Canonical correlation analysis (CAA) model for long-range forecasts of sub-divisional monsoon rainfall over India

Abstract

Using the canonical correlation analysis (CCA) approach, a forecast model for long range forecasts of monsoon (June-September) rainfall of 27 meteorological sub-divisions over India was developed, A set of 12 parameters, which have significant correlation with Indian monsoon rainfall, was used as predictors, The model was developed with the data of the period 1958-1994 and by retaining three significant canonical modes, The model showed useful predictive skill in of respect of meteorological sub-divisions over central parts of India and NW India with low errors and high skill scores for categorical forecasts, The model showed no predictive skill in respect of meteorological sub-division over south peninsula, Orissa, West Bengal and Bihar. The CCA model has been also found to perform better than another statistical model developed using the 12 same predictors, The CCA model also showed moderate skill in forecasting excess and deficient rainfall categories of sub-divisional monsoon rainfall during the extreme years.