Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Abstract

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions.

Sea level variability and modeling in the Gulf of Guinea using supervised machine learning

“…The interconnectedness of these equations enables the LSTM to capture and store relevant information over time, addressing the vanishing gradient problem in traditional RNNs. LSTM networks have proven successful in time series data applications, such as sea state and sea level modeling and prediction 81 , 82 .…”

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

“…trained in other domains feasible. This section discusses applying transfer learning techniques in various tasks, including coastal identification, extending ENSO prediction horizons, accurate internal wave amplitude inversion, and capturing spatial evolution features of SST anomalies (SSTAs) and SSH anomalies (SSHAs) [49], [53], [148], [149].…”

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

“…Transfer learning has garnered widespread attention in various marine-related fields [50]. For instance, Miao et al established a transfer learning model for predicting sea surface temperature anomalies and sea surface height anomalies based on satellite remote sensing observational data [51]. Kumar et al constructed a TL model for wave characterization prediction using data from Mexico, Korea, and the United Kingdom for three regions [24].…”