By Topic

Data-Adaptive Prediction of Sea-Surface Temperature in the Arabian Sea

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Neetu ; Meteorol. & Oceanogr. Group, Space Applic. Center, Ahmedabad, India ; Sharma, R. ; Basu, S. ; Sarkar, A.
more authors

A nonlinear data-adaptive approach known by the name of genetic algorithm has been proposed for predicting satellite-observed sea-surface temperature (SST) in the Arabian Sea. A preliminary empirical orthogonal function (EOF) analysis has been carried out to separate the temporal variability from the spatial variability, and the algorithm has been applied to the time series of the principal components (PCs). The algorithm finds explicit analytical forecast equations that are later used to forecast the PCs. Afterward, predicted SSTs have been reconstructed using the predicted PCs and precomputed EOFs. Performance of the forecast has been evaluated by comparing it with persistence forecast, and it has been found that the algorithm is able to improve upon persistence forecast for the lead times of two to four weeks.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 1 )