The Use of Ensemble Clustering on a Multimodel Ensemble for Medium-Range Forecasting at the Weather Prediction Center

Abstract

Abstract This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts. Significance Statement Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.