Using of a University Consortium Ensemble to Assess Forecast Risk and Confidence

Ensemble forecasting is currently one of the more effective methods of estimating forecast variability. Local and Regional ensemble forecasting allows the ability to collect a large number of possible forecast outcomes and can articulate forecast uncertainty but at a high computational cost.

SDSMT has developed a Confidence Index (CI); an algorithm applied to a single forecast to determine risk of forecast error. When applied to a region CI detects the presence of weather features that can lead to forecast error and creates a “score” that can be associated with forecast risk. From this an updated version of CI has been developed to include ensemble spread as one of the examined features. This was done by utilizing ensemble output form the Global Ensemble Forecast System.

Using this version of CI, the next step is to integrate it into the Big Weather Web project (BWW). The BWW is a multi-university project with the goal of creating a multi-university ensemble along with other useful tools such as cloud storage and web services. The main goal of the ensemble system is to distribute the computing costs of this type of model and create efficient ways for the output to be accessed. The goal is to use this consortium-based ensemble for CI and compare the results to that of the GEFS as well as present the challenges that can develop from this type of system.