• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.360-366
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• 2009

AQUA/AMSR-E 인공위성 자료를 활용하여 3차원 최적내삽 해수면온도 합성장을 생산하였고 시간평균장과 비교하여 문제점과 한계점을 기술하였다. 3-D SST 합성장은 북태평양 중앙부에서 전체적으로 $0.05^{\circ}C$ 이하의 작은 오차를 보였으나, 위성 결측이 있는 연안에서는 $0.4^{\circ}C$ 이상의 비교적 큰 오차를 유발하였다. 강한 강수나 구름으로 인한 결측이 있는 부분에서는 $0.1\sim0.15^{\circ}C$에 달하는 오차를 보였다. 시간평균장과 비교한 결과, 구름 부근의 화소에서는 해수면온도를 낮게 계산하는 경향이 있었으며, 해수면온도의 공간적 구배를 감소시키는 평활화가 전체적으로 나타났다. 저위도에서 OI SST는 실제 해수면온도에는 없는 불연속성을 만드는 경향이 있었고, 이는 OI 과정에서 사용한 윈도우의 크기와 해양 현상의 수평 규모가 위도에 따라 변화하는데서 기인하였다. 현상의 공간 규모의 척도인 로스비 내부 변형 반경은 북태평양에서 O(1) 정도로 위도에 따른 공간적 변화가 큰 것으로 나타났다. 본 연구는 SST 합성장 생산 과정에 위도와 해수의 수직적 밀도 구조와 밀접한 관련이 있는 해양 현상의 수평적 규모의 시공간적 변동 특성을 고려해야 함을 제시한다.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.613-623
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• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.3
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• pp.197-202
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• 2011

This study introduces a technique to merge three different sea surface temperature(SST) data obtained from multi-satellite sensors. NGSST algorithm, the most popular method of related society, estimates a center pixel of target SST using temporal and spatial correlations, excluding SST accuracies according to sensing methods or properties of satellites. We suggest a merging method of SST to consider the accuracy by satellite or sensor with a comparison with NGSST method. The data used for a merged daily SST with spatial resolution of 5 km was applied from three different satellite sensors such as MODIS, AVHRR and AMSR-E from April 2 to 4, 2011 around the southern coast of Korea. Results of the comparisons showed that the new method is higher than the NGSST method and its STDEV represents a comparatively low value. In future we are planning to compare and analyze the datasets during the daytime as well as nighttime over total cycle of the day.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Journal of the Korean earth science society
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• v.30 no.2
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• pp.223-233
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• 2009

Three-dimensional (3-D) optimally-interpolated sea surface temperature (SST) field was produced by using AQUA/AMSR-E satellite data, and its limitations were described by comparing the temporal average of sea surface temperatures. The 3-D OI (Optimum Interpolation) SST showed a small error of less than $0.05^{\circ}C$ in the central North Pacific, but yielded large errors of greater than $0.4^{\circ}C$ at the coastal area where the satellite microwave data were not available. OI SST composite around pixels with no observation due to heavy rainfall or cloudy pixels had estimation errors of $0.1-0.15^{\circ}C$. Comparison with temporal means showed a tendency that overall OI SSTs were underestimated around heavy cloudy pixels and smoothed out by reducing the magnitude of SST fronts. In the low-latitude areas near the equator, OI SST field produced discontinuity, originated from the window size for the OI procedure. This was mainly caused by differences in the spatial scale of oceanic features. Infernal Rossby deformation radius, as a measure of spatial stale, showed dominant latitudinal variations with O(1) difference in the North Pacific. This study suggests that OI SST methodology should consider latitudinally-varying size of window and the characteristics of spatial scales of oceanic phenomena with substantial dependency on latitude and vertical structure of density.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.555-567
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• 2018

In order to identify the characteristics of sea surface temperature (SST) differences between microwave SST from GCOM-W1/AMSR2 and in-situ measurements in the western coast of Korea, a total of 6,457 collocated matchup data were produced using the in-situ temperature measurements from marine buoy stations (Deokjeokdo, Chilbaldo, and Oeyeondo) from July 2012 to December 2017. The accuracy of satellite microwave SSTs was presented by comparing the ocean buoy data of Deokjeokdo, Chilbaldo, and Oeyeondo stations with the AMSR2 SST data more than five years. The SST differences between the microwave SST and the in-situ temperature measurements showed some dependence on environmental factors, such as wind speed and water temperature. The AMSR2 SSTs were tended to be higher than the in-situ temperature measurements during the daytime when the wind speed was low ($<6ms^{-1}$). On the other hand, they showed positive deviation increasingly as the wind speed increased for nighttime. In addition, increasing tendency of SST differences was related to decreasing sensitivity of microwave sensors at low temperatures and data contamination by land. A monthly analysis of the SST difference showed that unlike the previous trend, which was known to be the largest in winter when strong winds were blowing, the SST difference was largest in summer in Deokjeokdo and Chilbaldo buoy stations. This seemed to be induced by differential tidal mixing at the collocated matchup points. This study presented problems and limitations of the use of microwave SSTs with high contribution to the SST composites in the western coastal region off the Korean peninsula.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.