Forecasting of scatterometer‐derived wind fields in the north Indian Ocean with a data‐adaptive approach

Abstract

A combination of genetic algorithm (GA) and empirical orthogonal function (EOF) analysis has been employed to forecast satellite scatterometer–observed surface winds in the north Indian Ocean. The EOFs are used to compress the major part of the spatial variability into a few eigenmodes. The temporal variability is contained in the corresponding principal components (PC) which have been subjected to singular spectrum analysis for filtering out the random part. Forecast of the deterministic part has been carried out using GA with lead times varying from 1 to 4 days. The entire analysis has been done separately for the zonal and meridional winds. Finally, predicted wind fields have been generated as linear combinations of the spatial eigenmodes weighted by the predicted PCs. Forecast quality has been evaluated by comparing with an independent validation data set as well as with buoy data. The performance of the algorithm has been found to be quite encouraging.

Modelling Agro-Met Station Observations Using Genetic Algorithm

“…Thus it is ideal for those scientists, for example, scientists involved in crop forecast modeling, who only require forecasts of certain Agro-Met parameters at isolated locations, representative of a small area. The shortcoming is of course that it is not easy to generalize this algorithm for predicting weather parameters over the entire globe, or over large threedimensional regions, although there are promising twodimensional generalizations [5,6,[8][9][10]15]. The method is highly suitable for location-specific forecasts as demonstrated in the present study.…”

“…(GA). The algorithm has been discussed in detail by several authors [1,2,9,15]. However, for the benefit of the present readers, we describe below the essential details of the algorithm, borrowing heavily from the reference [15].…”

“…Also, the algorithm can work with only a small number of data points, typically of the order of a few hundred. Predictive skill of GA has been amply demonstrated in the cases of sea surface temperature (SST) in the Alboran Sea [5] and Arabian Sea [6], summer monsoon rainfall over India [7], SST and sea level anomaly in the Ligurian Sea [8], wave heights in the north Indian Ocean [3] and more recently, for basin-scale predictions of ocean surface wind in the north Indian Ocean [9] and wave heights in the Bay of Bengal [10]. Even tidal currents from a few tidal levels have been predicted using GA [11].…”

Variability of Significant Wave Height over the Indian Ocean Using Empirical Orthogonal Function Analysis

“…The EOF analysis is earlier used by Jones et al (1995) and Rao et al (2002) over the Indian Ocean to understand the variability of surface fluxes and wind stress curl. Recent studies of Sharma et al (2007a, b) and Basu et al (2009) have demonstrated wind forecast techniques using EOF analysis in conjunction with genetic algorithm on satellite scatterometer derived winds for the AS, BOB and north Indian Ocean regions separately. However, to the best of the knowledge and belief of the present authors, no study has been reported in the literature so far on the variability of the SWH using EOF in the Indian Ocean.…”

Statistical downscaling: A powerful tool for estimating global change impacts on regional scale

“…Basu et al have taken use of a combination of genetic algorithmand EOF analysis to forecast satellite scatterometer observed surface winds in the north Indian Ocean[27].…”