Browse > Article
http://dx.doi.org/10.7780/kjrs.2020.36.5.3.7

Prediction of Sea Surface Temperature and Detection of Ocean Heat Wave in the South Sea of Korea Using Time-series Deep-learning Approaches  

Jung, Sihun (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Kim, Young Jun (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Park, Sumin (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_3, 2020 , pp. 1077-1093 More about this Journal
Abstract
Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.
Keywords
sea surface temperature; prediction; ocean heatwave; Korea peninsula; machine learning; time-series;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 CHoo, H.-S. and D.-S. KIM, 1998. The effect of variations in the Tsushima warm currents on the egg and larval transport of anchovy in the southern sea of Korea, Korean Journal of Fisheries and Aquatic Sciences, 31(2): 226-244. (in Korean with English abstract).
2 Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sensing of Environment, 116: 140-158.   DOI
3 Kim, S., S. Hong, M. Joh, and S.-K. Song, 2017. Deeprain: Convlstm network for precipitation prediction using multichannel radar data, 1711(202316): 1-4.
4 Kim, T.-H. and C.-S. Yang, 2019. Preliminary Study on Detection of Marine Heat Waves using Satellitebased Sea Surface Temperature Anomaly in 2017-2018, Journal of the Korean Society of Marine Environment & Safety, 25(6): 678-686. (in Korean with English abstract).   DOI
5 Kim, Y. J., H.-C. Kim, D. Han, S. Lee, and J. Im, 2020. Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks, The Cryosphere, 14: 1083-1104.   DOI
6 Korea Institute of Science and Technology (KIOST), 2013. Report on Development of Korea Operational Oceanographic System (KOOS), Korea Institute of Science and Technology, Busan, KR (in Korean).
7 Korea Institute of Science and Technology (KIOST), 2018. Press, Korea Institute of Ocean Science and Technology, http://www.kiost.ac.kr/synap/skin/doc.html?fn=BBS_201808290917391950.hwp&rs=/vieweresult/BBSMSTR_000000000075/, Accessed on Aug. 29, 2018 (in Korean).
8 Korea Meteorological Administration (KMA), 2019. Abnormal climate report in 2018, Korea Meteorological Administration, Seoul, KR (in Korean).
9 Lee, S-H., Y.-G. Kim, J.-T. Yoo, and S.-H. Song, 2018. Characteristics of egg and larval distributions and catch changes of anchovy in relation to abnormally high sea temperature in the South Sea of Korea, Journal of the Korean Society of Fisheries and Ocean Technology, 54(3): 262-270 (in Korean with English abstract).   DOI
10 Kwak, G.-H., M.-G. Park, C.-W. Park, K.-D. Lee, S.-I. Na, H.-Y. Ahn, and N.-W. Park, 2019. Combining 2D CNN and bidirectional LSTM to consider spatio-temporal features in crop classification, Korean Journal of Remote Sensing, 35(5-1): 681-692 (in Korean with English abstract).   DOI
11 Lins, I. D., M. Araujo, M. das Chagas Moura, M. A. Silva, and E. L. Droguett, 2013. Prediction of sea surface temperature in the tropical Atlantic by support vector machines, Computational Statistics & Data Analysis, 61: 187-198.   DOI
12 Martin, M., E. Fiedler, J. R. Jones, E. Blockley, A. McLaren, and S. Good, 2019. PRODUCT USER MANUAL For OSTIA Near Real Time Level 4 SST products over the global ocean: SST-GLOSST-L4-NRT-OBSERVATIONS-010-001, Copernicus.
13 Moon, J.-E. and C.-S. Yang, 2009. Analysis of abnormal sea surface temperature in the coastal waters of the Yellow Sea using satellite data for the winter season of 2004, Korean Journal of Remote Sensing, 25(1): 1-10 (in Korean with English abstract).   DOI
14 Mu, B., C. Peng, S. Yuan and L. Chen, 2019. ENSO Forecasting over Multiple Time Horizons Using ConvLSTM Network and Rolling Mechanism, Proc. of 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, Jul. 14-19, pp. 1-8.
15 NA, J.-Y., S.-K. HAN, and K.-D. CHO, 1990. A study on sea water and ocean current in the sea adjacent to Korea peninsula-expansion of coastal waters and its effect on temperature variations in the South Sea of Korea, Korean Journal of Fisheries and Aquatic Sciences, 23(4): 267-279 (in Korean with English abstract).
16 Seong, K.-T., J.-D. Hwang, I.-S. Han, W.-J. Go, Y.-S. Suh, and J.-Y. Lee, 2010. Characteristic for long-term trends of temperature in the Korean waters, Journal of the Korean Society of Marine Environment & Safety, 16(4): 353-360 (in Korean with English abstract).
17 Nan, F., H. Xue. and F. Yu, 2015. Kuroshio intrusion into the South China Sea: A review, Progress in Oceanography, 137: 314-333.   DOI
18 Park, K., F. Sakaida. and H. Kawamura, 2008. Oceanic skin-bulk temperature difference through the comparison of satellite-observed sea surface temperature and in-situ measurements, Korean Journal of Remote Sensing, 24(4): 273-287 (in Korean with English abstract).   DOI
19 Park, H.-W, S.-H. Lee, E.-J. Lee, Y.-S. Cho, Y.-S. Park, J.-H. Lee, and D.-H. Yoo, 2020. Short-term forecasting for sea surface temperature based on tidal observatory observations, Journal of the Korean Data And Information Science Society, 31(2): 255-271 (in Korean with English abstract).   DOI
20 Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007. Daily high-resolution-blended analyses for sea surface temperature, Journal of Climate, 20(22): 5473-5496.   DOI
21 Woo, H.-J. and K. Park, 2020. Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions, Remote Sensing, 12(10): 1592.   DOI
22 Xue, Y. and Leetmaa, A, 2000. Forecasts of tropical Pacific SST and sea level using a Markov model, Geophysical Research Letters, 27(17): 2701-2704.   DOI
23 Xiao, C., N. Chen, C. Hu, K. Wang, J. Gong, and Z. Chen, 2019a. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach, Remote Sensing of Environment, 233: 111358.   DOI
24 Cho, D., C. Yoo, J. Im, and D. H. Cha, 2020. Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas, Earth and Space Science, 7(4): 1-18.
25 Xiao, C., N. Chen, C. Hu, K. Wang, Z. Xu, Y. Cai, L. Xu, Z. Chen, and J. Gong, 2019b, A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data, Environmental Modelling & Software, 120: 104502.   DOI
26 Xingjian, S., Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Advances in Neural Information Processing Systems, 2015: 802-810.
27 Yang, Y., J. Dong, X. Sun, E. Lima, Q. Mu, and X. Wang, 2017. A CFCC-LSTM model for sea surface temperature prediction, IEEE Geoscience and Remote Sensing Letters, 15(2): 207-211.   DOI
28 Zhang, Q., H. Wang, J. Dong, G. Zhong, and X. Sun, 2017, Prediction of sea surface temperature using long short-term memory, IEEE Geoscience and Remote Sensing Letters, 14(10): 1745-1749.   DOI
29 Zhong, L., L. Hu. and H. Zhou, 2019. Deep learning based multi-temporal crop classification, Remote Sensing of Environment, 221: 430-443.   DOI
30 Chin, T. M., J. Vazquez-Cuervo, and E. M. Armstrong, 2017. A multi-scale high-resolution analysis of global sea surface temperature, Remote sensing of Environment, 200: 154-169.   DOI