Taking CMPA (CMA Multi-source Merged Precipitation Analysis System) analysis data as a reference, the research analyzes the forecast performance of ECMWF, CMA-Meso, and SCMOC (National Meteorological Center grid precipitation forecast guidance product) in 74 rainstorm cases in 2020 and 2021 in Qinling Mountains and their surrounding areas by using the dichotomy classical verification score comprehensive diagram and the object-oriented MODE spatial verification method, based on the circulation classification in rainstorm weather. The research conclusions are as follows: (1) based on the high- and low-altitude circulation situation and focused on the direct impact system, rainstorms in the Qinling Mountains and their surrounding areas can be divided into five patterns. (2) Point-to-point verification shows that SCMOC has obvious advantages in rainstorm forecast, but the disadvantage is that the Bias is relatively high. CMA-Meso has advantages in RST (weak weather system) decentralized rainstorm forecast. (3) MODE verification shows that the number of ECMWF and SCMOC independent objects is significantly lower than that of observation, the forecast area of regional rainstorm objects of SCMOC is significantly larger, the SCMOC scattered rainstorm objects are missed, and the number of independent precipitation objects of CMA-Meso is higher than that of the other two precipitation products. (4) The forecast object area and intensity of SCMOC and observation match best in the XFC (westerly trough) circulation situation, while ECMWF has the best results for the forecast of FGXFC (subtropical high westerly trough) rainstorms.
It is very important to analyze the performance of the model rainstorm forecast for understanding the intensity and position deviation of the model precipitation and improving the forecast ability. This paper uses classical scoring and the MODE (Method for Object-Based Diagnostic Evaluation) method to evaluate the forecast performance of different products. The forecast and observation data used in the article mainly include CMA (China Meteorological Administration) multi-source merged precipitation analysis, the precipitation forecast of ECMWF (European Centre for Medium-Range Weather Forecasts), CMA-Meso (Mesoscale model forecast of CMA) and SCMOC (National Meteorological Center grid precipitation forecast guidance product) data. At the same time, the possible correction method of heavy rainfall area is explored by using the high and low-level circulation configuration of the ECMWF model. The main conclusions are as follows: ① MODE spatial verification shows that the number, intensity, area and location of ECMWF rainstorm precipitation objects match the observed precipitation best, which is obviously better than SCMOC and CMA-Meso precipitation forecasts. There are significantly fewer SCMOC rainstorm precipitation objects, and the area of each single precipitation object is significantly larger, which often fails to report the small area objects of convective precipitation. ② The reason for the high TS score of SCMOC is that it reduces the number of small area rainstorm objects and avoids the “double punishment” phenomenon caused by position forecast error, which leads to the failure of SCMOC in local rainstorm forecasts. ③ Analyzing the relationship between the circulation situation of the ECMWF model and the location of rainstorm forecasts by the model, it is found that the location of the rainstorm area is consistent with the upper circulation system, especially with the strong rising area of the vertical velocity of 700 hPa and the high value area of the specific humidity of 850 hPa. When the rainstorm area coincides with the upper air system but is not consistent with the ground convergence area and the high value area of velocity potential, the rainstorm location often has a large deviation. The location of the surface convergence area can be used as a reference to improve the performance of the rainstorm forecast.
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