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Predicting wind field in the Bay of Bengal from scatterometer observations using genetic algorithm
Author(s) -
Sharma Rashmi,
Sarkar Abhijit,
Agarwal Neeraj,
Kumar Raj,
Basu Sujit
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2006gl028288
Subject(s) - empirical orthogonal functions , scatterometer , singular spectrum analysis , bay , principal component analysis , bengal , meteorology , series (stratigraphy) , satellite , algorithm , field (mathematics) , footprint , remote sensing , wind speed , environmental science , climatology , geology , computer science , mathematics , singular value decomposition , oceanography , geography , artificial intelligence , engineering , aerospace engineering , paleontology , pure mathematics
A technique based on genetic algorithm (GA) is applied for predicting wind field in the Bay of Bengal (BOB) using satellite scatterometer observations. Empirical orthogonal function (EOF) analysis is used for compressing the spatial variability into a set of eigenmodes. The time series of each principal component (PC) is subjected to singular spectrum analysis (SSA) and GA is applied to the resulting filtered time series. The forecast PCs are weighted by the spatial eigenmodes for computing forecast wind fields. Predictions made up to 5 days in advance are found to be superior to forecast by persistence method.