Paper Info
Title
Deep Learning Models to Predict Sea Surface Temperature in Tohoku Region
Abstract
The prediction of sea surface temperature (SST) is a challenging task, especially for regions with high SST variability. Such predictions are either achieved by physics-based models, which often yield poor predictions and are computationally intensive, or by using data-driven methods, which are skillful and computationally less intensive. However, recent machine learning studies exploring SST prediction have not included the important meteorological parameters governing SST variability. Therefore, in this study, we propose various of deep learning (DL) models trained using past meteorological features to predict day-ahead SST. The proposed models include a deep multilayer perceptron (deep MLP), long short-term memory network (LSTM) and spatial 2-dimensional convolutional neural network (spatial 2D CNN). We explore the potential of the proposed DL models for day-ahead SST prediction across different locations in the Tohoku region (Japan's east coast), including in situ validation. Evaluation of these DL models' prediction skills suggests that the spatial 2D CNN's are highly skillful at coastal locations, whereas at offshore locations, equal prediction skills were noted from the deep MLP and LSTM. We further attempted to improve the spatial 2D CNN by including past SST features, and such improvisation showed very low errors ranging from 0.35 degrees C to 0.75 degrees C and high correlation skill from 0.64 to 0.96. These improved skills were also compared with persistent model (PM) skills using RMSE and Correlation (RC) phase diagram, where we found that improved skills are consistently better than PM skills. In addition, we extracted features from the spatial 2D CNN to understand the reason underlying such improved skills, and we noted that the proposed DL model successfully captured the major meteorological and oceanic features governing SST variability. This led us to conclude that the proposed DL models are capable of producing highly reliable SST predictions, and may be equally applicable to other study regions.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3167176
IEEE ACCESS
Keywords
DocType
Volume
Ocean temperature, Predictive models, Feature extraction, Data models, Training, Convolutional neural networks, Sea surface, Deep multi-layer perceptron (Deep MLP), spatial 2-dimensional convolutional neural network (spatial 2D CNN), long short-term memory networks (LSTM), deep learning for SST prediction, meteorologic parameter forced SST prediction
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Kalpesh R. Patil100.34
Masaaki Iiyama21714.23