青藏高原东北侧暴雨数值模式预报空间误差特征

doi:10.13866/j.azr.2022.01.07

摘要/Abstract

摘要：

利用2019—2020年5—9月ECMWF（European Center for Medium-Range Weather Forecast）、GRAPES-GFS（中国气象局GRAPES全球数值预报业务系统,Global/Regional Assimilation and Prediction System-Global Forecast System）大尺度数值模式36 h预报时效的24 h降水量预报和加密降水观测资料,基于CRA空间检验技术识别、分离青藏高原东北侧暴雨目标（ECMWF模式18个,GRAPES-GFS模式11个）,定量分析了两种模式对暴雨预报的空间误差（落区、强度和形态误差）特征,总结大尺度数值模式在青藏高原东北侧暴雨预报中的适用性。结果表明：（1）两种模式的降水预报形态误差占比均最大。ECMWF预报的强度误差占比最小,落区误差次之,而GRAPES-GFS的落区误差占比最小,强度误差次之。（2）两种模式预报的暴雨落区位置均较实况偏西偏北。暴雨大值中心ECMWF偏西偏南,而GRAPES-GFS仅偏西。（3）对暴雨雨区面积的预报两种模式均明显偏小,容易造成暴雨漏报,对最大降水量和平均雨强的预报GRAPES-GFS模式均偏弱超过40%,ECMWF预报的平均雨强偏弱11.49%,最大降水量偏强1.47%。（4）两种模式对甘肃陇东南地区和陕西西南部的暴雨预报效果较好,而对陕北及宁夏等偏北地区的预报效果较差。

关键词: CRA, 空间误差, 暴雨, 青藏高原东北侧

Abstract:

Rainstorm targets were identified from May to September in 2019-2020 over the northeastern side of Tibetan Plateau by using the Contiguous Rain Area technique. There were 18 rainstorm targets in European Center for Medium-Range Weather Forecast (ECMWF) and 11 in Global/Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS) mode. The spatial error characteristics—including displacement, intensity, and pattern error—of the two models for rainstorm forecasts were analyzed quantitatively, and the applicability of the two models in rainstorm forecasts over the northeastern side of Tibetan Plateau is here summarized. The results of the study showed that: (1) The pattern errors of rainstorm forecasts in ECMWF and GRAPES-GFS accounted for the largest proportion of the total errors. ECMWF had the smallest intensity error ratio for rainstorm forecast, followed by displacement errors, while the GRAPES-GFS model had the smallest displacement errors, followed by intensity errors. (2) The rainstorm areas in the two models shifted more northwestward compared with observations. The forecast of ECMWF for the rainstorm center was southwestward, while that of GRAPES-GFS was only westward. (3) The forecast of rainstorm area produced by the two models was obviously too limited, meaning that it could easily fail to predict rainstorm events. The GRAPE-GFS model underestimated both maximum precipitation and rainfall intensity by more than 40%, while ECMWF under-forecasted about 11.49% for the average rainfall intensity and overestimated about 1.47% for maximum precipitation. (4) The two models showed a better forecasting ability for rainstorm in the southeastern Gansu and southwestern Shaanxi areas, and a poorer one in the northern Shaanxi, Ningxia, and other northern areas.

Key words: Contiguous Rain Area, spatial error, rainstorm, the northeastern side of Tibetan Plateau

张君霞,孔祥伟,刘新伟,王勇. 青藏高原东北侧暴雨数值模式预报空间误差特征[J]. 干旱区研究, 2022, 39(1): 64-74.

ZHANG Junxia,KONG Xiangwei,LIU Xinwei,WANG Yong. Spatial error characteristics of rainstorm forecasts of large-scale numerical model over the northeastern side of Tibetan Plateau[J]. Arid Zone Research, 2022, 39(1): 64-74.

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暴雨天气过程（年-月-日）	暴雨区面积/km²	是否识别有效CRA ECMWF GRAPES-GFS		暴雨天气过程（年-月-日）	暴雨区面积/km²	是否识别有效CRAECMWF GRAPES-GFS
2019-05-05	1709	×	×	2020-06-16	61783	√	√
2019-05-06	1832	×	×	2020-06-25	20154	√	×
2019-06-04	13312	√	√	2020-07-10	11445	√	√
2019-06-05	9688	×	×	2020-07-11	2786	×	×
2019-06-20	15609	√	√	2020-07-23	16981	×	×
2019-06-27	33730	×	×	2020-07-24	11289	√	×
2019-07-21	21002	√	×	2020-08-03	3623	×	×
2019-07-28	6618	×	×	2020-08-04	39610	√	×
2019-08-02	25670	√	√	2020-08-05	2159	×	×
2019-08-03	26854	√	√	2020-08-06	12724	√	×
2019-08-08	4682	×	×	2020-08-07	2598	×	×
2019-08-19	1235	×	×	2020-08-10	6267	×	×
2019-08-26	11970	√	×	2020-08-12	14230	√	√
2019-09-09	38960	√	√	2020-08-14	2200	×	×
2019-09-12	19184	√	×	2020-08-15	44730	√	√
2019-09-13	10496	×	×	2020-08-16	19860	√	√
2019-09-14	55048	√	√	2020-08-17	4709	×	×
2020-05-07	5017	×	×	2020-08-23	7802	×	×
2020-06-15	3405	×	×

	平均面积误差/%	平均雨强误差/%	最大降水量误差/%	总降水量误差/%	落区误差占比/%	强度误差占比/%	形态误差占比/%	平均纬向位移误差/(°)	平均经向位移误差/(°)	平均纬向质心误差/(°)	平均经向质心误差/(°)
ECMWF	-16.20	-11.49	1.47	3.51	26.93	20.73	52.36	-0.34	0.08	-0.36	-0.11
GRAPES-GFS	-68.33	-43.40	-49.33	-79.72	16.33	31.23	52.58	-0.15	0.10	-0.22	0.03