Forecasting of Indian Monsoon Pattern with Empirical and Dynamic Models

Abstract

A strong monsoon usually produces abundant crops, although too much rainfall may produce devastating floods. Unlike irrigated agriculture, rainfed farming is usually diverse and risk prone. The India Meteorological Department (IMD) has long been issuing seasonal forecasts of rainfall using statistical prediction schemes that took firm root with the discovery of significant correlation between the seasonal rainfall and various regional and global climate phenomena. The prospect for monsoon forecast skill based on dynamical models in which ocean conditions are perfectly known, and are specified. Some of the monsoon predictors used is linked to the El Niño-Southern oscillation (ENSO), along with atmospheric and coupled models for additional input for better results. CFS (Climate forecast system) model simulated a weaker monsoon circulation due to a cold bias at the surface over the Asian continent. In India, the dynamical seasonal prediction (DSP) method, introduced a few years ago, integrates the GCM (General Circulation Model) with an ensemble of initial conditions for each season in order to provide probabilistic seasonal forecasts. Atmospheric and Coupled General Circulation Models (AGCM and CGCM) are the main tools for dynamical seasonal prediction. Dynamical Higher resolution using AGCMs with perfectly known Ocean conditions specified, as well as coupled models are reported to have insignificant forecast skill over ASM. The simulation of the link between EQUINOO and the Indian monsoon rainfall could be improved by investigations of the processes suggested to be important, such as the modulation of the interplay between the local Hadley circulation in the Indian longitudes and the Walker circulation associated with the El Niño events. In addition, GOALS (Global coupled ocean atmosphere-land model) or statistical based models, such as: ANN, GA-ANN models etc. may be used to predict the Indian Monsoon correctly.