Improving precipitation forecasts over the Global Tropical Belt

This study explores the nowcasting and short-range forecasting (up to 3 day s) skills of rainfall over the tropics using a high resolution global model . Since the model-predicted rainfall is very sensitive to model parameters, four key model parameters were first selected. They are the Asselin filter coefficient, the fourth order horizontal diffusion coefficient, the surfac e moisture flux coefficient, and the vertical diffusion coefficient. The op timal values were defined as those which contributed to the best one day ra infall forecasts in the present study. In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were design ed using the 14-level Florida State University Global Spectral Model (FSUGS M) at a resolution of T106. Comparisons were also made of the short-range f orecasts obtained from a control experiment subjected to normal mode initia lization (NMI) versus experiments based on physical initialization (PT). Th e latter experiments were integrated using the original FSUGSM and a modifi ed version. This modified FSUGSM was developed here by applying a reverse c umulus parameterization alorithm to the regular forecast model, which restr uctures the vertical humidity distribution and constrains the large-scale m odel's moisture error growth during the model integration. An improved shor t-range rainfall prediction skill was achieved from the modified FSUGSM in this study. The results showed a better agreement between model-based and o bserved rainfall intensity and pattern.