Method of Object-Based Diagnostic Evaluation for Numerical Heavy-Precipitation Prediction Based on Subjective and Objective Circulation Classification: Application and Testing over Northeast China during the Warm Season of 2019

Based on subjective and objective circulation classification and the MODE (Method of Object-based Diagnostic Evaluation), a evaluation method framework is developed for numerical heavy-rainfall prediction. This framework is used to verify the heavy-rainfall forecast by the global forecast model of the European Center for Medium-Range Weather Forecasts (ECMWF) and the regional mesoscale forecast model of the China Meteorological Administration (CMA_MESO) in Northeast China during the warm season of 2019. The results show that 54 heavy rainfall days in Northeast China during this period can be classified into a trough pattern (P1), western Pacific subtropical high pattern (P2), jet pattern (P3), western Northeast China Cold Vortex (NCCV) pattern (P4), and eastern NCCV pattern (P5). Among these five synoptic patterns, P1 and P3 are dominated by regional heavy rainfall, and the numerical model has high predictability for the occurrence of heavy rainfall with high threshold scores (TS). The heavy rainfall of P4 and P5 is localized, and the numerical model has poor predictability with low TS. P2 is also dominated by regional heavy rainfall. However, the forecast deviation for the location, intensity, and area of heavy rainfall is relatively large, and the TS is low. In addition, from the comparison of CMA_MESO and ECMWF results, CMA_MESO’s rainfall predictions are generally stronger in intensity and larger in area than the actual rainfall of heavy rains. For heavy rainfall, CMA_MESO results show a generally higher TS and false alarm rate than ECMWF results. CMA_MESO has a less consistent forecast deviation and generally lower predictability.