Forecasting the Spatial Variability of the Indian Monsoon Rainfall Using Canonical Correlation

Abstract

The application of a canonical correlation model to the long‐range forecast of the spatial variability of the Indian monsoon (June–September) rainfall has been demonstrated. The predictands used in the model are the summer monsoon rainfall of 29 contiguous meteorological subdivisions of India and the predictors are the 500 hPa ridge axis position over India for April, the Darwin surface pressure tendency (April– January), the sea‐surface temperature of the central and eastern equatorial Pacific for the five successive months preceding the monsoon (January to May) and the rainfall of the southernmost subdivision of India (Kerala) for April. The model is developed on 30 years (1939–1968) of data and tested on 16 independent years thereafter. The model demonstrates positive skill for the large contiguous meteorological subdivisions of India using the first canonical mode (found significant). The root‐mean‐square error and the absolute error between the observed and the predicted rainfall for different meteorological subdivisions are of the order of 1 cm. The high skill score (≥0ċ3) is found particularly for the meteorological subdivisions lying in west‐central India. The performance of the model appears to be better than that of the multiple regression model developed earlier by Prasad and Singh. The combined model (containing the first and the second canonical modes) appears to perform even better than that of the single model. These results, therefore, seem to be important in view of the long‐range forecast of the spatial variability of the Indian monsoon rainfall.

Prediction of Indian summer monsoon rainfall (ISMR) using canonical correlation analysis of global circulation model products

“…Prediction skill over the homogeneous regions such as the west central, hilly region as well as the country as a whole improved noticeably with the Composite forecasts as compared to the MME. Prasad and Singh (1996) have used the CCA method to estimate the monsoon rainfall using some global observed variables, such as the 500 hPa ridge axis position in April and the Darwin surface pressure tendency, and had obtained significant positive skill for the large contiguous meteorological subdivisions of India with high skill score (≥0.3), particularly for the meteorological subdivisions lying in west-central India. The present study, using GCM products, has been able to achieve better skill over most of the homogeneous regions considered.…”

“…Prasad and Singh (1996) have used this analysis to estimate the monsoon rainfall over 29 meteorological subdivisions using global variables such as the 500 hPa ridge axis position in April and the Darwin surface pressure tendency. Recently, Sinha et al (pers.…”

Performance of Multi Model Canonical Correlation Analysis (MMCCA) for prediction of Indian summer monsoon rainfall using GCMs output

“…The concept of CCA is also used over the Indian domain for the development of a statistical model (Prasad and Singh, 1996). In a recent study, the CCA is applied on individual GCM outputs for rainfall and the post-processed outputs of each GCM are then combined at Indian grid points (Singh et al, 2012b).…”

Impact of the modulated annual cycle and intraseasonal oscillation on daily-to-interannual rainfall variability across monsoonal India

“…The seasonal predictability of Indian summer monsoon rainfall is notoriously poor, operationally (Goddard et al 2003) as well as in studies of hindcast performance of statistical forecast models (Krishna Kumar et al 1995;Prasad and Singh 1996) and of general circulation models (GCMs; Gadgil and Sajani 1998;Brankovic and Palmer 2000;Joseph et al 2010). Large-scale atmospheric indices such as horizontal or vertical shear in the zonal wind have been shown to be more seasonally predictable (Webster Table 1 Definition and nature of the acronyms used throughout the paper and Yang 1992; Goswami and Ajaya Mohan 2001;Mitra et al 2005;Pattanaik and Kumar 2010), but this better predictability of the large-scale circulation does not necessarily translate to improvements in precipitation predictions over India.…”