Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks

Abstract

Sea surface temperature (SST) significantly affects the processes of air–sea interactions, and, thus, forms an important indicator of climate changes. In SST predictions, the approach of artificial neural networks (ANNs) is data-driven, unlike that of the numerical models, which are physics-based. In this letter, Operational SST and Ice Analysis (OSTIA) data set were used for training ANN models and verifying prediction results. Typically, time series SST data of a certain period were directly used for ANN’s training. To reduce the prediction errors caused by SST variations, the authors propose to separate SST time series data into climatological monthly mean and monthly anomaly data sets and construct two neural network models. The combination of these two models gives the final SST prediction results. This method was used for 12-month lead time SST prediction in the South China Sea. The average bias and standard deviation between the predicted SST and OSTIA SST are −0.16 °C and 0.37 °C, respectively. The percentage of SST difference between the predicted SST and OSTIA SST, within ±0.5 °C and ±1 °C, is 71.24% and 95.22%, respectively. The results indicate that the proposed training method gives good prediction accuracy.