Seven-day sea surface temperature prediction using a 3DConv-LSTM model

• Published in: Frontiers in Marine Science
• Main Authors: Li Wei, Lei Guan
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: Long short-term memory; operational SST and sea ice analysis (OSTIA); sea surface temperature (SST); spatiotemporal prediction; three-dimensional convolution; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; Sea ice
• Online Access: https://doi.org/10.3389/fmars.2022.905848; https://doaj.org/article/73909f30323248d09cfece58425d8857

Due to the application demand, users have higher expectations for the accuracy and resolution of sea surface temperature (SST) products. Recent advances in deep learning show great advantages in exploiting massive ocean datasets, and provides opportunities for investigating regional SST predictions in an efficiency approach. However, for deep learning-based SST prediction to be adopted by users, the output must be accurate. This paper investigates the 7-day SST prediction over the China seas and their adjacent waters at a 0.05° spatial resolution. To improve the prediction’s accuracy, we designed a deep learning model combining the three-dimensional convolution and long short-term memory under multi-input multi-output strategy. The Operational SST and Sea Ice Analysis (OSTIA) SST anomaly was used as training data. To test the model prediction ability, we verified the predicted results with the Sub-seasonal to Seasonal (S2S) prediction data from 2015 to 2019. Validation of the predicted SSTs using the OSTIA test datasets show that the root-mean-square error increases from 0.27°C to 0.53°C during the 1- to 7-day lead time, with predictability decreases from southeast to northwest in the study area. Furthermore, the comparison of predicted SST and S2S data with Argo shows that our model is slightly more accurate, which can achieve -0.08°C bias, with a standard deviation of 0.35°C for a 1-day lead time and -0.07°C bias, with a standard deviation of 0.59°C for a 7-day lead time. The results indicate that the proposed deep learning model is accurate and can be applied in regional daily SST prediction.