Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Abstract

The prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

Purely satellite data–driven deep learning forecast of complicated tropical instability waves

“…Recently, considerable attention has focused on using both site-specific and site-independent artificial neural network models to forecast SSTs. A site-specific model uses separate artificial neural network models to predict SSTs at different sites (7). The model considers the site difference but has high computational costs and requires sufficient training data at each location.…”

Dual Memory LSTM with Dual Attention Neural Network for Spatiotemporal Prediction

“…However, these methods [ 19 , 20 , 21 , 22 ] depend on local representations to some extent, which cannot get excellent performance on prediction task. An artificial neural network [ 23 ] has been proposed to model the unique properties of spatiotemporal data and derives a more powerful modeling capability to spatiotemporal data. A spatiotemporal prediction system [ 24 ] has been developed to focus on spatial modeling and reconstructing the complete spatio-temporal signal.…”

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

“…In addition, SST plays an important role in the occurrence of the El Niño Southern Oscillation (ENSO) phenomenon (Annamalai et al 2005;Gordon 1986;Nicholls 1984). There is strong evidence that SST anomalies directly influence extreme hydrological events such as droughts (Amouamouha and Gholikandi 2018;Salles et al 2016), and multiple studies have indicated a strong correlation between SST anomalies and hurricanes (Gholikandi et al 2018;Jiang et al 2018a;Kahira et al 2018;Patil and Deo 2018).…”