Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks

Kalpesh Patil,M Ravichandran,Subimal Ghosh,M C Deo

doi:10.1155/2013/302479

Abstract

Prediction of monthly mean sea surface temperature (SST) values has many applications ranging from climate predictions to planning of coastal activities. Past studies have shown usefulness of neural networks (NNs) for this purpose and also pointed to a need to do more experimentation to improve accuracy and reliability of the results. The present work is directed along these lines. It shows usefulness of the nonlinear autoregressive type of neural network vis-à-vis the traditional feed forward back propagation type. Neural networks were developed to predict monthly SST values based on 61-year data at six different locations around India over 1 to 12 months in advance. The nonlinear autoregressive (NAR) neural network was found to yield satisfactory predictions over all time horizons and at all selected locations. The results of the present study were more attractive in terms of prediction accuracy than those of an earlier work in the same region. The annual neural networks generally performed better than the seasonal ones, probably due to their relatively high fitting flexibility.

Highlights

The temperature of water at around 1 m below the ocean surface, commonly referred to as sea surface temperature (SST), is an important parameter to understand the exchange of momentum, heat, gases, and moisture across air-sea interface
A neural network in general consists of interconnected neurons, each acting as an independent computational element
Note that the total sample size was of 61 × 12 months as mentioned in the preceding section and out of it the sequence of past 24 months at every current time step was used as input to the network in a sliding window manner

Summary

Introduction

The temperature of water at around 1 m below the ocean surface, commonly referred to as sea surface temperature (SST), is an important parameter to understand the exchange of momentum, heat, gases, and moisture across air-sea interface. Like the air above it SST changes significantly over time, relatively less frequently due to a high specific heat. The changes in water temperature over a vertical are high at the sea surface due to large variations in the heat flux, radiation, and diurnal wind near the surface, and SST estimations involve considerable amount of uncertainty. There are a variety of techniques for measuring SST. These include the thermometers and thermistors mounted on drifting or moored buoys and remote sensing by satellites. In order to predict SST physicallybased as well as data driven methods are practiced. The latter type is many times preferred when site specific information is required and considering the convenience. Some investigators have in recent past applied this technique to predict the SST as described below

SST Predictions Using Neural Networks

The SST Data

Networks and Training

D L forward network

Testing of Networks and Results

Conclusions