_{10.5} - T_{8.6}) + a_9(T_{10.5} - T_{11.2}) + a_{10}(T_{10.5} - T_{12.3})\} T_{FG} + a_{11}$</td></tr><tr><td>C5</td><td>$SST = a_1 T_{10.5} + a_2 T_{10.5} S_\theta + a_3(T_{10.5} - T_{12.3}) + \{a_4(T_{10.5} - T_{8.6}) + a_5(T_{10.5} - T_{11.2})\} S_\theta + a_6 S_\theta + \{a_7(T_{10.5} - T_{8.6}) + a_8(T_{10.5} - T_{11.2}) + a_9(T_{10.5} - T_{12.3})\} T_{FG} + a_{10}$</td></tr><tr><td>C6</td><td>$SST = a_1 T_{10.5} + a_2(T_{10.5} - T_{12.3}) + \{a_3(T_{10.5} - T_{8.6}) + a_4(T_{10.5} - T_{11.2})\} S_\theta + \{a_5(T_{10.5} - T_{8.6}) + a_6(T_{10.5} - T_{11.2})\} (\sec\theta - 1)^2 + a_7 S_\theta + \{a_8(T_{10.5} - T_{8.6}) + a_9(T_{10.5} - T_{11.2}) + a_{10}(T_{10.5} - T_{12.3})\} T_{FG} + a_{11}$</td></tr><tr><td>C7</td><td>$SST = a_1 T_{10.5} + a_2 T_{10.5} S_\theta + a_3(T_{10.5} - T_{12.3}) + \{a_4(T_{10.5} - T_{8.6}) + a_5(T_{10.5} - T_{11.2})\} S_\theta + \{a_6(T_{10.5} - T_{8.6}) + a_7(T_{10.5} - T_{11.2})\} S_\theta^2 + a_8 S_\theta + \{a_9(T_{10.5} - T_{8.6}) + a_{10}(T_{10.5} - T_{11.2}) + a_{11}(T_{10.5} - T_{12.3})\} T_{FG} + a_{12}$</td></tr><tr><td>C8</td><td>$SST = a_1 T_{10.5} + a_2 T_{10.5} S_\theta + a_3(T_{10.5} - T_{12.3}) + \{a_4(T_{10.5} - T_{8.6}) + a_5(T_{10.5} - T_{11.2})\} S_\theta + \{a_6(T_{10.5} - T_{8.6}) + a_7(T_{10.5} - T_{11.2})\} S_\theta^2 + a_8(\sec\theta - 1) + a_9 S_\theta^2 + \{a_{10}(T_{10.5} - T_{8.6}) + a_{11}(T_{10.5} - T_{11.2}) + a_{12}(T_{10.5} - T_{12.3})\} T_{FG} + a_{13}$</td></tr></tbody></table>

IV. DISCUSSION

A. Geometrically-driven Path Length and Spatial Scale

The GK-2A SST algorithm, similar to the GOES-R SST algorithm, incorporates the impact of the SZA in the SST formulation as indicated in (2). However, as previously analyzed, it is consistently observed that SST tends to be overestimated at high SZAs. This overestimation occurs because as the SZA increases, so does the optical depth of the atmosphere through which the emitted radiation from the ocean surface must pass, enhancing the influence of Earth's atmosphere on the radiation reaching the satellite. Since the optical thickness between the top of the atmosphere (TOA) and the satellite is effectively zero, we set the TOA at approximately 11 km instead of the GK-2A altitude of 36,000 km and roughly investigated the path length. Directly beneath the satellite, the TOA is about 11 km, but it increases toward the edges, reaching about 37.9 km at an SZA of 65 degrees. As the distance from the nadir increases, the geometric atmospheric path

contributing to the optical thickness becomes more than three times longer. This significantly increases the likelihood of atmospheric gases and water vapor absorbing and scattering radiation, thereby increasing optical depth and potentially introducing errors in SST calculations.

Further investigation into the size of each grid revealed that at the nadir point, the resolution is 2 km with an area of 4 km², whereas within an SZA of 65 degrees, the area increases to 13 km², more than tripling in size. Particularly, as the pixel area increases, if oceanic fronts with rapid temperature changes or small-scale SST variations are located within these grids, significant discrepancies between satellite-derived SSTs and in-situ measurements are inevitable [64]. Especially, since the Rossby deformation radius is a function of latitude and oceanic phenomena tend to occur proportionally to this radius, the potential for larger errors increases significantly at higher latitudes with large SZAs. For example, the radius is greatly reduced to less than 4 km in the northern part of the East Sea in winter [65].
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<caption>TABLE V</caption>

STATISTICS OF GK-2A SST ERRORS (RMSD, BIAS, STANDARD DEVIATION (STD), ANOMALY CORRELATION COEFFICIENT OF SSTA, SCATTER INDEX) USING ORIGINAL MSST FORMULATION AND ADDITIONAL ONES FROM C1 TO C8 THROUGH THE CONSIDERATION OF SATELLITE ZENITH ANGLE. THE SHADED COLORS REPRESENT THE ERRORS WITH LOWEST ERROR OUT OF THE EIGHT FORMULATIONS

<table><thead><tr><th rowspan="2"></th><th colspan="2">RMSD (K)</th><th colspan="2">Bias (K)</th><th colspan="2">STD (K)</th><th colspan="2">Anomaly Corr. Coef.</th><th colspan="2">Scatter Index</th></tr><tr><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th><th>Day</th><th>Night</th></tr></thead><tbody><tr><td>MSST</td><td>0.5485</td><td>0.5528</td><td>0.0138</td><td>-0.0476</td><td>0.5483</td><td>0.5507</td><td>0.7823</td><td>0.7561</td><td>0.0255</td><td>0.0252</td></tr><tr><td>C1</td><td>0.5002</td><td>0.5101</td><td>0.0316</td><td>-0.0445</td><td>0.4992</td><td>0.5081</td><td>0.8110</td><td>0.7829</td><td>0.0232</td><td>0.0232</td></tr><tr><td>C2</td><td>0.5125</td><td>0.5211</td><td>0.0201</td><td>-0.0550</td><td>0.5121</td><td>0.5182</td><td>0.8053</td><td>0.7782</td><td>0.0238</td><td>0.0237</td></tr><tr><td>C3</td><td>0.4907</td><td>0.4966</td><td>0.0434</td><td>-0.0303</td><td>0.4888</td><td>0.4957</td><td>0.8148</td><td>0.7886</td><td>0.0228</td><td>0.0226</td></tr><tr><td>C4</td><td>0.4928</td><td>0.5068</td><td>0.0167</td><td>-0.0578</td><td>0.4925</td><td>0.5035</td><td>0.8147</td><td>0.7852</td><td>0.0229</td><td>0.0231</td></tr><tr><td>C5</td><td>0.4897</td><td>0.4973</td><td>0.0435</td><td>-0.0310</td><td>0.4878</td><td>0.4963</td><td>0.8156</td><td>0.7884</td><td>0.0227</td><td>0.0227</td></tr><tr><td>C6</td><td>0.4860</td><td>0.4941</td><td>0.0315</td><td>-0.0397</td><td>0.4849</td><td>0.4925</td><td>0.8165</td><td>0.7900</td><td>0.0226</td><td>0.0225</td></tr><tr><td>C7</td><td>0.4815</td><td>0.4940</td><td>0.0260</td><td>-0.0461</td><td>0.4808</td><td>0.4918</td><td>0.8190</td><td>0.7903</td><td>0.0224</td><td>0.0225</td></tr><tr><td>C8</td><td>0.4794</td><td>0.4921</td><td>0.0231</td><td>-0.0476</td><td>0.4789</td><td>0.4898</td><td>0.8200</td><td>0.7913</td><td>0.0224</td><td>0.0224</td></tr></tbody></table>

## B. Additional Consideration of Satellite Zenith Angle in SST Formulation

Given the significant influence of the SZA on the accuracy of SST retrievals, it is crucial to integrate more comprehensive adjustments for this parameter in the SST formulation. This section discusses the adjustments and enhancements made to the GK-2A SST retrieval algorithm to better account for the effects of the SZA, particularly at high angles where discrepancies are most pronounced. We explore various formulations that incorporate different terms related to the zenith angle, examining their impacts on the accuracy of the SST measurements. These modifications aim to refine the existing models and reduce the error margins in satellite-derived SSTs across all viewing angles.

The GK-2A SST retrieval formulation incorporates the impact of SZA ($\theta$) by including a term $sec\theta - 1$ ($S_\theta$) as specified in MSST (4). Despite this, analysis has consistently shown that SST tends to be overestimated at high latitude $\theta$ values. This overestimation occurs as the SZA increases, enhancing the atmospheric optical depth through which the radiated energy from the ocean surface passes. To further investigate this, the study explored the effect of SZA by experimenting with various terms involving the $\theta$ term as shown in Table IV. The first formulation (C1) considered the $S_\theta$ for the 10.5 µm channel only. As discussed and observed in Fig. 11, SST errors exponentially increased at SZA above 50°, indicating the need to account for non-linear effects related to $\theta$ in the formulation. The C2 formulation accounted for this by including squared terms for the BTD between the 10.5–8.6 µm and 10.5–11.2 µm channels. C3, similar to the OSISAF SEVIRI formulation [66], considered the $S_\theta$ term independently without quadratic components. C4 included squared terms for $\theta$. C5 reflected the $S_\theta$ effect in the T10.5 µm term and also included an independent $S_\theta$ term. C6 incorporated squared terms, and C7 expressed the effects of SZA using three terms. The final formulation, C8, considered both linear and non-linear terms related to SZA, incorporating a total of five terms.

For each formulation from C1 to C8 as presented in Table IV, coefficients were derived using the previous month's satellite-in situ matchup data, similar to the current real-time GK-2A SST retrieval process, and applied to current data to compute SST and perform accuracy verification. This method validated the accuracy of all eight formulations. Table V provides a comparative analysis of the impact of various formulations on the accuracy of SST retrieval through error statistics (RMSD, bias, STD, anomaly correlation coefficient, and scatter index), highlighting the most effective approaches in reducing errors related to SZA. RMSD results ranged from 0.4794 K to 0.5485 K for daytime and 0.4921 K to 0.5528 K for nighttime. Among the eight methods, C8, which considered both linearity and non-linearity of SZA, showed the lowest errors in terms of RMSD (0.4794 K, 0.4921 K) and STD (0.4789 K, 0.4898 K) for both day and night, respectively. Bias error was lowest during the day for C4 and at night for C3, however, overall differences with C8 were minimal. C8 also exhibited the lowest scatter indices (0.0224). Collectively, these results suggest that C8 is the most effective method for minimizing errors. While GK-2A has been producing real-time SSTs with a satisfactory RMSD of around 0.5 K since launch, the increase in errors at high SZA values justifies further consideration of SZA effects. It is advisable to include additional terms related to SZA, as shown in C8, in the existing formulation.

## C. Reduction of SST Discrepancies Related to Satellite Zenith Angle

The study investigated the extent to which the SST, corrected for the satellite zenith angle using C8, improves over the currently used MSST in real-time applications (Fig. 12). Fig. 12(a) and (b) display the spatial distribution of RMSD and bias for SST calculated with C8, compared to in-situ measurements for both day and night over the study period from 2019 to 2023. Notably, the relatively high RMSD and positive bias observed at the edges of Fig. 4 were significantly improved.
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<figure><img src="image_12.png" /><figcaption>**Fig. 12.** GK-2A/AMI-derived SST errors as compared with in-situ SSTs; (a) RMSD and (b) bias in a bin of 2° × 2° from July 2019 to August 2023. (c) RMSD and (d) bias as a function of satellite zenith angle (°), where the black line and blue line represent original GK-2A MSST and SST retrievals based on C8 formulation, respectively.</figcaption></figure>

As shown by the blue solid line in Fig. 12(c), the RMSD of the newly calculated SST improved by 0.02 K to 0.10 K across all SZAs compared to the original MSST RMSD, indicated by the black solid line (Fig. 12(c)). The difference in RMSD between the

new and original calculations gradually increased with larger SZAs, reaching a maximum improvement of approximately 0.1030 K around a zenith angle of 63°. The MSST bias showed a significant negative bias within -0.1 K for SZA below 60°, with a large positive bias of 0.12 K occurring near 60° (Fig. 12(d)). When using the C8 formulation, the high zenith angle bias near 60° was reduced to 0.057 K, representing an improvement of about 0.064 K. However, around 40 degrees, a positive bias of approximately 0.07 K was observed. Overall, the C8 formulation generally reduced the bias, with the exception of the region near 40°. Despite these points, considering both the bias and RMSD, it can be concluded that the approach accounting for the nonlinear effects of SZA generally contributed to reducing the overall GK-2A SST uncertainties. These findings underscore the importance of carefully considering the effects of SZAs in geostationary satellite SST retrievals.

Comparing the SST error scatter plot using the new formulation (Fig. 13(a)) with Fig. 9 (prior to correction), it becomes evident that the errors associated with SZAs have been significantly reduced. GK-2A and Himawari-8 observe similar regions. The Himawari-8 SST algorithm is based on a physics-based regression method that determines skin SST by modeling the radiative transfer processes and deriving an approximate solution to the radiative transfer equation. The coefficients used in this algorithm are pre-generated using radiative transfer models and numerical weather prediction data to reflect various atmospheric states and observational conditions. This approach aims to reduce the systematic errors inherent in conventional empirical algorithms.

<figure><img src="image_13.png" /><figcaption>**Fig. 13.** Scatter plots of RMSD (K) and bias (K) of the newly-derived GK-2A SSTs as compared to in-situ SSTs for the period (a) from July 2019 to August 2023 and (b) from January 2022 to December 2022, and (c) SST error plot of Himawari-8 SSTs from January 2022 to December 2022. The radius of dotted circles represents the mean SST differences (radius = $\sqrt{(bias)^2 + (RMSD)^2}$) and the colors represent satellite zenith angle (°).</figcaption></figure>
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However, in contrast, a comparison of error statistics for 2022 data from GK-2A and Himawari-8 shows that the SZA effect is considerably improved in GK-2A (Fig. 13(b)). In contrast, Himawari-8 SST errors exhibit a notable linear increase in negative bias and RMSD as the SZA increases (Fig. 13(c)). Although direct comparison is challenging because Himawari-8 targets skin SST, these results also highlight the necessity of addressing the SZA effect when deriving SSTs from geostationary satellites. One possible explanation for the high bias errors of Himawari-8 SST retrievals may be ascribed to cloud contamination due to imperfect cloud masking at high SZAs. This issue related to cloud contamination should be further studied.

V. CONCLUSION

In this study, we validated the quality of SST products from the GK-2A satellite over a period of 4 years and 2 months, from 2019 to 2023, and analyzed the characteristics of their bias and uncertainties. The validation results showed that the GK-2A SST met the target uncertainty of approximately 0.5K. However, seasonal variations, latitudinal changes, particularly amplified errors in high latitudes, and contrasting wind speed biases between day and night were observed. Additionally, SST bias and uncertainties increased linearly with the development of ocean fronts, and there was a tendency for the GK-2A SST to be underestimated in dry atmospheric conditions with low water vapor content and overestimated in humid conditions. The effect of the SZA was found to be more significant than these tendencies. At zenith angles greater than 50°, SST errors nonlinearly amplified regardless of day or night. To better account for the effects of SZAs in SST formulation, we tested eight different formulations. The optimal SST formula, which showed the highest accuracy, included terms proportional to T10.5, BTD terms, and both linear and nonlinear terms for the SZA.

Satellite SST data are used globally to create daily SST composite products, which are distributed and successfully utilized by various agencies. In South Korea, GK-2A SST data have been widely used by the KMA and marine-related agencies. The SST data are also used for data assimilation in oceanic and atmospheric numerical models to reduce biases. The analysis indicates that larger SZAs increase the optical thickness, resulting in positive biases and higher RMSDs in SST retrievals. This means that SSTs are likely to be overestimated in the edge regions of geostationary satellite images where the SZA exceeds 50°. In high latitudes, the imperfection in cloud detection, such as residual cloud cover or subpixel variability can also contribute to the part of the amplification of the SST uncertainties. All of these uncertainties can significantly impact the results of data assimilation processes in numerical models. Therefore, it is crucial to minimize these errors in SST retrievals before using them for various purposes, such as SST composites and data assimilation in numerical models. When clouds are present, making it difficult to observe the ocean using infrared satellites, microwave satellites play a crucial role. Although polar-orbiting microwave satellites have relatively low observation frequencies of 1–2 times per day, geostationary satellites have much higher observation frequencies, making significant

contributions to daily SST composites. To improve the accuracy of GK-2A SST, it is necessary to enhance the formulation by considering both linear and nonlinear effects of the SZA. Satellite SST data are actively used in various fields, including recent studies on marine heat waves. This study identified various factors contributing to the accuracy of GK-2A geostationary SST and emphasized the importance of the SZA. These findings are expected to help improve the accuracy of not only GK-2A but also other geostationary satellite SST products by reconsidering the effects of the SZA.

In conclusion, the GK-2A SST has demonstrated significant potential as a valuable data source for improving the accuracy of global daily SST composites, particularly in the East Asian region. By accounting for the nonlinear effects of SZA, the new formulations have effectively reduced both bias and RMSD, enhancing the overall reliability of the GK-2A SST data. As a result, the GK-2A SST is expected to play a role in contributing to global SST datasets, supporting a wide range of oceanographic and meteorological applications.

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<figure><img src="image_14.png" /></figure>

**Kyung-Ae Park** (Member, IEEE) received the B.S. degree in earth science education, the M.S. degree in physical oceanography, and the Ph.D. degree in satellite oceanography from Seoul National University, Seoul, Korea, in 1987, 1989, and 1996, respectively. Since 2007,

she has been a Professor in the Department of Earth Science Education, Seoul National University. Her research interests include the study of physical processes and ocean dynamics of mesoscale oceanic phenomena using multi-satellite data and oceanic *in-situ* measurements at regional scales in the East/Japan Sea and the marginal seas of the northwest Pacific Ocean. She has long utilized a variety of satellite remote-sensing techniques (sea surface temperature, near-surface wind, chlorophyll-a concentration, suspended sediment, wave height, sea level, and SAR) in her researches and applied them for physical oceanographic applications including air-sea interaction and its linkage to climate change. Dr. Park received the Len Curtis Award of the International Journal of Remote Sensing, Remote Sensing and Photogrammetry Society's Annual Conference, U.K., in 2015. She is currently the

president of the Korean Earth Science Society and a member of the science team for the Group for High-Resolution Sea Surface Temperature (GHRsst).

<figure><img src="image_15.png" /></figure>

**Hye-Jin Woo** received the B.S. degree in earth science education, and M.S. and Ph.D. degrees in satellite oceanography from Seoul National University, Seoul, Korea, in 2011, 2013, and 2019, respectively. She is currently a postdoctoral researcher in the Department of Earth Science Education at Seoul National University. Her research

interests include physical oceanography using multisensor satellite data, primarily sea surface temperature, significant wave height, and ocean fronts.

<figure><img src="image_16.png" /></figure>

**Stephane Saux Picart** is based at the Centre National de Recherche Météorologique in Lannion, France (CNRM, CNRS, Météo-France) since 2014. After a PhD in the field of modelling the Soil-Vegetation-Atmosphere heat and water exchanges, he orientated his research

in the field of ocean remote sensing working initially on salinity retrieval in the context of the SMOS mission. He then worked more than 6 years at the Plymouth Marin Laboratory on ecosystem model validation and primary productivity using remote sensing data. He is now specialized in remote sensing of the sea surface temperature using data from the infra-red radiometers. He is leading a small team of researchers and technicians working for the OSI SAF (https://osis-saf.eumetsat.int/) project: developing, validating and distributing sea surface temperature products from a number of satellite programs such as Meteosat Second and Third Generation, MetOp, GOES and NOAA.

<figure><img src="image_17.png" /></figure>

**Anne O'Carroll** is an Ocean Remote Sensing Scientist based at EUMETSAT since 2009 and leads the Surface Temperature Radiometry team in the Marine Applications area. She received a BSc in Physics and Meteorology from the University of Reading in 1996 followed by

13 years as a research scientist in the Space Department of the UK civil service (DERA) and at the UK Met Office focusing on the research and validation of satellite sea surface temperatures. Her current activities focus on generating and validating satellite sea and sea-ice surface temperature data from EUMETSAT missions including the high-quality reference data from the Copernicus Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR), designing and leading
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projects for Fiducial Reference Measurements of sea and sea-ice surface temperature, and preparing for the next generation of satellite sea surface temperature missions. She has chaired the Group for High-Resolution Sea-Surface Temperature (GHRSST) since 2016.

<figure><img src="image_18.png" /></figure>

**Eun-Ha Sohn** received the B.S. degree in Atmospheric Sciences and an M.S degree in Environmental System from Busan National University, Republic of Korea, in 1996 and 1999, respectively. She is currently working at the National Meteorological Satellite Center, Republic

of Korea. Her expertise includes the development of technologies for climate change monitoring and the application of satellite-based meteorological information.

<figure><img src="image_19.png" /></figure>

**HuiTae Joo** received an M.S. in Oceanography from Busan National University in 2013 and a Ph.D. in the primary production of phytoplankton from the same university in 2016. He spent the two years as a postdoctoral research associate in the Climate Change Research

Division at the National Institute of Fisheries Science (NIFS), Busan. He is currently working in the Marine Remote Sensing Laboratory at NIFS, South Korea. He has experience in biochemical and biological oceanography, and its application to ocean remote sensing.

<figure><img src="image_20.png" /></figure>

**Joon-Soo Lee** received the Bachelor's degree in Earth Science Education and Master's degree in School of Earth and Environmental Sciences from Seoul National University, Republic of Korea, and the Ph.D. degree in Interdisciplinary Graduate School of Engineering Sciences,

Kyushu University, Japan, in 2007. He spent two and a half years as a postdoctoral researcher at National Research Institute of Fisheries Science, Japan. He is currently responsible for the research on adaptation to climate change in marine and fisheries sector at National Institute of Fisheries Science, Republic of Korea. He has experience in earth science, physical oceanography, numerical modeling, and its application to climate change adaptation.

<figure><img src="image_21.png" /></figure>

**Joon-Yong Yang** received the Ph.D. degree in Oceanography from Seoul National University in 2006. He spent 25 years as a researcher in National Institute of Fisheries Science, Republic of Korea. He has experience in data management, marine environmental impact assessment and

fisheries disaster response.