The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Pattern Analysis in East Asian Coasts by using Sea Level Anomaly and Sea Surface Temperature Data

해수면 높이와 해수면 온도 자료를 이용한 동아시아 해역의 패턴 분석

  • 황도현 (부경대학교 지구환경시스템과학부) ;
  • 정민지 (부경대학교 지구환경시스템과학부) ;
  • 김나경 (부경대학교 지구환경시스템과학부) ;
  • 박미소 (부경대학교 지구환경시스템과학부) ;
  • 김보람 (부경대학교 지구환경시스템과학부) ;
  • 윤홍주 (부경대학교 공간정보시스템공학과)
  • Received : 2021.03.14
  • Accepted : 2021.06.17
  • Published : 2021.06.30
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Abstract

In the ocean, it is difficult to separate the effects of one cause due to the multiple causes, but the self-organizing map can be analyzed by adding other factors to the cluster result. Therefore, in this study, the results of the clustering of sea level data were applied to sea surface temperature. Sea level data was clustered into a total of 6 nodes. The difference between sea surface temperature and sea level height has a one-month delay, which applied sea surface temperature data a month ago to the clustered results. As a result of comparing the mean of sea surface temperature of 140 to 150°E, where the sea surface temperature was variously distributed, in the case of nodes 1, 3, and 5, it was possible to find a meandering sea surface temperature distribution that is clearly distinguished from the sea level data. While nodes 2, 4 and 6, the sea surface temperature distribution was smooth. In this study, sea surface temperature data were applied to the clustered results of sea level data, but later it is necessary to apply wind or geostrophic velocity data to compare.

해양에서는 여러 원인들이 복합적으로 작용하여 하나의 원인에 의한 효과를 분리하기 쉽지 않은데, 자기 조직화 지도는 군집 결과에 다른 인자를 추가하여 분석이 가능하다. 따라서 본 연구에서는 해수면 높이 자료로 군집된 결과를 해수면 온도에 적용시켜 분석해보았다. 해수면 높이 자료는 총 6개의 노드로 군집되었다. 해수면 온도와 해수면 높이의 차이에는 1개월 시간 지연이 있어 군집된 결과에 1개월 전 해수면 온도 자료를 적용시켰다. 해수면 온도가 다양하게 분포하였던 140 ~ 150°E의 평균 해수면 온도를 비교해본 결과 노드 1, 3, 5의 경우 해수면 높이 자료에서 뚜렷하게 구분되는 사행하는 모양의 해수면 온도 분포를 찾을 수 있었으나, 노드 2, 4, 6의 경우 해수면 온도 분포는 완만하게 나타났다. 본 연구에서는 해수면 높이 자료로 군집된 결과에 해수면 온도 자료를 적용해보았지만, 추후 바람이나 지형류 자료를 적용시켜 비교해볼 필요가 있다고 판단된다.

Keywords

References

  1. D. Hwang, S. Bak, U. Enkhjargal, M. Jeong, N. Kim, M. Park, B. Kim, and H. Yoon, "Study on the Front Detection Techniques by using Satellite Data," J. of the Korea Institute of Electronics Communication Sciences, vol. 15, no. 6, 2020, pp. 1201-1208.
  2. R. Grotjahn, R. Black, R. Leung, M. F. Wehner, M. Barlow, M. Bosilovich, A. Gershunov, W. J. Gutowski Jr., J. R. Gyakum, R. W. Katz, Y. Lee, Y. Lim, and Prabhat, "North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends," Climate Dynamics, vol. 46, 2016, pp. 1151-1184. https://doi.org/10.1007/s00382-015-2638-6
  3. Y. Jeong and K. Choi, "For Gene Disease Analysis using Data Mining Implement MKSV System," J. of the Korea Institute of Electronics Communication Sciences, vol. 13, no. 4, 2019, pp. 781-786.
  4. Y. Kim, "Development of advertising effect prediction model for celebrity models using Big Data," Master's Thesis, Hanyang University, 2019.
  5. T. Kohonen, Self-Organizing Maps. New York: Springer 2001.
  6. S. Jun and Y. Choi, "The applicability of the self-organizing map method for synoptic climatology studies over the Republic of Korea," J. of Climate Research, vol. 8, no. 1, 2013, pp. 1-11.
  7. Y. Liu, R. H. Weisberg, and C. N. K. Mooers, "Performance evaluation of the self-organizing map for feature extraction," J. of Geophysical Research, vol. 111, 2006, pp. C05018. https://doi.org/10.1029/2005JC003117
  8. D. B. Reusch and R. B. Alley, "North Atlantic climate variability from a self-organizing map perspective," J. of Geophysical Research, vol. 112, 2007, pp. D02104. https://doi.org/10.1029/2006JD007460
  9. B. C. Hewitson and R. G. Crane, "Self-organizing maps: applications to synoptic climatology," Climate Research, vol. 22, no. 1, 2002, pp. 13-26. https://doi.org/10.3354/cr022013
  10. D. Arribas-Bel, P. Nijkamp, and H. Scholten, "Multi-dimensional urban sprawl in Europe: a self-organizing map approach," Computers, Environment and Urban Systems, vol. 35, no. 4, pp. 263-275. https://doi.org/10.1016/j.compenvurbsys.2010.10.002
  11. W. Lim and K. Seo, "Investigation on Characteristics of Summetime Extreme Temperature Events Occurred in South Korea Using Self-Ogranizing Map," Atmosphere Korean Meteorological Society, vol. 28, no. 3, 2018, pp. 305-315.
  12. Y. Liu, R. H. Weisberg, and Y. Yuan, "Patterns of upper layer circulation variability in the South China Sea from Satellite altimetry using the self-organizing map," Acta Oceanologica Sinica, vol. 27, 2008, pp. 129-144.
  13. H. Ahn, J. Shin, C. Jeong, and J. Heo, "Assessing applicability of self-organizing map for regional rainfall frequency analysis in South Korea," J. of Korea Water Resources Association, vol. 51, no. 5, 2018, pp. 383-393. https://doi.org/10.3741/JKWRA.2018.51.5.383
  14. G. Taburet, A. Sanchez-Roman, M. Ballarotta, M. I. Pujol, J. Legeais, F. Fournier, Y. Faugere, and G. Dibarboure, "DUCAS DT 2018: 25 years of reprocessed sea level altimetry products," Ocean Science, vol. 15, no. 5, 2019, pp. 1207-1224. https://doi.org/10.5194/os-15-1207-2019
  15. F. Mertz, M. Pujol, and Y. Faugere, "Product User Manual for Sea Level SLA Products." CMEMS, 2017.
  16. GHRSST Science Team, "The Recommended GHRSST Data Specification (GDS) Revision 2.0 Technical Specifications," GHRSST International Project Office, 2010, pp. 120.
  17. S. Back, S. Kim, M. Jung, J. Roh, and S. Son, "Classification of Heat Wave Events in Seoul Using Self-Organizing Map," J. of Climate Change Research, vol. 9, no. 3, 2018, pp. 209-221. https://doi.org/10.15531/KSCCR.2018.9.3.209
  18. M. Beccali, M. Cellura, V. L. Brano, and A. Marvuglia, "Forecasting daily urban electric load profiles using artificial neural networks," Energy Conversion and Management, vol. 45, 2004, pp. 2879-2900. https://doi.org/10.1016/j.enconman.2004.01.006
  19. H. Speckmann, G. RAddatx, and W. Rosenstiel, "Considerations of geometrical and fractal dimension of SOM to get better learning results," In Proc. of the ICANN'94, Sorrento, Italy, May 1994, pp. 342-435.
  20. M. R. Islam, S. C. Sheridan, and C. C. Lee, "Using self-organizing maps to identify the South Asian seasonal cycle," Theoretical and Applied Climatology, vol. 137, 2018, pp. 1385-1401. https://doi.org/10.1007/s00704-018-2681-4
  21. K. Kiviluoto, "Topology preservation in self-organizing maps," Technical report A29, 1995.
  22. R. M. Gray, "Vector quantization," IEEE ASSP Magazine, vol. 4, no. 2, 1984, pp. 4-29. https://doi.org/10.1109/MASSP.1987.1165576
  23. D. Hwang, S. Bak, M. Jeong, N. Kim, M. Park, B. Kim, and H. Yoon, "Study on Dimensionality Reduction for Sea-level Variations by using Altimetry Data around the East Asia Coasts," Korean J. of Remote Sensing, vol. 37, no. 1, 2021, pp. 85-95. https://doi.org/10.7780/KJRS.2021.37.1.7
  24. S. Oh, S. Jang, D. Kim, and H. Yoon, "Temporal and Spatial Variations of SL.SST in the Korean Peninsula by Remote Sensing," J. of Fisheries and Marine Sciences Education, vol. 24, no. 2, 2012, pp. 333-345. https://doi.org/10.13000/JFMSE.2012.24.2.333