A new technique for forecasting surface wind field from scatterometer observations: A case study for the Arabian Sea

The possibility of predicting ocean-surface wind field a few days ahead from satellite scatterometer observations in the Arabian Sea has been explored in this paper. The prediction technique is based on a combination of empirical orthogonal function (EOF) analysis and genetic algorithm (GA). The space-time distributed satellite data (zonal or meridional wind field) have been decomposed into a set of spatial eigenmodes ranked by their temporal variance. The associated temporal amplitude functions have been used by the GA for carrying out forecasts with lead times varying from one to five days. The GA finds the analytical equations that best describe the behavior of the different temporal amplitude functions in the EOF decomposition. Later, the predicted wind field has been generated as a linear combination of the dominant spatial modes weighted by the corresponding predicted amplitudes. The technique has been tested using independent validation data sets. It has been further tested by comparing the forecast fields with buoy data. The performance of GA is comparable to that of persistence forecast for the first two days of forecast, while it is better than that of persistence for three- to five-day-ahead forecasts.