Improving Precipitation Forecasts over the Global Tropical Belt

Summary

 This study explores the nowcasting and short-range forecasting (up to 3 days) 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 surface moisture flux coefficient, and the vertical diffusion coefficient. The optimal values were defined as those which contributed to the best one day rainfall forecasts in the present study. In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were designed using the 14-level Florida State University Global Spectral Model (FSUGSM) at a resolution of T106. Comparisons were also made of the short-range forecasts obtained from a control experiment subjected to normal mode initialization (NMI) versus experiments based on physical initialization (PI). The latter experiments were integrated using the original FSUGSM and a modified version. This modified FSUGSM was developed here by applying a reverse cumulus parameterization alorithm to the regular forecast model, which restructures the vertical humidity distribution and constrains the large-scale model’s moisture error growth during the model integration. An improved short-range rainfall prediction skill was achieved from the modified FSUGSM in this study. The results showed a better agreement between model-based and observed rainfall intensity and pattern.