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

Prediction of sea surface temperature (SST) is desired for several applications ranging from climate studies to maintenance of coastal eco-system. Such prediction with the help of artificial, or simply, neural network has by now fairly stabilized. However corresponding studies are mostly applicable only to a specified single location. In this study we have expanded them to cover an entire sea basin. The basin under consideration is Bay of Bengal (BoB) located on the east side of the Indian peninsula. We have predicted SST at the daily time scale using time series approach in which we feed a selected length of past daily SST observations to the neural network and derive the predicted value of SST at multiple lead times (days) as output. The gridded NOAA v2 high resolution dataset derived from satellites was used for this purpose. At every grid in the BoB feed forward back propagation type of neural network was developed. The networks were trained using 70% of data and tested with the help of remaining 30%. The performance in testing of such large spatial-scale networks was judged on the basis of the error statistics of correlation coefficient, ‘r’, and root mean square error, RMSE. The prediction skill of ANN models were found to be very good at shorter lead times (1-3 days) and reasonably good at higher lead times (4-7 days). Apart from that, these ANN models were also evaluated for their performance during extreme weather events which are peculiar to BoB region and found to be capturing such events in advance with sufficient time. Overall therefore it is claimed that the basin-scale neural networks developed in this study can not only carry out multiple time step predictions of daily SST at individual grid points simultaneously but can also predict basin scale weather phenomena in advance.