New statistical models for long-range forecasting of southwest monsoon rainfall over India

Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes

“…India's vital dependence on its summer monsoon rainfall for agriculture and other water resources makes improvements in skilful forecasting of rainfall amount a continual challenge to climate scientists, with a long history of sustained efforts to understand monsoon predictability [e.g., Charney and Shukla, 1981;Webster et al, 1998] and to develop statistical forecasting methods (see Shukla and Mooley [1987], Rajeevan et al [2007], and Krishna Kumar et al [1995] for reviews). The variability and predictability hinges on two widely regarded influences: local land warming and the distant El Niño-Southern Oscillation (ENSO) [e.g., Charney and Shukla, 1981;Shukla and Mooley, 1987].…”

“…Most statistical methods for forecasting monsoon rainfall, used by India Meteorological Department, use predictors that are in one way or another related to these influences [Krishna Kumar et al, 1995]. Subsequent studies have identified predictors from other parts of land and global oceans with improved skills but in some cases with limited understanding of the physical connections [e.g., Guhathakurta et al, 1999;Rajeevan et al, 2000Rajeevan et al, , 2007Delsole and Shukla, 2002;Sahai et al, 2003;Pai and Rajeevan, 2006]. Linear regression is the standard method used, but nonlinear and multimodel combination methods have been proposed [Rajeevan et al, 2007], and regardless, the skills are limited by the strength of the predictors to monsoon rainfall.…”

“…Subsequent studies have identified predictors from other parts of land and global oceans with improved skills but in some cases with limited understanding of the physical connections [e.g., Guhathakurta et al, 1999;Rajeevan et al, 2000Rajeevan et al, , 2007Delsole and Shukla, 2002;Sahai et al, 2003;Pai and Rajeevan, 2006]. Linear regression is the standard method used, but nonlinear and multimodel combination methods have been proposed [Rajeevan et al, 2007], and regardless, the skills are limited by the strength of the predictors to monsoon rainfall. Although a combination of such predictors once engendered optimism [e.g., Shukla and Mooley, 1987], marked variations in correlations with ENSO indices in recent years cast doubt on such predictors [e.g., Krishna Kumar et al, 1999Kumar et al, , 2006.…”

Data Mining for Evolving Fuzzy Association Rules for Predicting Monsoon Rainfall of India

“…The predictor variables used for the study of ISMR include Darwin sealevel pressure, Latitudinal position of 500 mb ridge along 75° E, Arabian sea SST, Indian Ocean SST, Quasi-Biennial Oscillation, Sea-surface temperature anomalies over different Nino regions, Western Pacific region SST, Eastern Indian Ocean region SST, Eurasian surface temperature and Indian Surface temperature, Equatorial East Indian Ocean sea surface temperature, Nino 3.4, Equatorial Indian Ocean Oscillation zonal wind index (EQWIN), Eurasian snow cover and NW Europe temperature. The complex relation between these large scale circulation patterns and ISMR leads to a poor performance of the models used so far to forecast ISMR (Gadgil et al, 2004;Rajeevan et al, 2004).…”

An empirical model for the seasonal prediction of southwest monsoon rainfall over Kerala, a meteorological subdivision of India

“…One way to tackle this is to do different ensemble runs of numerical models. However till today, these models do not have the capability to accurately reproduce the salient features of monsoon and its variability (Rajeevan et al, 2004, Gadgil et al, 2005. So, the forecast for these smaller regions, whenever necessary, needs to be based on statistical methods.…”