In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days.This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble.This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 107 weights.Use of these weights provides the possibility for real-time medium range forecasts with the superensemble.We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon.The member models of our suite include ECMWF,NCEP/EMC, JMA,NOGAPS (US Navy),BMRC,RPN (Canada)and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds,temperature,500 hPa geopotential height,sea level pressure and precipitation.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.This real-time product is being provided to many interested research groups.The FSU multimodel superensemble,in real- time,stands out for providing the least errors among all of the operational large scale models.
Volume 129, 2020
Continuous Article Publishing mode
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