Dynamical-Empirical forecast for the Indian monsoon rainfall using the NCEP Coupled Modelling System – Application for real time monsoon forecast - PATTANAIK, D.R.; TYAGI, AJIT; KUMAR, ARUN (MAUSAM, 2012)
The performance of the National Centre for Environmental Prediction’s (NCEP) operational coupled modeling system known as the Climate Forecast System (CFS) is evaluated for the prediction of all India summer monsoon rainfall (AISMR) during June to September (JJAS). The evaluation is based on the hindcast initialized during March, April and May with 15 ensemble members each for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of observed climatology with both the rainfall maxima (over the west-coast of India and over the head Bay of Bengal region) well captured, with a signification correlation coefficient between the forecast and observed climatology over the Indian monsoon region (bounded by 50°E-110°E and 10°S-35°N) covering Indian land mass and adjoining oceanic region. Although the CFS forecast rainfall is overestimated over the Indian monsoon region, the land only rainfall amount is underestimated compared to observation. The skill of the prediction of monsoon rainfall over the Indian land mass is found to be relatively weak, although it is significant at 95% with a correlation coefficient (CC) of 0.44 with April ensembles. By using CFS predicted JJAS rainfall over the regions of significant CCs, a hybrid dynamical-empirical model is developed for the real time prediction of AISMR, whose skill is found to be much higher (CC significant above 99% level) than the raw CFS forecasts. The dynamical-empirical hybrid forecast applied on real time for 2009 and 2010 monsoons are found to be much closer to the observed AISMR. Thus, when the hybrid model is used there is a correction not only to the sign of the actual forecast as in the case of 2009 monsoon but also to its magnitude and hence can be used as a better tool for the real time prediction of AISMR.