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

Abstract

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.

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

“…Compared to LSTM and CNN-LSTM, CNN had slightly lower errors but significantly higher learning speed. However, there are different findings in the literature that evaluated these models' performance for temperature prediction [20,38,[43][44][45][46]. Moreover, as CNN-LSTM is a hybrid model that exploits the benefits of both CNN and LSTM to improve its prediction accuracy, it was expected that CNN-LSTM would perform more accurately than the other models [34].…”

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

“…In recent years, increasing interest has been given to machine learning (ML) techniques, even though, in contrast to the dynamical model, ML techniques do not know when they are violating the laws of physics (Buizza et al, 2022). As a "learning from data" approach, machine learning has the advantages of computational efficiency, accuracy, transferability, flexibility, and ease-of-use in ocean forecasting studies (Boukabara et al, 2019;Li et al, 2020;Wei and Guan, 2022;Taylor and Feng, 2022). Moreover, they are also less prone to model bias errors (Jacox et al, 2020), and, beyond computational efficiency, ML techniques excel in approximating nonlinear functions (Hornik, 1991).…”