Predicting wind field in the Bay of Bengal from scatterometer observations using genetic algorithm | Zendy

Sharma Rashmi | Zendy; Sarkar Abhijit | Zendy; Agarwal Neeraj | Zendy; Kumar Raj | Zendy; Basu Sujit | Zendy

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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 , series (stratigraphy) , meteorology , algorithm , field (mathematics) , satellite , wind speed , footprint , remote sensing , environmental science , climatology , geology , computer science , mathematics , singular value decomposition , oceanography , geography , physics , artificial intelligence , paleontology , pure mathematics , astronomy

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.

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