SOME NUMERICAL PREDICTION METHODS FOR THE WIND SPEED IN THE SEA BASED ON ERS-1 SCATTEROMETER WIND DATA

Abstract

Three functional models, polynomial, autoregression and autoregressive moving average models, are fitted and compared in fitting and predicting the sea wind speeds based on the ERS-1 radar altimeters. The robust maximum likelihood theory is introduced in fitting the functional models. To purify the satellite scan data for the sea Hinds and construct the data sets with equivalent intervals, we deleted the data which responds to the ice and land, then using the retained pure sea data sets to fit a simple polynomial and interpolate the sea data that was deleted. By many trial computations, we find that the autoregressive moving average model is not only more complex but also less accurate than the autoregression model in our data sample. The robust autoregression model has not only the best inner precision, but also the most accurate prediction. The accuracy is very little changed with the increase of the distance between the measurement points and the predicted points.