甘肃省数值模式暴雨预报效果比较与性能评估

doi:10.13866/j.azr.2025.09.02

Abstract

Abstract:

Using the precipitation observation data of the National Meteorological Station and based on the model evaluation tools test system, an evaluation was conducted on the precipitation prediction performance of the European Centre for Medium-Range Weather Forecasts (ECMWF), Global Assimilation Prediction System of China Meteorological Administration (CMA) Global Forecast System, Mesoscale Weather Numerical Prediction System of CMA CMA-SH9 during the 2022 Gansu main flood season rainstorm. The Mesoscale (CMA-MESO), and Shanghai Numerical Prediction Model of results demonstrate that: (1) The four numerical models show similar accuracy in predicting sunny and rainy weather, all above 0.80, with CMA-SH9 achieving the highest score for precipitation forecasts ≥50 mm, followed by CMA-MESO. (2) According to the characteristics of the impact system and circulation situation, the rainstorm in the main flood season in Gansu Province can be divided into three types: subtropical high marginal, northwest airflow, and low trough. The CMA-SH9 model has the best prediction effect for precipitation ≥50 mm, particularly for low-trough rainstorm processes, followed by the subtropical high marginal type. (3) Method for Object-based Diagonostic Evaluation spatial test indicates that the CMA-MESO and CMA-SH9 regional models outperform the ECMWF and CMA-GFS global models in forecasting ≥50 mm precipitation. In the rainstorm process on July 14, the CMA-SH9 model predicted that the centroid distance and axial angle deviation of the rainstorm were relatively small, and the prediction of rainstorm location and spatial trend was most close to the actual situation.

Key words: numerical model, performance evaluation, rainstorm, spatial verification, Gansu

PENG Xiao, KONG Xiangwei, CHEN Xiaoyan, WU Jing, FU Jing, LI Wenxue. Comparison and performance evaluation of numerical model rainstorm prediction in Gansu Province[J].Arid Zone Research, 2025, 42(9): 1563-1573.

Figures/Tables 11

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预报	实况
预报	有降水	无降水
有降水	A	B
无降水	C	D

模式	质心距离/km	轴角偏差/（°）	面积比（预报/观测）	50%分位强度比率（预报/观测）	90%分位强度比率（预报/观测）
ECMWF	15.71	33.62	0.80	0.83	0.62
CMA-GFS	9.10	23.56	1.45	0.92	0.83
CMA-MESO	26.37	30.25	1.16	0.98	1.02
CMA-SH9	19.56	19.74	2.55	0.99	1.02

模式	质心距离/km	轴角偏差/（°）	面积比（预报/观测）	50%分位强度比率（预报/观测）	90%分位强度比率（预报/观测）
ECMWF	-	-	-	-	-
CMA-GFS	-	-	-	-	-
CMA-MESO	30.75	41.99	90.69	1.20	1.82
CMA-SH9	14.99	8.55	6.67	1.06	1.14

模式	质心距离/km	轴角偏差/（°）	面积比（预报/观测）	50%分位强度比率（预报/观测）	90%分位强度比率（预报/观测）
ECMWF	16.04	7.39	1.79	0.80	0.65
CMA-GFS	-	-	-	-	-
CMA-MESO	3.18	1.38	1.29	0.97	0.76
CMA-SH9	5.63	7.51	2.24	1.09	0.84

模式	质心距离/km	轴角偏差/（°）	面积比（预报/观测）	50%分位强度比率（预报/观测）	90%分位强度比率（预报/观测）
ECMWF	22.47	22.67	0.50	0.87	0.57
CMA-SH9	4.91	5.21	2.10	1.20	1.13

Comparison and performance evaluation of numerical model rainstorm prediction in Gansu Province

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