Performance of FSU multimodels rainfall forecast over the tropics during June-Sep 2007

Over the years tropical rainfall forecast skills of the large scale operational numerical weather prediction models for the medium range forecast is significantly improved. We have used these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble. In this study we examined the rainfall predictability over the tropics by using Florida State University multimodels. This procedure utilizes 90 of the recent-past forecasts from these models to arrive at the training phase statistics.Use of these weights provides the possibility for real-time medium range forecasts with the superensemble.The member models of our suite include ECMWF,NCEP/EMC, JMA,NOGAPS (US Navy),BMRC and RPN (Canada). The FSU SE forecasts were consistent with spatio temporal pattern of the Inter Tropical Convergence Zone (ITCZ), which is the main rain bearing synoptic machanism over the tropics. We show in this paper the probabilistic and deterministic skill scores for day 1 through day 5 of rainfall forecasts.In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean.The skill matrices we use include the RMS errors,the anomaly correlations and equitable threat scores.For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial.The FSU multimodel superensemble, in real- time, stands out for providing the least errors among all of the operational large scale models.