[논문]정규크리깅을 이용한 우리나라 주변해역 일평균 해수면온도 격자지도화 및 내삽정확도 분석

안지혜; 이양원

doi:10.7848/ksgpc.2022.40.1.51

정규크리깅을 이용한 우리나라 주변해역 일평균 해수면온도 격자지도화 및 내삽정확도 분석
Ordinary Kriging of Daily Mean SST (Sea Surface Temperature) around South Korea and the Analysis of Interpolation Accuracy 원문보기

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

안지혜 (Research Institute for Geomatics, Pukyong National University) , 이양원 (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)

초록
AI-Helper

해수면온도는 지구 시스템에서 가장 중요한 메커니즘의 하나인 대기-해양의 상호작용을 단적으로 나타내며, 기후변화를 이해하는 데 필수적인 해양 기상요소이다. 이에, 공백 없이 시공간해상도가 일정한 격자자료는 해수면온도연구에 있어 그 활용도가 매우 높다. 이 논문에서는 2020년 해양 실측자료 137개 지점으로부터 최적화된 베리오그램을 도출하고 이를 이용한 정규크리깅을 통해 우리나라 주변해역의 일평균 해수면온도 격자지도를 산출하고 그 정확도를 평가하였다. 베리오그램 최적화는 가중최소제곱법을 이용하였고, 내삽정확도 검증을 위하여 공간적인 치우침이 없도록 객관적인 샘플링 기준을 적용하여 암맹평가를 수행하였다. 4회에 걸친 암맹평가 결과, 평균제곱근오차 0.995~1.035℃, 상관계수 0.981~0.982의 상당히 높은 정확도를 나타냈다. 계절별로는 여름철의 정확도가 상대적으로 약간 낮게 나타났는데, 이는 태풍의 영향으로 인한 급격한 수온 변동 때문으로 사료된다. 또한 가까운 바다보다 먼 바다에서, 동해, 남해보다 서해에서 상대적으로 정확도가 높게 나타났는데, 이는 가까운 바다에서 종종 반폐쇄해 지형으로 인해 해수의 물리적인 특성에 차이가 발생할 수 있기 때문인 것으로 보인다. 향후에는 계절별, 해역별 특성을 반영하는 SST 추정기법의 개선이 필요할 것이며, 개선된 자료는 우리나라 주변해역의 고품질 SST 합성장을 산출하는 앙상블 멤버로 활용될 수 있을 것으로 기대한다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (31)

그림 Fig. 1. Study area (32.0N-39.0N and 122.5E-132.5E)
표 Table 1. Overview of in-situ SST observations
그림 Fig. 2. Workflow of this study
그림 Fig. 3. Concept of the weighted least squares for variogram optimization
그림 Fig. 4. Spatial distribution of training and validation points for Kriging: example of May 31, 2020
그림 Fig. 5. Scatter plots of Kriging SST for each round
표 Table 2. Accuracy statistics of the Kriging SST for each round
그림 Fig. 6. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Jan 1, 2020
그림 Fig. 7. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Feb 7, 2020
그림 Fig. 8. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Mar 6, 2020
그림 Fig. 9. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Apr 3, 2020
그림 Fig. 10. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on May 31, 2020
그림 Fig. 11. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Jun 5, 2020
그림 Fig. 12. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Jul 5, 2020
그림 Fig. 13. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Aug 22, 2020
그림 Fig. 14. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Sep 29, 2020
그림 Fig. 15. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Oct 22, 2020
그림 Fig. 16. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Nov 16, 2020
그림 Fig. 17. Spatial distribution of (a) input data for Kriging and (b) SST grid map by Kriging on Dec 13, 2020
그림 Fig. 18. Scatter plots of seasonal SST by Kriging
표 Table 3. Seasonal accuracy statistics of Kriging SST
표 Table 4. Statistics on the occurrence of typhoons affecting the Korean Peninsula (KMA, 2022)
그림 Fig. 19. Changes in SST due to the passing of Typhoon. (a) Track of Typhoon BAVI in August 2020, (b) SST time series at Ongjinsocheongcho in 2020
그림 Fig. 20. Spatial distribution of training and validation points by sea area on May 31, 2020
표 Table 5. Accuracy statistics of Kriging SST in the East Sea, South Sea, and West Sea
그림 Fig. 21. Scatter plots of Kriging SST in the East Sea, South Sea, and West Sea
그림 Fig. 22. Spatial distribution of Kriging validation data within/outside 1km of the coastline in 2020
그림 Fig. 23. Scatter plots of Kriging SST within/outside 1km of the coastline in 2020
그림 Fig. 24. Spatial distribution of observation stations near (a) Gamcheon Bay and Busan Bay, (b) Masan Bay and Jinhae Bay
표 Table 6. Accuracy statistics of Kriging SST within/outside 1km of the coastline in 2020
표 Table 7. Number and ratio of validation data by sea area

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