Classification interpretation method and verification of circulation ensemble forecasts in GRAPES-GEPS

Ensemble prediction is playing a vital role in the development of numerical weather prediction system. Hence, how to effectively extract the information of ensemble swatches to improve ensemble forecasting skills has always been an important issue. Based on the 500 hPa geopotential height data from the China global ensemble forecasting operational system (GRAPES-GEPS), the ensemble forecasts have been classified by a cluster analysis approach, and the cluster results are further verified. By introducing the dynamic "elbow" cluster scheme, a classification interpretation method for circulation ensemble forecast is developed, and the related deterministic forecast verification is conducted using the GRAPES-GEPS real-time ensemble forecast dataset. A case study using this method indicates that the clustering of ensemble forecasts of 500 hPa circulation field over central and eastern China can efficiently classify the circulation types and meanwhile provide corresponding probabilities of their occurrence. The deterministic forecast verification results show that with the forecast lead time increase, the primary clusters of ensemble forecasts have significantly improved prediction skill scores together with the highest probability compared to those of the ensemble mean. Generally speaking, the classification interpretation method for circulation ensemble forecasting can provide a quite helpful reference for weather forecast, and it is of great value for potential application to operational weather forecast.