关键词: 强降水, SAL, 数值预报, 暴雨分型

Abstract:

Based on the actual precipitation of three source fusion grid, the observation data from intensive automatic station, the basic radar reflectivity factors, high-resolution numerical prediction products, and FNL reanalysis data, the synoptic system classification tests on twelve regional rainstorm processes during flood season of 2020 in Liaoning province were carried out, and it was shown that the predictability of the cyclone rainstorm was low.Then the typical cyclonic rainstorm during July 12-14 was selected for further analysis.Using the object-oriented spatial test method, SAL (Structure, Amplitude, Location), combined with the traditional test method, the causes of prediction errors in different models were quantitatively analyzed from three aspects including structure, strength, and position.The results show that the rainstorm area is concentrated and presents double rain belt distribution.Rain intensity in the local area is significant and the reasons for precipitation are different in the areas of east and west of Liaoning province.The TS scores of CMA regional model are higher than those of the global model.SAL space test shows that CMA regional model well represented the internal structure of the rain belt, whereas the structural errors in the global model mainly due to the forecasting weakness of extreme precipitation.The strength tests show that the predicted rainfall intensities appear close to the actual situation in CMA-MESO3km, followed by EC_THIN, and insufficient in CMA_GFS.In general, the rainstorm area in each model is credible with the best result in CMA-MESO3km.The prediction errors in the rainstorm area mainly result from the significant discrepancies between the model prediction focus and the actual situation.

Key words: Heavy precipitation, SAL (Structure, Amplitude, Location), Numerical prediction, Rainstorm classification