Comparative Analysis of SCMOC and Models Rainstorm Forecasting Performance in Qinling Mountains and Their Surrounding Areas

Liujie, Pan; Zhang, Hongfang; Liu, Jing; Gao, Xingxing; Qi, Chunjuan

doi:10.3390/atmos13050705

Cited by 2 publications

(1 citation statement)

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“…The MODE method was developed by Davis et al [23][24][25]. The method convolved the original precipitation field by a given radius R, and it calculates the precipitation intensity, axis angle and area of the object based on analyzing the conditions.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic Analysis of Multimodel Rainstorm Forecast for Cases Based on MODE Method

Zhang

Liujie

2022

Atmosphere

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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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Section: Methodsmentioning

confidence: 99%