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

摘要/Abstract

摘要：

利用国家气象站降水观测资料，基于MET检验系统对2022年主汛期甘肃暴雨过程中欧洲中期天气预报中心全球模式（ECMWF）、中国气象局全球同化预报系统（CMA-GFS）、中国气象局中尺度天气数值预报系统（CMA-MESO）和中国气象局上海数值预报模式系统（CMA-SH9）的降水预报性能进行评估，结果表明：（1）四种数值模式的晴雨准确率相差不大，均在0.80以上。对≥50 mm降水预报的TS评分，CMA-SH9评分最高，CMA-MESO次之。（2）根据影响系统及环流形势特征将甘肃主汛期暴雨分为副高边缘型、西北气流型和低槽型三种类型。对≥50 mm降水预报，四种模式中CMA-SH9模式预报效果最好，尤其是对于低槽型暴雨过程，其次是副高边缘型。（3）基于MODE空间检验发现，对≥50 mm降水预报，CMA-MESO、CMA-SH9两个区域模式预报能力优于ECMWF、CMA-GFS两个全球模式；7月14日暴雨过程中CMA-SH9模式预报暴雨的质心距离和轴角偏差相对偏小，对暴雨的位置与空间走向预报最接近实况。

关键词: 数值模式, 性能评估, 暴雨, 空间检验, 甘肃

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

彭筱, 孔祥伟, 陈晓燕, 吴晶, 伏晶, 李文学. 甘肃省数值模式暴雨预报效果比较与性能评估[J]. 干旱区研究, 2025, 42(9): 1563-1573.

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.

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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