Clustering Technique Suitable for Eulerian Framework to Generate Multiple Scenarios from Ensemble Forecasts

Abstract

Abstract In operational weather forecasting, it is effective to aggregate information on all members of an ensemble forecast through cluster analysis. The temporal coherence of ensemble members in each cluster is an important piece of information about the robustness of the forecast scenario given by clusters. This information is especially important for forecasts for which the target area is a city or prefecture, that is, an Eulerian framework, because the members that compose each cluster can change over time because of the small size of the target area. This study provided the temporal coherence of members in clusters by performing principal component analysis and cluster analysis on 3-hourly 500-hPa geopotential height forecasts and linking the clustering results in the time direction. The new method provided a consistently well-divided forecast scenario throughout the forecast period for Eulerian frame forecasts, as well as information on the temporal coherency of the members in the clusters, which was demonstrated to be effective through the experiment to preselect a cluster with small errors. The application of the new technique to improve precipitation forecasts was also discussed. Significance Statement Numerical weather forecasts always contain errors. Although the uncertainty of such forecasts cannot be obtained from the forecast itself, ensemble forecasts, which are aggregates of many forecasts, can be used to estimate the uncertainty of the forecast. In this study, a new method was developed to transfer the information contained in many ensemble forecasts into four forecasts by cluster analysis and to provide forecast information suitable for a small forecasting area such as a prefecture. The use of this method for improving precipitation forecasts was also examined.