Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques

The prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the Meyer wavelet with discrete approximation worked more satisfactorily than other wavelets.