Breeding Method for Ensemble Weather Prediction of Northeast Monsoon under the Influence of Global Warming

Numerical weather prediction models as well as the atmosphere itself can be viewed as nonlinear dynamical systems in which the evolution depends sensitively on the initial conditions. Any small error in the initial condition will lead to forecast errors that grow with increasing forecast time. The methods of ensemble forecasting are developed to generate a representative sample of the possible future states of a dynamical system. For an efficient ensemble forecasting, the initial perturbations to the control analysis should adequately sample the possible analysis errors. The analysis cycle is similar to a breeding cycle, which acts as a nonlinear perturbation model upon the evolution of the real atmosphere. This paper proposes a breeding method for generating ensemble perturbations that can effectively represent the uncertainties in the observed meteorological data. In order to simulate the possible states of northeast monsoon over Southeast Asia under the influence of global warming, selected data from the Intergovernmental Panel on Climate Change (IPCC) are used for testing the generation of initial perturbations in the breeding process by integration of a shallow water model. The results from this research showed the effectiveness of the breeding method in generating ensemble perturbations for short-range weather forecast.